Towards the Development of a Common Digital Repository for ... 2.1 OSR Educational Design.pdf · as the “free-choice learning” sector…Any public education reform effort that

D-2.1: OSR Educational Design OpenScienceResources

1

Towards the Development of a Common Digital Repository for Formal and

Informal Science Education

Deliverable D-2.1: The OSR Educational Design

Version 2.0 May 2010

Authors: Sofoklis Sotiriou, Pavlos Koulouris, Fotis Kouris (Ellinogermaniki Agogi) Nikos Zygouritsas, Spiros Borotis, Nikitas Kastis (Lambrakis Research Foundation) Demetrios Sampson, Panagiotis Zervas, Alexandros Kalamatianos (CERTH)


2

Table of Contents

1 INTRODUCTION.........................................................................................................................4

2 METHODOLOGY .......................................................................................................................5

3 PEDAGOGICAL PRINCIPLES .................................................................................................6 3.1 BRIDGING THE GAP BETWEEN FORMAL AND INFORMAL LEARNING: THE NEED FOR EDUCATIONAL REFORM ........................................................................................................................8 3.2 CONTEXTUALIZED MODEL OF LEARNING..............................................................................10

3.2.1 Personal context .............................................................................................................10 3.2.2 Socio-cultural context.....................................................................................................10 3.2.3 Physical context ..............................................................................................................10

3.3 INQUIRY-BASED LEARNING ..................................................................................................11 3.3.1 A Renewed Pedagogy for the Future of Europe .............................................................11 3.3.2 Distinguishing features of inquiry-based learning .........................................................12 3.3.3 Inquiry is on a continuum...............................................................................................14 3.3.4 Problem-based learning (PBL) and problem-oriented learning (POL) .........................15

3.4 RESOURCE-BASED LEARNING...............................................................................................16 3.4.1 Overview of Resource-based learning............................................................................16 3.4.2 Components of resource-based learning ........................................................................16 3.4.3 Enabling contexts ...........................................................................................................17 3.4.4 Resources........................................................................................................................18 3.4.5 Tools ...............................................................................................................................18 3.4.6 Scaffolding......................................................................................................................19 3.4.7 Opportunities and challenges with resource-based learning .........................................20

4 DIGITAL EDUCATIONAL RESOURCES AND EDUCATIONAL METADATA.............22 4.1 INTRODUCTION.....................................................................................................................22 4.2 CHARACTERIZING DIGITAL RESOURCES WITH STANDARDISED EDUCATIONAL METADATA ...23

4.2.1 Educational metadata standards ....................................................................................23 4.2.2 The IEEE Learning Objects Metadata (IEEE LOM) Standard ......................................24 4.2.3 Application Profiles ........................................................................................................25

4.2.3.1 Definition ............................................................................................................................ 25 4.2.3.2 Main reasons for developing an application profile ............................................................ 27 4.2.3.3 Methodology for developing an application profile ............................................................ 27 4.2.3.4 Review of existing IEEE LOM application profiles............................................................ 28

4.2.4 Repositories and metadata .............................................................................................38 4.2.5 Some additional issues....................................................................................................43

4.3 SOCIAL TAGGING OF DIGITAL EDUCATIONAL RESOURCES ....................................................45 4.3.1 Introduction ....................................................................................................................45 4.3.2 Advantages and disadvantages of social tagging ...........................................................46 4.3.3 Social tagging in education and science museums/centres.............................................47 4.3.4 Social tagging tools ........................................................................................................49 4.3.5 Comparison of social tagging tools ................................................................................51

5 THE OSR CONTENT ENHANCEMENT STRATEGY: OUTCOMES OF EDUCATIONAL DESIGN..................................................................................................................53

5.1 OSR IEEE LOM APPLICATION PROFILE: THE STRUCTURE OF THE OSR EDUCATIONAL METADATA .........................................................................................................................................53

5.1.1 Use and adaptations of the standard ..............................................................................54 5.1.2 Presentation of the OSR IEEE LOM Application Profile ...............................................60


3

5.2 THE OSR EDUCATIONAL PATHWAYS...................................................................................69 5.2.1 The concept of Educational Pathway .............................................................................69 5.2.2 OSR user roles and use contexts: Defining the dimensions of digital-resource-based science learning ............................................................................................................................70 5.2.3 The Educational Pathway Patterns ................................................................................72

5.2.3.1 Structure of the OSR Educational Pathway Patterns........................................................... 73 5.2.3.2 The Educational Pathway Patterns developed..................................................................... 74

5.2.4 OSR Educational Pathway Pattern for a Pre-Structured Visit by the School Community 74

5.2.4.1 Introductory note................................................................................................................. 74 5.2.4.2 The underlying pedagogical approach for the structured visit ............................................ 75 The Educational Pathway Pattern for a Pre-Structured Visit by the School Community ......................... 77

5.2.5 OSR Educational Pathway Pattern for an Open Visit by Lifelong Learners..................81 5.2.5.1 Introductory note................................................................................................................. 81 5.2.5.2 The underlying pedagogical approach for the open visit..................................................... 81 The Educational Pathway Pattern for an Open Visit by Lifelong Learners.............................................. 82

5.3 SOCIAL TAGGING OPPORTUNITIES AND OPTIONS IN OSR......................................................85

6 REFERENCES............................................................................................................................87

7 ANNEXES ...................................................................................................................................93 7.1 ANNEX I: EDUCATIONAL DESIGN REQUIREMENTS ELICITATION QUESTIONNAIRE...............93 7.2 ANNEX II: SCIENCE LEARNING CONTENT VOCABULARY FOR THE “CLASSIFICATION” ELEMENT [ELEMENT 26] OF THE OSR EDUCATIONAL METADATA STRUCTURE...............................104


4

1 Introduction

The aim of this document is to describe the Educational Design underlying the services and activities of the OpenScienceResources (OSR) project. It is the outcome of desk research and consultations within the OSR consortium that took place initially in the first six months of the OSR project and subsequently revisited in the second half of the first year, setting the ground for the consequent phases. It provides the conceptual framework and agenda for the development of the OSR Portal service, and the scenarios of its use, trial and validation in real-life settings. As it lies right at the outset of the project activities, the OSR Educational Design should be seen as a starting point in the project development rather than a final project outcome. It will be revisited throughout the following project phases, enriched with experiences gained there, and, if necessary, enhanced and revised. Work conducted towards the development of the OSR Educational Design aimed to investigate the pedagogical issues arising out of the project’s intention to apply innovative educational metadata techniques to science learning digital content available in the repositories of science museums and centres, so as to bridge school science education with informal science learning. The OSR approach builds on the strengths of the informal learning taking place in science centres and museums, to involve school students, their teachers as well as all lifelong learners in extended episodes of playful learning – in close relation and interplay with the school curriculum, if that is of interest to the user of the content. The outcome is the proposition of an innovative learning approach that crosscuts the boundaries between places of science learning provision and science learning opportunity, placing the user, his/her circumstances, preferences and learning style, at the centre of the learning experience. The methodology followed to carry out this work is outlined in the chapter following this introduction. Subsequently, the third and fourth chapters present the results of the theoretical investigation of the conceptual elements of the OSR project. In line with the state-of-the-art in the area, the question of bridging the existing gaps between formal and informal science learning are examined in the light of the Contextualised Model of Learning, Inquiry-Based Learning, and Resource-Based Learning. In parallel, opportunities and challenges arising through the use of educational metadata techniques are examined from two angles: characterisation of digital learning content with standardised educational metadata, and characterisation with annotations directly contributed by users (social tags and resulting folksonomies). The last part of the document (chapter five) presents the tangible input that the OSR Educational Design feeds into the next steps of the project, based on the general theoretical propositions and principles adopted in the first part. The tangible outcomes presented include the OSR standards-based educational metadata structure (‘OSR Application Profile’), the OSR Educational Pathways (flexible schemes for the combination of digital resources into wider meaningful learning experiences


5

appropriate for the user and context of use), as well as initial thoughts about useful directions in the investigation of the potential of social tagging in the OSR project.

2 Methodology

The OSR Educational Design is the result of a dialogic process that took place in the first six months of the project (and subsequently revisited in the second half of the first project year), between existing knowledge in the fields covered (formal and informal science learning, educational metadata and social tagging), and the concepts and objectives of the OSR project. The aim of this dialogue was to identify the state-of-the-art and gradually explore and highlight significant opportunities and challenges for innovation and a more effective exploitation of the rich but disperse educational content available in the digital repositories of science museums and centres across Europe. Existing knowledge and the state-of-the-art were captured through literature review, as well as through an extensive consultation process within the OSR consortium. The literature review covered diverse fields, including formal and informal science learning in the context of the Contextualised Model of Learning, Inquiry-Based Learning, and Resource-Based Learning, and the use of educational metadata techniques for the characterisation of digital learning content with standardised educational metadata, and with user-contributed (social) metadata. Beyond the literature review, consultation within the OSR consortium provided clear insights into the trends, opportunities and challenges arising. By encouraging and facilitating the exchange between at least three distinct ‘worlds’, those of formal school education, informal science learning facilitated by science museums/centres, and the use of educational metadata, the OSR Educational Design has made effective use of the rich and diverse expertise represented in the project to reflect on the state-of-the-art and identify the emerging challenges. An important tool in the process of consortium-wide consultation was the ‘Educational Design Requirements Elicitation Questionnaire’, which is presented in Annex 1. The responses gathered through this instrument helped the finalisation of the OSR standards-based educational metadata scheme (the ‘OSR Application Profile’). In addition, partners and particularly Content Providers were invited and facilitated to participate in a structured exchange of views about, and experimentation with approaches to, the notion of Educational Pathways, as well as the practice-oriented and research opportunities arising out of social tagging techniques. The outcomes presented in the fifth chapter of this report have been informed with all input and feedback received from the project partners through these consultation procedures. The formulation of the present document was the last step in the process, aiming to present and explain the rationale, background and details of the OSR Educational Design and thus provide input into the next project phases.


6

3 Pedagogical principles Imagine an educational environment in which youngsters at the age of seven or eight, in addition to-or perhaps instead of-attending a formal school, have the opportunity to enrol in a children's museum, a

science museum, or some kind of discovery centre or exploratorium. As part of this educational scene, adults are present who actually practice the disciplines or crafts represented by the various exhibits. Computer programmers are working in the technology center, zookeepers and zoologists are tending the animals, workers form a bicycle factory assemble bicycles in front of the children's eyes...During the course of their schooling, youngsters enter into separate apprenticeships with a number of these

adults...If we are to configure an education for understanding, suited for the students of today and for the world of tomorrow, we need to take the lessons of the museum and the relationships of the

apprenticeship extremely seriously. Not, perhaps, to convert each school into a museum, nor each teacher into a master, but rather to think of the ways in which the strengths of a museum atmosphere, of apprenticeship learning, and of engaging projects can pervade all educational environments from

home to school to workplace. Gardner, 1991

[…As a society we need to recognise and support the vast, important

and successful learning enterprise that takes place outside of schools and the work-place –learning from museums, libraries, the Internet, TV, films, books, newspapers

and magazines. Collectively these experiences encompass what is known as the “free-choice learning” sector…Any public education reform effort that does not

embrace the benefits of free-choice learning is incomplete. Falk & Dierking (2002)1

According to modern pedagogy, teaching should be guided by a holistic planning process that takes the students' learning processes, the subject matter and the teaching methods into account. Students' orientation is a very important and significant variable which correlates positively with students' performance. It offers students the chance to link the information presented to their prior experience and knowledge. They have the chance to engage in an active and self-guided learning process. Consequently, effective learning processes should be designed with student's prior experience and knowledge. For to learn science in meaningful ways students need to see connections to familiar problems relevant and important in their daily lives. Additionally, situated learning fosters the ability to transfer acquired knowledge to a variety of different situations. Situated learning is an essential component of acquiring the ability for self-organised and self-regulated learning. Ideally, schools should provide opportunities for the development of a competence to learn and an ability to be an autonomous learner in the future. This includes the development of meta-cognitive learning competences like e.g. elaboration strategies or learning strategies and their application and usefulness. Learning processes in the future will be embedded in communicative situations, where teaching science offers good conditions for fostering communication and cooperation in students' experimental practices. For a content orientation the planned teaching topics should be based on a broad field of knowledge and application. To be effective, the teaching sequences should build up

1 The term “free-choice learning” is often used in the literature, mainly in the USA, instead of informal learning. In this document the two terms are considered identical.


7

in a way that student knowledge can increase and link, in other words be “constructed” by them. Learning processes in science are orientated to the increasing complexity in science. An increasing process of constructing systems and rules, a more and more theoretical guided model building on the basis of an experimental extraction of a part of reality are features of such scientific inquiry. This necessary systematic, long-term planned and cumulative learning contributes to well-arranged, internally linked and in different situations, flexible adaptive knowledge. Of course, the school is a part of the students’ life, but learning in school can only be successful if the information is also relevant beyond the walls of the school. There should be a guaranteed link to future learning processes. Summarizing these aspects, teaching and learning in science are successful if it is possible to realize a sequence of topics that both guarantee a systematic learning (vertical knowledge transfer) and situation-orientated learning with every day tasks and problems (horizontal knowledge transfer). A method orientation expresses the possibility for the students to learn the necessary subject and cross subject methods. In the learning groups there should be occasions for dialogs, at first guided by the help of a teacher, but more and more autonomous and aimed at the development of scientific orientated conceptions and concepts. The students should have the possibility to describe their individual learning pathways and their individual solutions to problems. In the teaching of science subjects in school, curriculum material and instructional strategies ideally should be tailored to the abilities and aptitudes of different types of learners. The overall objective should be to create learning environments which allow students to interact physically and intellectually with instructional materials through 'hands-on' experimentation and 'minds-on' reflection. Effort should be made to provide materials and instruction that give reality and concreteness to scientific concepts. Ideally, teachers should use a variety of instructional strategies and learning materials with the aim of increasing the impact and the effectiveness of their teaching. The importance of varying instructional techniques has been investigated recently (Bybee et al, 2008; Osborne & Dillon, 2008), suggesting that a strong relationship exists between a student's motivational characteristics and her or his preference for particular modes of instruction. This finding is important and should be taken into consideration in the design and implementation of instructional techniques and content. In practice, it is difficult to respond appropriately to students' motivational characteristics and preferred modes of instruction. Informal (free-choice) science learning environments (e.g., science museums, zoos and outdoor settings; science youth programs; science media) could be utilized to maximize this end. Therefore, it would be useful if science educators would consciously utilize (1) a wide repertoire of instructional strategies in their work with learners in schools, as well as (2) a wide range of out-of-school environments which foster science learning. Evidence from current research literature suggests that informal science experiences - in school-based field trips, student projects, community-based science youth programs, casual visits to informal learning settings, and the press and electronic media - can be effectively used to advance science learning (Hofstein & Rosenfeld, 1996). In the past, there was a strong link between learning contexts and methods; for example, it was assumed that the compulsory school context was tightly linked with formal learning methods, while the free-choice (out-of-school) context was linked with informal learning methods. This link is at best artificial and at worst harmful to the pedagogy of science teaching and learning. It is artificial because a person's


8

knowledge of science cannot be limited to what is learned in schools, and it can be harmful by limiting the types of learning opportunities available to students.

3.1 Bridging the gap between formal and informal learning: The need for educational reform

The most extensively taught science subjects in the countries of the European Union are Biology, Physics, Chemistry and Technology, which are all offered as mono-disciplinary subjects (Koulaidis, 2003; STEDE, 2002). On the contrary, subjects characterized by an interdisciplinary approach like Natural Sciences, Earth Sciences or Health Education-Hygiene are only offered in a low number of countries. The curricular subjects are characterized by great variation in the secondary level of most countries. As far as the curricular resources used in the science and technology based subjects it can be noted that the most widely used resources are textbooks (either officially prescribed or commercially offered); libraries’ resources and school laboratories. It is worrying however, that ICT are rather rarely used as teaching resources for science subjects. During the last decade some attempts have been made to evaluate the impact of efforts and investments made in Science and Technology Education worldwide, for example the two large scale studies (TIMSS, 2003; PISA, 2000, 2003, 2006). Among other things these two studies have explored the achievement and the attitudes towards S&T of the students’ population in many countries of the world. The main findings of these studies are that the average achievement of the students’ population is relatively low in most of the southern European countries. Additionally while the vast majority of students hold positive attitudes towards S&T at the early schooling stages (70-80% of the 4th graders in all countries), this situation is considerably moderated at the later stages (8th Grade). The message is clear. Our educational systems have to shift from the traditional paradigm of teacher-directed learning and the dissemination of knowledge, to learner-centred curricula that promote the development of lifelong learners who can think critically, solve problems and work collaboratively (Rocard et al, 2007; Osborne & Dillon, 2008). During the last centuries a considerable number of pedagogues and educational practitioners (e.g. Comenius, Pestalozzi, Montessori and Dewey) have stressed the importance of visualisation and of hands-on experiences as vital components to the learning process (Bransdford et al, 1999). As a result of these perspectives, may different pedagogical methods have been developed, especially those to be used in lower grades at elementary schools. But as the age of the students increases, more theoretical and abstract the teaching and the learning tends to be. Emerging trends in education are moving towards learner-centred approaches. In these, learning becomes an active process of discovery and participation based on self-motivation rather than on more passive acquaintance with facts and rules. The ideas of collaboration and joint construction of knowledge have also found its way into the school system. Still, the construction is often theoretical and mostly does not involve real products. Since the 1990s informal education has become a widely accepted and integrated part of school systems. However, examples of theoretical or empirical research concerning informal education are rare. Recently, informal learning has become a more accepted part of educational science, although there is still very little valid research for example about such a central topic as learning via the Internet. The role of the Internet is a clear example of a learning source that was originally created for other purposes. The Internet is an effective informal learning source, which was first used by individual


9

teachers and then officially by schools and other formal learning institutions. In other words, the Internet can be described by the term ‘out-of school education’ meaning schools using informal learning settings and sources as a part of their curriculum. Recent research (Salmi, 2001, 2003; Sotiriou & Bogner, 2008) in science centres indicates the following results: • The results of knowledge tests showed clear learning effects. However, the time

spent in a science centre is rather short, and because of that the focus must be on the quality and not the quantity of learning.

• The series of visits to a science centre appears to have a positive effect on the motivation of students in all the age groups.

• Gifted students seemed to get more motivated than others during the visits. However, by using programmes linking the school curriculum and science centre exhibitions, encouraging motivational results were also obtained for the group of students with learning difficulties.

• Informal learning sources such as science centres have an effect on career choices of university students.

Science centres are no longer isolated hands-on workshops created by a couple of ‘science freaks’, but have become part of a larger movement promoting public understanding of science. They are influenced not only by the scientific community, but also by the other groups of society and vice versa. Science education is not only a question of advancing technology or of demands for a scientifically qualified workforce, but it is also a question of social goals. As Osborne & Dillon (2008) summarise: ‘The aim is not solely to produce more scientists and technologists; it is also to produce a new generation of citizens who are scientifically literate and thus better prepared to function in a world that is increasingly influenced by science and technology’. The unique strength of museums is that they can provide schools with the real world context and the exploratory experiences that constructivists are advocating. With such an idea in mind, Gardner (1991a, 1991b, 1992) has brought a powerful message of educational reform to practitioners within the respective fields of education and museum education. In this message, he challenges schools and museums to combine the evocative learning that takes place within experience-oriented museums with the rigor and structure of a cognitive apprenticeship. Museum educational reform has pushed museums to give education a higher priority, by incorporating it into their mission statements and into every organisational activity. Such a goal requires the redefinition of museum-school collaboration. The OpenScienceResources Agenda aims to build upon the strengths of the informal learning taking place in science centres and museums, demonstrating a next generation learning scheme that crosscuts the boundaries between formal and informal learning settings by involving learners in extended episodes of playful learning. The partnership will investigate the pedagogic aspects of the application of innovative metadata techniques, to create a pedagogical framework that recognizes the diversity of personal learning styles and behaviours in different contexts and applications. The OpenScienceResources Agenda is based on three main pedagogical approaches, The Contextualized Model of Learning that represents the potential of informal education, the Inquiry Based and Problem Based approaches that are currently considered the most appropriate approaches for the reform of formal science education and the Resource Based Learning that are providing unique opportunities


10

for the effective bridging between formal and informal science education. These approaches are described in detail in the following sections.

3.2 Contextualized model of learning

The user centred approach of the OpenScienceResources project is based on the Contextual Model of Learning (Falk & Dierking, 2000). This model suggests that three overlapping contexts – the Personal Context, the Socio-cultural Context, and the Physical Context – contribute to and influence the interactions and experiences that people have when engaging in learning activities such as visiting museums.

3.2.1 Personal context The Personal Context describes all the personal characteristics that a person brings to an informal learning situation including his or her interests and motivations, learning style preferences, prior knowledge and experience, each very critical component of successful experiences (and learning). Motivation and emotional connection also play an important role in this context. Four important lessons are at the heart of the Personal context: 1) informal learning flows from appropriate motivational and emotional cues; 2) informal learning is facilitated by personal interest; 3) “new” knowledge is constructed from a foundation of prior experience and knowledge; and 4) learning is expressed within appropriate contexts.

3.2.2 Socio-cultural context However, personal factors are not the only influence on successful informal learning experiences. Learners rarely engage in informal learning alone and the Socio-cultural Context encompasses factors that recognize that learning is both an individual and a group experience. What someone experiences and learns, let alone why and how someone engages in such experiences, are inextricably bound to the social, cultural and historical context in which that experience and learning occurred. More often than not, informal learning experiences are shared experiences, opportunities for collaborative learning. And even those learners that choose to learn alone become a part of the socio-cultural milieu of the learning setting itself, in the case of a museum, a world of other visitors, staff and volunteers. In addition, there are all of the cultural overlays of what these informal learning institutions represent in a society (e.g., elitist or inclusive, modern or antiquated). Interestingly, not only is learning a socio-cultural process in the here and now, but the historical and cultural modes of communicating ideas are also socio-cultural in nature. This helps to account for the fact that universally, people respond well and better remember information if it is recounted to them in a story or narrative form, an ancient socio-cultural vehicle for sharing information.

3.2.3 Physical context An informal learning experience also does not happen in a vacuum, isolated from the real world. When executed well, informal learning takes place in rich physical environments, filled with many real world objects and connections that help to meaningfully contextualize the presented concepts/ideas. Physical Context factors also transcend the specifics of the learning situation. The architecture and “feel” of a


11

building or natural setting, the way learners are oriented, the design features which guide learners through the experience and the sights, sounds and smells, also strongly influence learning. The Contextual Model of Learning provides the large-scale framework within which to organize one’s conceptualisation of free-choice learning; the details vary depending upon the specific context of the learner.

Figure 1: Social tagging offers a less formal, more participatory, and highly distributed way to augment science centres and museums collections and exhibits with educational content that reflects the perspectives and the interests of their communities.

Thus, the experience, and any learning that results, is influenced by the interactions between these three contexts. In this approach, learning is a life-long dialogue between the individual and his or her environment through time. Visiting experience and learning can be conceptualized as a contextually-driven effort to make meaning in order to survive and prosper within the world. Following the Contextual Model of Learning, the approach of OpenScienceResources project is to promote a contextually-driven dialogue, i.e., a dialogue between the relevant science content and the individual’s personal, socio-cultural and physical contexts. None of these three contexts is ever stable or constant; all are changing across the life of the individual. The scenario design approach will guarantee that the methodologies and the tools deployed will indeed provide users with enhanced access to the science education resources, so that they are offered genuine opportunities for contextual learning in and around science museums, centres and knowledge repositories.

3.3 Inquiry-based learning

3.3.1 A Renewed Pedagogy for the Future of Europe In June 2007, a group of experts published the report "Science Education Now: A Renewed Pedagogy for the Future of Europe" (Rocard et al, 2007). The group, set up by Commissioners Janez Potočnik and Jan Figel, made a number of recommendations. In accordance with these recommendations all actors involved should support actions to promote the more widespread use of problem and inquiry-based science teaching techniques in primary and secondary schools as well as actions to bridge the gap between the science education research community and science teachers in order to facilitate the uptake of inquiry-based science teaching (IBSE). More specifically the main priorities for the science education at school level are:


12

• A reversal of school science-teaching pedagogy from mainly deductive to inquiry-based (inductive) methods provides the means to increase interest in science.

• Improvements in science education should be brought about through the new

forms of pedagogy: The introduction of the inquiry-based approaches in schools and the development of teachers’ networks should actively be promoted and supported.

• Renewed school’s science-teaching pedagogy based on IBSE provides

increased opportunities for cooperation between actors in the formal and informal arenas.

• Specific attention should be given to raising the participation of girls in key

school science subject, and to increasing their self-confidence in science. • Teachers are key players in the renewal of science education. Among other

methods, being part of a network allows them to improve the quality of their teaching and supports their motivation.

In the following sections some basic features of inquiry-based learning methods are presented.

3.3.2 Distinguishing features of inquiry-based learning Inquiry based learning has been characterized in a variety of ways over the years (Collins, 1986; DeBoer, 1991; Rakow, 1986) and promoted from a variety of perspectives. Some have emphasized the active nature of student involvement, associating inquiry with "hands-on" learning and experiential or activity-based learning. Others have linked inquiry with a discovery approach or with development of process skills associated with "the scientific method." Though these various concepts are interrelated, inquiry based learning is not synonymous with any of them. From a science perspective, inquiry based learning engages students in the investigative nature of science. So, inquiry involves activity and skills, but the focus is on the active search for knowledge or understanding to satisfy a curiosity. Teachers vary considerably in how they attempt to engage students in the active search for knowledge; some advocate structured methods of guided inquiry (Igelsrud & Leonard, 1988) while others advocate providing students with few instructions (Tinnesand & Chan, 1987). Others promote the use of heuristic devices to aid skill development (Germann, 1991). A focus on inquiry always involves, though, collection and interpretation of information in response to wondering and exploring. From a pedagogical perspective, inquiry based learning is often contrasted with more traditional expository methods and reflects the constructivist model of learning, often referred to as active learning, so strongly held among science educators today. According to constructivist models, learning is the result of ongoing changes in our mental frameworks as we attempt to make meaning out of our experiences (Osborne & Freyberg, 1985). In classrooms where students are encouraged to make meaning, they are generally involved in "developing and restructuring [their] knowledge schemes through experiences with phenomena, through exploratory talk and teacher


13

intervention" (Driver, 1989). Indeed, research findings indicate that, "students are likely to begin to understand the natural world if they work directly with natural phenomena, using their senses to observe and using instruments to extend the power of their senses" (National Science Board, 1991, p. 27). In its essence, then, inquiry-oriented teaching engages students in investigations to satisfy curiosities, with curiosities being satisfied when individuals have constructed mental frameworks that adequately explain their experiences. One implication is that inquiry-oriented teaching begins or at least involves stimulating curiosity or provoking wonder. There is no authentic investigation or meaningful learning if there is no inquiring mind seeking an answer, solution, explanation, or decision.

Figure 2: The Inquiry Cycle (http://inquiry.uiuc.edu). Inquiry based learning has been officially promoted as a pedagogy for improving science learning in many countries (Hounsell & McCune, 2002; NRC, 2000; Rocard et al., 2007). Inquiry can be defined as "the intentional process of diagnosing problems, critiquing experiments, and distinguishing alternatives, planning investigations, researching conjectures, searching for information, constructing models, debating with peers, and forming coherent arguments" (Linn, Davis, & Bell, 2004). It is often touted as a way to implement in schools the scientific method: "The crucial difference between current formulations of inquiry and the traditional "scientific method" is the explicit recognition that inquiry is cyclic and nonlinear." (Sandoval & Bell, 2004). However, we use inquiry based learning in a more specific manner, referring to a specific teaching model : an iterative process of (1) question eliciting activities, (2) active investigation by students, (3) creation, these are (4) discussed already at early stages of the process, leading to (5) reflection about knowledge and the learning process, which in turn leads to new and refined questions (1) and the process goes on for another cycle. Here we have to mention that the Guided research teaching model of Schmidkunz & Lindemann (1992) is a rather similar approach that has been adopted in many primary school science curricula (e.g. in Greece and in Cyprus). The word research in the model description reveals its aim to help students explore the research procedures themselves while the word “guided” emphasises that this research effort will take place as a structured discovery within the frame of organised teaching. This teaching model includes five teaching stages (bringing up the phenomenon to a problem, suggestions for confrontation with the problem, implementation of a suggestion, abstraction of the finding, consolidation) which are divided in several sub


14

stages (Schmidkunz & Lindemann, 1992). Still the implementation of this approach is also realised in a linear way in school practice. Inquiry Based Science Education (Rocard et al, 2007) Historically, two pedagogical approaches in science teaching can be contrasted. The first one, traditionally used at school, is the “Deductive Approach”. In this approach, the teacher presents the concepts, their logical – deductive – implications and gives examples of applications. This method is also referred to as ‘top-down transmission’. To be used, the children must be able to handle abstract notions, what makes it difficult to start teaching science before secondary education. In contrast, the second has long been referred to as the “Inductive Approach”. This approach gives more space to observation, experimentation and the teacher-guided construction by the child of his/her own knowledge. This approach is also described as a ‘bottom-up’ approach. The terminology evolved through the years and the concepts refined, and today the Inductive Approach is most often referred to as Inquiry-Based Science Education (IBSE), mostly applied to science of nature and technology. By definition, inquiry is the intentional process of diagnosing problems, critiquing experiments, and distinguishing alternatives, planning investigations, researching conjectures, searching for information, constructing models, debating with peers, and forming coherent arguments (Linn, Davis, & Bell, 2004). In mathematics teaching, the education community often refers to “Problem-Based Learning” (PBL) rather than to IBSE. In fact, mathematics education may easily use a problem based approach while, in many cases, the use of experiments is more difficult. Problem-Based Learning describes a learning environment where problems drive the learning. That is, learning begins with a problem to be solved, and the problem is posed in such a way that children need to gain new knowledge before they can solve the problem. Rather than seeking a single correct answer, children interpret the problem, gather needed information, identify possible solutions, evaluate options and present conclusions. Inquiry-Based Science Education is a problem-based approach but goes beyond it with the importance given to the experimental approach.

3.3.3 Inquiry is on a continuum In practice, inquiry often occurs on a continuum. On one end of the continuum of inquiry might be the use of highly structured hands-on activities and “cookbook” experiments; in the middle might be guided inquiry or the use of science kits; and, at the farthest end, students might be generating their own questions and investigations. A teacher’s goal should be to strive for the farthest end of the continuum where students are involved in full inquiry. There are times when he/she will find it necessary to employ lower-level inquiry strategies to meet specific goals. However, a teacher should not assume that a structured hands-on activity will necessarily have all the elements of inquiry. When choosing from the continuum, teachers will need to consider a number of variables such as their own teaching skills; student readiness, maturity, and ability; and pedagogical goals. Occasionally, the teacher will move back and forth on the inquiry continuum to meet certain goals and circumstances.


15

3.3.4 Problem-based learning (PBL) and problem-oriented learning (POL) Problem-Based-Learning (PBL) was first introduced at the Faculty of Health Sciences of McMasters University in Hamilton (Canada). It has the aim to train students to be self-responsible learners who decide over their individual learning paths, actively search relevant information, and simultaneously develop problem-solving as well as critical thinking skills. Furthermore, social competencies are fostered by organizing students into small tutorial groups, in which the given problems are initially discussed and learning objectives are specified. Working in groups is supposed to enhance students’ collaborative or team skills. Curricula based on Problem-Oriented-Learning (POL) usually work with real world problems, which facilitates the transfer of skills acquired during such a training to real work situations. The main objectives of POL are for students to acquire knowledge in such a way that it can easily be applied in practice, so that students learn to analyse and to solve problems. The role of the teachers is to provide educational material, such as relevant problems for a certain domain, and to guide students through their learning process. In a typical PBL process, small groups of students are confronted with some problem, which they first try to solve with their already acquired knowledge. At the same time, they are asked to identify and research the information they are still missing to really understand the problem. The search for information is self-directed and therefore personalized to the needs and learning-styles of each student. Finally new information is applied to the problem and students assess their own as well as their peers’ work. Since PBL is an active process, where students deal with real-world problems, study topics are usually seen as important and relevant to their own lives and consequently the motivation to learn is much higher than in traditional learning contexts. Reviews of experimental evidence concerning the effectiveness of problem-based learning suggested that although the approach may not improve content-free problem solving and may even initially reduce learning levels, it will foster long-term retention, flexible knowledge and specific problem solving skills. PBL may enhance transfer of concepts and integration of concepts into domain-specific problems. Furthermore, it enhances intrinsic interest of subject matter and enhances self-directed learning (Hmelo-Silver, 2004; Norman & Schmidt, 1992). One drawback of PBL is that the problems are provided by teachers, whereas psychological research, also in computer-based learning and training scenarios shows that learning effects and transfer are strongest, whenever learners are requested to be active by for example generating problems themselves or constructing their own problem scenarios. Thus, a problem oriented learning (POL) approach, where students are challenged to not only solve but also generate problems seems to be the preferable way of teaching. Furthermore, the selection of problems should be personalized, so that each student can work on exactly those problems, which are most appropriate with regard to his or her already acquired and still missing competencies. In other words, individual learning paths should be provided. This, of course requires technology enhanced learning environments, which are able to adapt to the individual needs of a learner and at the same time provide a possibility for collaborative work by assessing for which learning objects (LOs) a student should act as learner, peer, or mentor. In order to integrate POL with a personalized learning approach, the problems should be selected according to the individual skills or competencies of a learner. This requires on one the hand an assessment of which competencies the learner has already


16

acquired and which are still missing and on the other hand an analysis of the problem situations (or LOs) regarding the skills required and taught by this problem. Competence based Knowledge Space Theory (see below) provides a formal framework, which allows the integration of competencies, problem descriptions, and learner profiles. With respect to the domain independent skills, there are some general reasoning skills that are necessary for problem solving and that can also be acquired. Nickerson (2004) presents several trainable qualities and abilities that are involved in good reasoning. Examples are knowledge of the domain, of human cognition in general, of common limitations, foibles, and biases, the abilities to analyze and evaluate arguments, to take alternative perspectives, to manage own reasoning (plan, monitor, and assess, use meta-cognitive skills), or the sensitivity to missing information. In addition, there are several heuristics which can also be used independent of the domain, as for example analyzing the problem’s end and means, make assumptions explicit, representing the problem and partial solutions in different ways, breaking the problem down into manageable sub-problems, etc. Which strategies are finally effective, of course depends on the specifics of the problem as well as on knowledge and skill of the problem solver. Thus for an efficient training it is necessary to analyze the context variables as well as the learner profile.

3.4 Resource-based learning

3.4.1 Overview of Resource-based learning During recent years, the definition, role and uses of recourses have undergone a metamorphosis. The changes have transformed how we think about resources, the distributed production of and access to digital resources, and how, when, and for what purposes we create and use them. The metamorphosis has been propelled by the exponential growth of information systems such as the internet and the web, and the ubiquitous presence of enabling technologies in classrooms, libraries museums, homes and communities. While increasing the numbers of and access to resources is energizing, realizing the educational potential of these breakthroughs may prove daunting. This is particularly true in formal learning settings (schools and universities) where current practices do not emphasize optimizing available resources or preparing individuals to learn in resource-rich environments. Teaching focuses on established curriculum goals, sequences, resources, and activities. Subjects like science provide an opportunity to exploit Resource-Based Learning (RBL) alternatives, expanding both the materials and the methods used in teaching and learning. Resource-based learning “…involves the reuse of available assets to support varied learning needs” (Beswick, 1990). Several factors make RBL viable: 1) increased access to resources (print, electronic, people) in a variety of contexts not previously available; 2) resources are increasingly flexible in their manipulation and use; and 3) economic realities dictate that resources become more readily available, useable, and shareable across a variety of contexts and purposes.

3.4.2 Components of resource-based learning RBL features four basic components: enabling contexts, resources, tools, and scaffolds. Taken together these components enable educators to create and implement learning environments of considerable diversity and flexibility.


17

Table 1: Components and Characteristics of Resource-Based Learning

RBL Components Key Characteristics

Enabling contexts Imposed: Teacher or external authority determines goal. Induced: Learner or learner and teacher determine the goal.

Resources People, things or ideas that support the learning process.

Tools Objects used to help facilitate the learning process. Range from processing to organization to communication tools.

Scaffolds Support that is faded over time. Includes conceptual, metacognitive, procedural and strategic scaffolds

Table 1 provides and overview of key characteristics. Each of the components will be briefly described in the following paragraphs (for a more detailed description see Hill and Hannafin, 2001).

3.4.3 Enabling contexts Enabling contexts supply the situation or problem that orients learners to recognise or generate problems and frame their learning needs. By creating and enabling contexts, meaningful learning can occur with and through the resources provided or obtained. Enabling contexts can be imposed, induced or generated. Imposed contexts clarify expectations explicitly and guide teacher and student strategies implicitly. Teachers may use determined objectives (e.g. National Curriculum). Induced contexts introduce a domain where problems or issues are situated, but not specific problems to be addressed. A typical scenario enables multiple problems or issues to be generated or studied based on different assumptions, topical relevance, and the context of use (see Figure 3). In generated contexts, specific problem contexts are not provided; rather, the learner establishes and interpretive context based on his or her unique needs and circumstances.

Figure 3: Using the “timeline mode” the user can move in space and time through a database of unique historical scientific devices and archives that represent the major moments in the history of science. The organisation of the content of science museums in a common way will give opportunities for the presentation of the development of scientific ideas during the time, connected with the historical and social context of their time. The user will able, starting from an exhibit of one of the museums of the network, to perform a time travel in the history of science till the origin of the scientific ideas and explore their development as a consequence of the continuous interaction between theory and experimentation


18

3.4.4 Resources Resources are “raw materials” that support learning, such as electronic databases, textbooks, video, images, original source documents, and humans. Resources maybe provided by a more knowledgeable other (e.g. teacher) to assist others in extending or broadening knowledge or understanding. Resources may also be gathered by the learner as questions and/or needs arise. Given varying contexts of use, the utility of a resource may change dramatically from situation to situation. The web for example enables access to millions of resource documents, but their integrity and usefulness is judged by the individual and in accordance with the context of use. As resources increasingly become both relevant to the learners’ need and accessible, they assume greater utility.

3.4.5 Tools Tools enable learners to engage with and manipulate both resources and ideas. Tool uses vary with the enabling contexts and user intentions; the same tool can support different activities and functions. Eight types of tools are used in RBL: processing, seeking, collection, organisation, integration, generation, manipulation, and communication. Processing tools help students to manage the cognitive demands associated with RBL. Processing tools, such as self-directed learning systems, for example, enable learners to work with ideas, extending their cognitive abilities and reducing the need to “remember” or engage in unnecessary mental manipulation (see Jonassen and Reeves, 1996 for a discussion of cognitive tools). Seeking tools (e.g. keyword searches, topical indexes, search engines) help to locate and access resources. Seeking tools can also be specific to a particular context. For example “Sustainable Table” provides an educational portal that offer access to numerous resources, activities and games and promotes the positive shift toward local, small-scale sustainable farming. Sustainable Table was created to educate consumers on food-related issues, and to help build community through food. (http://www.sustainabletable.org/intro/). Collection tools, ranging from paper-based worksheets to high-end PDAs, aid in amassing resources and data for closer study. Learners might use collection tools as they explore a learning space or after completing a tour. For example the MEATRIX site (http://www.themeatrix.com/interactive/), which includes an animated, 360 degree interactive industrial dairy farm scene, provide an entertaining way to give students an overview of the problems associated with factory farms. Organisation tools are used to represent and define relationships among ideas, concepts, or “nodes”. Like collection tools, organisation tools range from electronic to non-electronic devices. Concept mapping tools (e.g. www.insparation.com) are powerful devices that enable users to demonstrate relationships and links between and amongst ideas. Integration tools help learners to relate in a new way with existing knowledge, which helps to both organise and integrate ideas. Integration tools might range from a word processing program to a web site. The depth and breath of what is represented by a single tool or set of tools vary according to the needs and abilities of the user.


19

Generating tools as simple as a web site or as sophisticated as a modelling tool (e.g. SimEarth) help learners to create “objects” of understanding. Manipulation tools, which also range in their complexity, are used to explore beliefs and theories-in-action. Finally, communication tools (both synchronous and asynchronous) support efforts to initiate or sustain exchanges among learners, teachers, and experts.

3.4.6 Scaffolding Scaffolding – support provided to assist learners and subsequently faded (Vygotsky, 1980) – varies with problem(s) encountered and the demands of the enabling context. Four types of scaffolding could be useful in exploring ways for the introduction of RBL in formal learning environments: conceptual, metacognitive, procedural and strategic. Conceptual scaffolds guide learners in what to consider, identifying knowledge related to a problem or making organisation readily apparent. Worksheets have traditionally been used in formal learning settings to help guide students as they explore a new concept or a topic. Conceptual scaffolding might be extended through communication tools in the form of leading questions or scenarios that set a context for the learners on a web site. Problem based learning makes considerable use of conceptual scaffolding to help guide learners as they explore new areas and build understanding (Knowlton and Sharp, 2003). Metacognitive scaffolds support the underlying cognitive demands in RBL, helping learners to initiate, compare, and revise their approaches. Scenarios or cases are often used to focus and guide the learners as they explore and attempt to understand. Scenarios or cases can present ideas for learners to consider as well as checkpoints where learners examine their understanding, seeking to uncover what they do and do not know or understand (Kolodner, 1993). Procedural scaffolding aids the learner while navigating and emphasizes how to utilize a learning environment’s features and functions. WebQuests, for example, use procedural scaffold extensively and have been used in a variety of contexts and content areas. According to Bernie Dodge, the primary creator, “WebQuests are designed to use learners’ time well, to focus on using information rather than looking for it, and to support learners’ thinking at the levels of analysis, synthesis and evaluation”. By focusing on “how to”, procedural scaffolds free up cognitive resources for other important learning activities (e.g. problem solving, higher-order thinking).


20

Resource-Based Learning Scenario Imagine a student engaged in a course of physics, engaged with the topic of acceleration. A traditional top-down approach would start with learning theory, followed by doing one or two experiments in a lab and concluding with an examination. An alternative approach based on problem-based learning starting with a research question, e.g. (e.g. “how are particles accelerated in an electromagnetic field?”) and provide the student with the means and support needed to pursue that question. The student has the opportunity to actually experiment with the graphs, data and animations and be able to construct a recommendation for the way of calculation of the rotation speed of the particles in LHC. Theoretical knowledge would be directly applicable to this realistic endeavour, and hence it will be easier to integrate this into the student’s existing knowledge. While on such a mission, students perform several types of learning actions that can be characterized as productive (experiment, game, share, explain, design, etc.), students encounter multiple resources, they collaborate with varying coalitions of peers, and they use changing constellations of tools and scaffolds (e.g., to design a plan, to state a hypothesis etc.). Fulfilling such a mission requires a combination of knowledge from different domains (e.g., physics, biology, engineering and/or social sciences). Scaffolding will take place by dedicated scaffolds and tools that will assist learners in all aspect of the inquiry and design processes. Scaffolds will include tools for data collection in the lab and in the field (using mobile equipment), means to create intuitive and mathematical models of phenomena, visualizations of data and processes of inquiry and collaboration and templates for the creation of reports and designs. Finally, strategic scaffolds provide ways to analyze, plan and respond, such as identifying and selecting information, evaluating resources, and integrating knowledge and experience. Several models have been particularly useful in selecting and evaluating resources. The I-Search process (Joyce and Tallmann, 1997) strategic scaffolding focuses on integrating knowledge an experience. I-Search enables learners to select a topic of personal interest, then guides through the process of finding and using information and developing a final product.

3.4.7 Opportunities and challenges with resource-based learning RBL creates opportunities for the qualitative upgrade of both teaching and learning, heretofore unavailable, optimising the affordances of available and future technologies across a range of diverse settings. RBL enables access to multitude of perspectives on a given phenomenon. One of the most completing characteristics of RBL is the ability to view a variety of resources from a potentially unlimited number and range of perspectives. This is currently apparent in how textbooks are used in formal learning settings. Textbooks are often written from a particular perspective to promote a specific view of events and processes. Digital resources may also be written from a particular perspective, but


21

ready access and easy cross-referencing enable extended access to more resources and therefore, multiple perspectives. RBL can be implemented in a variety of contexts. RBL approaches change both the nature and also the role of traditional resources (e.g. books), as well as the contexts in which they are used. RBL frameworks can be applied in multiple contexts, ranging from formal to informal, electronic to physical, specific to distributed locations, and at particular through unlimited time. RBL facilitates learner-centred approaches. While RBL tends to focus on individual approaches to learning versus teacher or large group approaches to learning, it is not inherently limited to one-to-one interactions. Learners (individually, in small groups, or classes) can access a multitude of electronic, print and physical resources to assist with their learning in an RBL context. While the individual needs maybe addressed, it does not necessarily follow that student work is isolated or without guidance. Learners may receive guidance or direction from an expert peer (e.g. an astronomer) via a communication tool. The key RBL focus is what the individual learner needs to facilitate growth in knowledge and understanding, not simply the group size or ratio; thus learner-centred approaches are not only supported but encouraged through RBL. RBL cultivates key skills and competencies. The skills and the competencies of the learners in the Knowledge Society are different from those of generations past. With the explosion of knowledge, resources and challenges, learners need more strategic approaches to identify what is important and the depth of knowledge or skill needed in different contexts. Increasingly, learners need to discriminate when “knowing that” versus “understanding why” is appropriate or necessary. Given the prevalence of inaccurate, questionable, and contradictory evidence, assertions and propaganda expands geometrically. It is no longer sufficient for learners to simply master what they encounter; they also need to demonstrate greater critical thinking, problem solving, reflection and self-direction than past generations. The use of open questions e.g. “how are particles accelerated in an electromagnetic field?” for example, stimulates an investigation rather than simple answer-seeking and engages the students in critical examination, reflection, and manipulation of multiple resources, thereby cultivating needed information seeking and evaluation skills. The potential of RBL is considerable. Whereas conventional teaching approaches address known learning goals using well-organised sequences, resources, and activities, methods for supporting context-specific, user-centred learning have been slower to develop. Increasingly, individuals evaluate a vast number of digital resources located in expanding information repositories. Individuals must recognise and clarify their learning needs, develop strategies to address these needs, locate and access resources, evaluate their veracity and utility, modify approaches based on learning progress, and otherwise manage their teaching or learning. RBL enables teachers and learners to take advantage of the information systems we now have available, expending the resources they use to enhance the teaching and learning process.


22

4 Digital educational resources and educational metadata

4.1 Introduction

During the past years a large amount of digital educational content has become available worldwide in the form of online collections, digital repositories and libraries. This large amount of digital educational content has the potential to support technology-enhanced learning. On the other hand, the creation of quality educational resources is a costly process (Zimmermann et al., 2007). Hence, reuse of high quality learning materials has become a very important research topic for a variety of people, organizations etc., as it can lead to an important reduction of development cost and time, while at the same time it can improve the quality of technology-enhanced learning. In this context, effective and efficient organisation of the available digital learning content is gaining importance. In the relevant discussions and developments, ‘Learning Objects’ and ‘Educational Metadata’ are two crucial terms. These two terms are briefly discussed below. Typically, educational resources in technology-enhanced learning are organised as ‘Learning Objects’ (LOs) (Wiley, 2002; McGreal, 2004). Over the last decade LOs have gained a lot of interest as the basis of a new type of computer-based instruction in which the instructional content is created from reusable components. Learning Objects are presented in literature as a new way of thinking about learning content that is developed to support technology-enhanced learning processes (Polsani, 2003). Learning objects were defined by the IEEE Learning Technology Standardization Committee (LTSC) as ‘any entity, digital or non-digital, that can be reused to support learning, education or training’ (IEEE LOM, 2002). In general, any digital resource that can be reused to support learning (Wiley, 2002), can be considered as learning object. Learning objects include, but are not limited to, simulations, animations, tutorials, diagrams, audio and video clips, quizzes and assessments. The main difference between a learning object and an information object is that the learning object is designed to support a concrete educational goal: that is, it is associated with one or more educational objectives. On the other hand, the use of Educational Metadata is the contemporary response given by technology-enhanced learning specialists to the question of better organising the exponentially growing bodies of digital educational content (Learning Objects), so that it becomes more accessible, better searchable and (re-)usable. Metadata are generally defined as data about an information resource, or simply data about data (Berners-Lee, 1997). They describe the different characteristics and


23

attributes of an information source, e.g. Title, Author, Date and Subject. Further, a metadata model is a structured description about the characteristics and properties of an information resource, and allows the creation of catalogues and indexes for information resources, as well as searching information resources on the basis of these characteristics. The metadata specification widely used for the description of digital information resources is Dublin Core2 (DC) (Greenberg, 2001). Educational metadata, simply put, are data about Learning Objects, i.e. meta-information attributed to educational digital content in order to describe its various characteristics. The current section (Section 4) of this document examines the two main options offered in the field of characterising Learning Objects with metadata, namely: a) the use of pre-defined, standardised metadata (the standardisation lying on the basis of expert knowledge); and b) the use of metadata directly contributed by the end-user beyond the use of a standard (the so-called ‘social metadata’).

4.2 Characterizing digital resources with standardised educational metadata

An important factor in making search and retrieval of digital resources more efficient is the quality and quantity of metadata associated with these resources. A response to this need is provided through the use of metadata models. A metadata model is a structured description about the characteristics and properties of an information resource, and allows the creation of catalogues and indexes for information resources, as well as, searching information resources on the basis of these characteristics. In the case of Learning Objects, generic metadata models for digital resources such as the Dublin Core model are not sufficient, as they do not include information about the educational characteristics of an object. Therefore, specialized models that give emphasis on the educational metadata of digital resources have been developed. Educational metadata represent the educational characteristics of a learning object, such as the target groups it involves, or the thematic area it concerns. The educational metadata specification widely used for the description of learning objects is the IEEE Learning Object Metadata (LOM) (IEEE LOM, 2002). This standard will be presented further below, following a review of other available educational metadata standards.

4.2.1 Educational metadata standards Dublin Core Education Working Group This working group of the Dublin Core Metadata Initiative is involved in the development of education-specific elements, element qualifiers, and value qualifiers (controlled vocabularies) to be used with the Dublin Core to describe educational materials for the purpose of enhancing resource discovery.

2 http://dublincore.org


24

GEM (Gateway to Educational Materials) Metadata Element Set The Gateway to Educational Materials is a large U.S. initiative with the goal of providing Internet access to educational materials. GEM has created a metadata element set based on Dublin Core with the addition of education-specific elements. Although not billed as a "standard", the GEM Element Set has been widely used; the metadata elements used in CHIN's Learning with Museums were based largely on the GEM Element Set. GEM staff is working closely with Dublin Core Education Working Group. IMS Global Learning Consortium (IMS) IMS is a global consortium of members with an interest in providing access to online learning resources. It is involved in the development and promotion of "open specifications for facilitating online distributed learning activities such as locating and using educational content, tracking learner progress, reporting learner performance, and exchanging student records between administrative systems". IMS produces a suite of specifications, including a metadata specification and a content packaging specification. IMS uses IEEE LOM as its base. IMS metadata elements can be mapped to the more general Dublin Core elements, as well as to education-specific element sets. A recent agreement among IMS, Dublin Core, and the IEEE promises "significant harmonization and collaboration...in the areas of educational metadata interoperability and implementation". Canadian Core Learning Resource Metadata Protocol (CanCore Protocol) CanCore was developed by a group of national and provincial educators and technology developers with funding and support from Industry Canada/CANARIE and other groups. CanCore is a standard for educational metadata that is "based on and fully compatible with the IMS Learning Resource Metadata Information Model. CanCore has defined a sub-set of data elements from this IMS model for the purposes of the efficient and uniform description of digital educational resources in Canada and elsewhere. It is intended to facilitate the interchange of records describing educational resources and the discovery of these resources both in Canada and beyond its borders". IEEE Learning Objects Metadata (IEEE LOM) Standard The IEEE (Institute of Electrical and Electronics Engineers) Learning Technology Standards Committee (LTSC) has created a draft standard for Learning Object Metadata (LOM). "The LOM outlines the minimal set of attributes needed to allow Learning Objects (either digital or non-digital) to be managed, located, and evaluated". The LOM standard has been mapped to Dublin Core; the mapping is available in an appendix of the standard. The IEEE LTSC-LOM and the Dublin Core Metadata Initiative (DCMI) have announced their joint commitment to develop interoperable metadata. The IEEE LOM, being the most widely used educational metadata standard, is discussed in detail in the following section.

4.2.2 The IEEE Learning Objects Metadata (IEEE LOM) Standard IEEE Learning Object Metadata 1484.12.1-2002 (IEEE LOM) standard (IEEE LOM, 2002) was suggested by the IEEE Learning Technology Standardization Committee and is implemented by a big number of research and commercial projects internationally.


25

IEEE LOM standard was developed to cover the needs of those that create web-based collections of learning objects and need standardized metadata in order to share, search, retrieve and manage them. IEEE LOM standard defines a semantic data schema, which defines the structure of a learning object metadata instance IEEE LOM Standard consists of the following metadata categories:

General: contains information about the description of the learning object as a

whole. Life Cycle: contains information about the history and current state of the

learning object Meta-Metadata: contains information about the metadata instance Technical: contains information about technical requirements and technical

characteristics of the learning object Educational: contains information about educational and pedagogic

characteristics of the learning object Rights: contains information about intellectual property rights and conditions

of use of the learning object Relation: contains information about the relationship of the learning object

with other learning objects Annotation: contains comments on the educational use of the learning object Classification: contains information about the relation of the learning object

with a particular classification system. Figure 4 on the next page represents the categories and the elements of the IEEE LOM standard.

4.2.3 Application Profiles

As more and more applications are implemented using educational metadata, it was recognized early enough that it is not possible for a generic standard such as IEEE LOM to meet specific requirements and accommodate the particular needs of different educational communities. As a result, a practice of generating Application Profiles (APs) of the IEEE LOM has emerged and a number of different Application Profiles have been developed worldwide.

4.2.3.1 Definition The European Committee for Standardization (CEN/ISSS) defines an Application Profile as: “an assemblage of metadata elements selected from one or more metadata schemas and combined in a compound schema. Application profiles provide the means to express principles of modularity and extensibility. The purpose of an Application Profile is to adapt or combine existing schemas into a package that is tailored to the functional requirements of a particular application, while retaining interoperability with the original base schemas” (Duval et al., 2006).


26

Figure 4: Categories and Elemetns of IEEE LOM standard (IEEE LOM, 2002)


27

4.2.3.2 Main reasons for developing an application profile An application profile is typically developed for a particular application with a particular constituency. Such a community may be large (for instance: the European Academic context) or small (for instance: a small enterprise in a particular domain) (Duval et al., 2006). According to IMS Global Learning Consortium, the main reasons for the development of an application profile can be summarized below (IMS, 2005a):

To meet technical and other requirements and preferences specific to a project, community, domain, or region

To address ambiguity and generality in a specification or standard To foster semantic interoperability, e.g., through the use of commonly

understood vocabularies To facilitate testing for conformance and successful interoperability.

4.2.3.3 Methodology for developing an application profile It is important in the process of Application Profiling to provide the communities, which are interesting in developing Application Profiles, with a consistent approach that will facilitate them during that process. The IMS Global Learning Consortium recognizes several benefits from this decision, namely (IMS, 2005a):

Consistent set of rules for constructing an Application Profile will bound the changes that can be made thus ensuring greater interoperability across conformant Application Profiles.

Consistent documentation of Application Profiles will enable vendors to more easily build products and services that span multiple communities with simple configuration settings for localization.

The growing number of publicly documented Application Profiles will allow subsequent adopting communities to select and reuse elements of existing Application Profiles, rather than develop from first principles.

Providing strongly typed, machine readable definitions of Application Profiles will enable runtime context negotiation between domains to facilitate data exchange and interoperability across communities.

In order to achieve the above mentioned benefits, International Organizations such as IMS Global Learning Consortium and European Committee for Standardization (CEN/ISSS) have published specific guidelines for the development of Application Profiles with specific focus on the IEEE LOM standard. These guidelines include the following steps (IMS, 2005a; IMS, 2005b):

Step 1 – Selection of data elements: During this step the Application Profile Developer is selecting the data elements that the Application Profile will be built on

Step 2 – Size and smallest permitted maximum: This step includes the definition of the size that a data element may be allowed to be present in one metadata instance


28

Step 3 – Data elements from multiple namespaces: This step aims at the definition of data elements from different namespaces, which are part of different metadata schemas

Step 4 – Adding local data elements: During this step new local data elements, which are not contained to the initial metadata schema, are added to the AP

Step 5 – Obligation of data elements: This step aims at the definition of mandatory data elements (value for the data elements shall always be present), conditional (if a certain condition is satisfied, then a value for the data element shall be present), recommended (some Application Profiles recommend including values for specific metadata elements)

Step 6 – Value space: During this step the value space of the data elements is defined. The value space defines the set of values that the data element shall derive its value from

Step 7 – Relationship and dependency: This step includes the definition of inter-relationships and dependencies between data elements

Step 8 – Data type profiling: This step aims at the definition of the data types of specific metadata elements.

Step 9 – Application profile binding: The final step includes the production of the AP binding, which is the conceptual data schema of the Application Profile and should be represented using XML or RDF.

4.2.3.4 Review of existing IEEE LOM application profiles Several educational communities have developed a number of Application Profiles, so as to adapt the IEEE LOM standard to their specific requirements. In this paragraph, we are presenting a number of well known Application Profiles already developed worldwide including:

The Learning Resource Exchange (LRE) Application Profile The VETADATA Application Profile The Australia New Zealand LOM (ANZ-LOM) Application Profile The UK-LOM Core Application Profile The JORUM Application Profile The Resource Discovery Network (RDN)/Learning and Teaching Support

Network (LTSN) LOM Application Profile The Department of Education and Training (DET) Learning Resource

Metadata (DET-LRM) Application Profile. LRE IEEE LOM Application Profile The Learning Resource Exchange (LRE3) by the European Schoolnet (EUN) partnership is a service that enables schools to find educational content from many different countries and providers. LRE content is provided by 31 Ministers of Education (MoE), commercial and non – profit content providers (Publishers), and cultural heritage organizations (Museums). It includes also user-generated content (Teachers). The LRE Application Profile (EUN Partnership, 2007) is compatible with IEEE LOM and consists of five (5) mandatory, nine (9) recommended and twenty three (23)

3 http://lreforschools.eun.org/LRE-Portal/Index.iface


29

optional metadata elements. For the metadata elements that are using vocabularies, these are derived mostly from the IEEE LOM. However, the LRE Application Profile extends the suggested IEEE LOM vocabularies for the following elements:

5.5 Educational.Intended End User Role 5.6 Educational.Context 7.1 Realtion.Kind 9.2.2 Classification.Taxon Path.Taxon

Figure 5 on the next page presents an overview of the LRE Application Profile metadata elements. VETADATA IEEE LOM Application Profile VETADATA (VETADATA, 2009) is an application profile for describing educational resources in VET Systems. It was developed by and for Australian vocational education and training system VETADATA is a subset of IEEE LOM Standard and provides detailed guidelines on specific vocabularies to be used to describe various aspects of educational resources. The figure below presents an overview of the VETADATA Application Profile metadata elements. The VETADATA Application Profile consists of six (6) mandatory, six (6) recommended and nine (9) optional metadata elements. The VETADATA Application profile is using the suggested IEEE LOM vocabularies for its most elements and extends only two (2) IEEE LOM vocabularies for the following elements:

5.2 Educational.Learning Resource Type 9.2.2 Classification.Taxon Path.Taxon

Figure 6 presents an overview of the VETDATA Application Profile metadata elements. ANZ IEEE LOM Application Profile ANZ–LOM Application Profile (ANZ-LOM, 2008) was developed by the Le@rning Federation, which is an initiative for collaboration between the Ministries of Education in Australia and New Zealand. ANZ-LOM Application Profile consists of twenty one (21) mandatory, eight (8) recommended and nine (9) optional metadata elements. The ANZ-LOM application profile is using the suggested IEEE LOM vocabularies for the most of its metadata elements and extends the vocabularies for the following metadata elements:

2.3.1 LifeCycle.Contribute.Role 5.2 Educational.Learning Resource Type 9.2.2 Classification.Taxon Path.Taxon

Figure 7 presents an overview of the ANZ-LOM Application Profile metadata elements.


30

Figure 5: An overview of LRE IEEE LOM Application Profile


31

Figure 6: An overview of VETADATA IEEE LOM Application Profile


32

Figure 7: An overview of ANZ IEEE LOM Application Profile


33

UK-LOM CORE IEEE LOM Application Profile UK LOM Core (UK-LOM Core, 2008) is an application profile of the IEEE LOM that has been optimized for use within the context of UK education. The primary objective of the UK LOM Core is to increase the interoperability of metadata instances and application profiles within the UK educational community. UK-LOM Core Application profile consists of twelve (12) mandatory, twenty (20) recommended and thirteen (13) optional metadata elements. The UK-LOM Core application profile suggests extensions for the IEEE LOM vocabularies of the following metadata elements:

5.2 Educational.Learning Resource Type 5.6 Educational.Context 9.2.2 Classification.Taxon Path.Taxon

Figure 8 on the next page presents an overview of the UK-LOM Application Profile metadata elements. JORUM IEEE LOM Application Profile JORUM Application Profile (Stevenson, 2005) has been developed for UK further and higher education, as part of the JORUM Project funded by the Joint Information Systems Committee (http://www.jisc.ac.uk/) The JORUM Application Profile consists of fourteen (14) mandatory, sixteen (16) recommended and twelve (12) optional metadata elements. JORUM application profile is based on UK-LOM Core application profile and for this reason it is using the same extended vocabularies with UK-LOM Core. Figure 9 presents an overview of the JORUM Application Profile metadata elements. RDN/LTSN IEEE LOM Application Profile RDN/LTSN Application Profile (Powell, 2005) facilitates record sharing between Resource Discovery Network (RDN) and Learning and Teaching Support Network (LTSN) using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). The RDN/LTSN Application Profile consists of seven (7) mandatory, seven (7) recommended and eleven (11) optional elements. The RDN/LTSN is extending the vocabulary of only two (2) metadata elements, which are the following:

5.2 Educational.Learning Resource Type 9.2.2 Classification.Taxon Path.Taxon

Figure 10 presents an overview of the RDN/LTSN Application Profile metadata elements.


34

Figure 8: An overview of UK-LOM Core IEEE LOM Application Profile


35

Figure 9: An overview of JORUM IEEE LOM Application Profile


36

Figure 10: An overview of RDN/LTSN Core IEEE LOM Application Profile


37

Figure 11: An overview of JORUM IEEE LOM Application Profile


38

DET-LRM IEEE LOM Application Profile DET Learning Resource Metadata (DET-LRM) Application Profile (DET-LRM, 2007) specifies a set of metadata elements and vocabularies to describe educational resources in the New South Wales Department of Education and Training (DET) in Australia. This application profile is based on the IEEE LOM Standard and its purpose is to support searching, evaluation, access, management, exchange and use of educational resources in DET. The DET-LRM application profile has no mandatory and recommended elements. It has twenty seven (27) optional elements. DET-LRM application extends the vocabulary of five (5) metadata elements, which are the following:

2.3.1 LifeCycle.Contribute.Role 5.2 Educational.Learning Resource Type 5.5 Educational.Intended End User Role 5.6 Educational.Context 9.2.2 Classification.Taxon Path.Taxon

Figure 11 on the previous page presents an overview of the DET-LRM Application Profile metadata elements.

4.2.4 Repositories and metadata Typically, educational resources tagged with metadata are stored in web-based repositories referred to as Learning Object Metadata Repositories. Learning Object Metadata Repositories store learning object metadata records and offer facilities for searching, retrieving and sharing educational resources in the form of learning objects. Learning object metadata repositories are being developed worldwide in order to collect descriptions of learning objects, and facilitate interested users (such as educators, students and content providers) in locating and accessing them. Popular learning objects metadata repositories include MERLOT4 in USA and ARIADNE5 in Europe. Standards and specifications are important to maximise the potential for interoperability and because they detail best practices that are relevant to almost all the aspects of repository operations. We address the following three questions: 1. Why does a repository need to support standards and specifications and which ones to adopt? 2. How are specifications and standards applied to build an interoperable repository? 3. How are standards-compliant metadata and vocabularies created and maintained? Repositories do not operate in isolation. They are typically a component within a broader infrastructure and can potentially interconnect and interoperate with other

4 http://www.merlot.org 5 http://www.ariadne-eu.org


39

services such as virtual learning environments, authoring tools, content aggregators, registries, metadata generators and educational portals. Excellent repositories exist that provide good collections of high-quality learning resources and a successful user experience. While it is doubtful that a single source could provide all the necessary learning resources that a teacher or learner might require, most users prefer to get access to all the resources they need via a unique access point. Therefore, they should be able to transparently search multiple information sources from within their favourite learning portal or virtual learning environment rather than having to visit several repositories providing different and unfamiliar environments and end-user experiences. The use of standards and specifications is of crucial importance to supporting this kind of scenario. There are many examples of problems being experienced by both repository owners and teachers, which result from the absence of common standards and specifications. They include general issues such as the lack of agreed specifications to solve problems, methodological issues and more specific issues related to the use of metadata and vocabularies, content and how to expose it. General issues:

Existing standards and specifications are insufficiently used; In some areas, no agreed specifications exist.

Methodological issues:

A fragmented approach to funding, particularly in the cultural sector, means that there is no consistency of approach;

The standards and specifications adopted by repositories are not always appropriate for use with particular groups of suppliers and users;

Specifications for describing digital and physical learning materials are not always aligned and compatible;

Backward compatibility problems exist between different versions of some specifications.

Metadata-related issues:

There is a need for the use of persistent identifiers; It is currently unclear whether there is a requirement for resolver services,

such as those based on OpenURL, within the UK school sector; DC-based profiles are sometimes more appropriate for tagging individual

assets than IEEE LOM; Teachers and other creators of digital learning resources are often poor at

creating formal metadata; The use of information professionals to produce metadata is expensive; Content providers often provide insufficient metadata; Application profiles of standards and specifications are often required to

ensure interoperability, particularly in a Web 2.0 environment; The mechanism for storing ‘rights’ information within IEEE LOM is too

limited; There are numerous (some would say ‘too many’) metadata standards, each

being based on different conceptual assumptions which makes their harmonisation non-trivial.


40

Vocabulary-related issues: Much metadata displays incoherence in terminology and an absence of

controlled vocabularies; Where controlled vocabularies are used, they are often insufficient or

inappropriate; A strategy is required for avoiding the need to re-tag content when

specifications change; The relationship between controlled vocabularies and folksonomies is unclear; Maintaining controlled vocabularies is problematic; There is a lack of tools to automate the bulk updates and changes to

vocabularies; There is a need for a simple common vocabulary for describing genres/types

of resource. Content-related issues:

Content often does not conform to the required technical standards; It is difficult for users (i.e., teachers, faculty) to create standards-compliant

content. Exposing content:

Syndication mechanisms such as RSS and Atom are not used to any great extent;

Backward compatibility problems exist between RSS 2.0 and earlier versions; There is no accepted standard for publication/deposit of materials into a

repository; Some repositories do not allow search engines to index their content; There is no agreement on a common CQL context set to facilitate the use of

SRW/SRU with IEEE LOM metadata; Methods of exposing content need to integrate with, or at least be aware of,

authentication and authorisation services Federation.

Exemplars 1. The LRE (http://lre.eun.org) developed in the framework of the Calibrate and Melt projects provides several examples of best practice. The LRE Metadata Application Profile is a profile of LOM that comes with:

internationalised controlled vocabularies (each vocabulary term = token + translation in multiple languages) that facilitate the automatic translation of the metadata. These vocabularies include a multilingual thesaurus for the European school domain.

An XML binding Tools for tagging resources, compliance testing and automatic metadata

translation. The LRE offers to repositories several ways to expose their content to the LRE users: Federated searching with SQI, harvesting with OAI-PMH, and batch-upload of metadata. 2. One major achievement of Curriculum OnLine (UK) has been to help move forward the technical state of play regarding learning content repositories. It has


41

demonstrated that running a large-scale content repository for the school sector is perfectly feasible, and significant operational lessons have been learned. These include:

A large number of suppliers are able to provide metadata conforming to the COL specification (which is a profile of IEEE LOM), with support from the COL tagging tool;

The metadata provided allowed an effective and consistent user experience in searching and browsing the repository; and

It has clarified some key operational processes for managing a repository. 3. The e-Framework is a good example of a Service Oriented Approach. 4. In the open source world, FEDORA (http://www.fedora.info/), SCAM (http://scam.sourceforge.net/), DSpace (http://www.dspace.org/) and MINOR (http://minor.sourceforge.net/) are all examples of repository software designed to be interoperable through support for harvesting and/or open search protocols. 5. ARIADNE, CORDRA and LRE are examples of good practice in the use of unique identifiers to provide persistence in a federated environment. 6. LORN in Australia, in collaboration with E-standards for Training (http://e-standards.flexiblelearning.net.au/), works with repository owners to continually improve the technical quality of the content in order to achieve standards conformance. Over time, this has led to improvements in searchability and usability. 7. edna (http://www.edna.edu.au/) provides several examples of best practices:

A metadata application profile of Dublin Core (http://www.edna.edu.au/metadata) with several controlled vocabularies (http://www.groups.edna.edu.au/course/view.php?id=1132&topic=2)

Extensive use of RSS 2.0 throughout edna services www.edna.edu.au/edna/go/resources/toolkit/rss_services

8. MIT OpenCourseWare (OCW) objects are available both as MIT OCW Common Data Interchange Format (CDIF), which is a format compliant with IMS Content Packaging, and as flat HTML. The MIT OCW metadata implementation has adopted the use of the IEEE LOM standard for capturing descriptive, technical and rights metadata. Further rights metadata has been captured in a Filemaker Pro database. Additional operational and administrative metadata has been captured in the content management system used to create and publish the MIT OCW website. It is recommended that OCW implementers adopt the IEEE LOM metadata standard. It is flexible and can be extended to accomplish a wide range of metadata requirements. It is also recommended that OCW implementers take advantage of the flexibility of LOM to incorporate controlled vocabularies for describing the subject, genre and format of learning objects. The taxonomic work of institutions such as the Library of Congress, IEEE and the National Center for Educational Statistics was employed in the creation of metadata for MIT OCW objects. Individual implementations may require institution- or context-specific vocabularies, such as the National Library of Medicine’s Medical Subject Headings (MeSH).


42

9. The RUBRIC toolkit is aimed at streamlining the approach taken by Australian and New Zealand universities in building their research capability through implementing institutional repositories. While developed specifically for the university sector, it provides useful guidelines under the following headings:

Potential Repository Functions Planning System Options Establishing a Pilot Repository Publicity and Marketing Populating the Repository Managing a Repository Digital Preservation Management Data Management Metadata Access Management Pilot to Production

Metadata harvesting and federated search OAI-PMH is the preferred protocol used for metadata harvesting between educational repositories. Whereas (subject based) repositories in higher education often will have a global aim, the context for harvesting metadata in K-12 repositories more often seems to be national; reasons for this being primarily language and cultural barriers. The generality of OAI-PMH is an advantage as it allows it to be used for creating a central index solution relevant for educators based on fairly different metadata sources (e.g. cultural heritage sector, public and commercial content supplier repositories). The use of federated search schemes not based on metadata harvesting seems to be diminishing, with the most cited reasons for this including inconsistent search results for end users and technically complex implementation models. Automatic metadata generation Most educational repositories have had a strong focus on providing (high quality) descriptive metadata at the time of publishing – balancing the number of desired metadata elements with resources and expertise available to tagging resources. Better solutions are needed and simplifying the process of metadata creation by automatic tools has high priority among network members. The reasons for the focus on (semi-) automatic tools include the trivial fact that i) metadata creation by professional indexers is (too) expensive and ii) the quality of metadata produced by authors at the time of deposit is often not sufficient to provide good search and discrimination possibilities. Many repositories currently do some sort of validation/moderation of teacher produced metadata for exactly this reason. The content of a number of “simple” metadata elements are already automatically generated in many repositories as part of the depositing process – most notably: file type/size; user details; dates and other elements directly available from either uploaded files or the user currently logged in/tagging/depositing a resource.


43

The biggest potential to be realized in the near future is probably evolving around title, keywords and description elements. As these are not education specific, other content producing sectors are well worth watching for relevant tools and services. To help improve the quality and simplify the process work is currently undertaken on for example: • filling in more default element values by use of profiles/templates • (semi-) automatic metadata creation based on a range of different tools6 • include possibilities for user tagging • integration of descriptive metadata generation into authoring tools • context aware metadata creation (e.g. when depositing directly from VLE).

4.2.5 Some additional issues Metadata and vocabularies are fundamental components in the authoritative descriptions of learning resources. It would be justified to say that choosing and correctly using metadata standards (such as LOM, Dublin Core, MARC and EAD, to name just a few) is equally important to the success of developing digital libraries for education and lifelong learning. Selection of one standard or variation on a standard comes with both advantages and disadvantages for cataloguers (i.e., having a metadata standard format that matches the assets they want to catalogue versus having to adapt assets to a standard that does not quite fit). As in other areas, establishing vocabularies is still a concern – especially if resources have to cross borders, or even just different segments of the educational sector. One specific aspect is linking resources to the curriculum to provide search mechanisms based on this. The traditional perception of metadata as an aid to resource discovery, definitely still holds true. But as our access to information becomes more abundant, the importance of metadata as an aid to discriminate also becomes more pronounced. Metadata can support discrimination by providing additional information, often about relationships that helps clear a path to discovery. If you search in a repository for a resource on grammar the discovery function of metadata might tell you that there are 10 resources on the topic. However, it might also tell you the number of other grammar resources that each author/content supplier has produced which will allow you to discriminate and select a resource by someone who is clearly devoted to producing grammar resources. Yet another use of metadata is for recommendation. While discovery and discrimination are based on activities and choices dictated by the seeker, recommendation systems push information based on the activities and choices of others. As a result they can lead to the discovery of resources the seeker might never have otherwise considered. The repository could for example point out that users downloading a specific grammar resource also downloaded resources on mobile dictionaries, or on podcasting, etc. The point about recommendation systems is that the results are often unanticipated.

6 For a recent discussion on available tools and the different types of metadata they can help produce see the synthesis report from the JISC funded project on “Automatic Metadata Generation: Use Case Identification and Tools/Services Prioritisation”


44

Considerations along these lines means that the current focus for educational repositories is shifting away from descriptive metadata produced at the time of depositing/publishing a resource. Current focus is much upon best practice for producing social, usage and attention metadata. To this end focus is on existing best practice outside the educational sector instead of developing new standards. In the context of school education, a series of projects and initiatives in the field of educational content enrichment, organisation and metadata structures have been launched, mainly from EUN, the European Network of Ministries of Education in an effort to organise educational content for school use. CELEBRATE (http://celebrate.eun.org/eun.org2/eun/en/index_celebrate.cfm) has set the following question back in 2003: “Do emerging standards (for interoperability) make it easier for schools to exchange and reuse LOs (Learning Objects) within the sorts of learning platforms (LMS, LCMS, VLEs etc.) that schools are increasingly using? And can teachers and pupils make their own standards’ compliant, interoperable LOs?” During the validation phase of the CELEBRATE project it was clearly demonstrated that metadata created by an indexer related to a learning resource type may not always reflect how a resource will really be used in classrooms by experienced teachers. For example, an indexer might decide to add metadata which indicates that something is essentially a “drill and practice” type of resource whereas, in practice, teachers might actually be able to use that resource in many different pedagogical contexts – even for collaborative learning. This phenomenon is even more complex in the case of science museums and centres digital resources, which are being developed in an informal learning context, still one of the target audience are high school teachers who work in formal learning settings and value materials and resources to national content standards and science curricula. Practices for retrofitting existing digital assets to fit within a learning object framework are also rarely addressed in current literature on learning objects and LOM. The discussion of developing libraries that use IEEE LOM as their main metadata standard revolves almost exclusively around developing learning objects from scratch—a practice that is too time consuming, expensive, and unsustainable as a design practice with the preservation and public dissemination of science centres and museums extended educational resources (Fait and Hsi, 2005) subset of LOM fields can hinder development and interoperability. Further, developing additional vocabularies for assets that don’t fit a standard requires skills that are both complex and time consuming to master. The experience from the development of the Exploratorium Digital Library (http://www.exploratorium.edu/educate/dl.html) shows that LOM fell short in allowing the development team to accurately and reliably describe informal science learning assets, and thus, the development of new vocabularies for existing fields and extending the range of LOM fields was considered necessary (Fait and Hsi, 2005). As a result, notwithstanding these important efforts, the use of the available digital content of science museums and centres for both formal and informal education remains in fact very limited. The main reasons behind this observed discrepancy are: • The available content is widely spread among different repositories. Most

importantly, these repositories are not organized according to a standardized ontology and therefore they are not inter-operable.


45

• Learning objects and use of IEEE LOM tends to rely on the assumption that learning objects are only developed from scratch and does not include discussion on how to retrofit existing learning resources to fit a learning object format. For instance, (Polsani, 2003), (Hong et al., 2000), (Wiley, 2000) and many others take the perspective of having created, or expecting creation of new learning objects addressing issues of accessibility, reusability, and interoperability. This approach, while helpful to new collections for digital libraries, is less useful if there is a legacy of digital content to be catalogued.

• Existing metadata ontologies suffer from two fundamental issues: a) tagging an extensive collection is very expensive and time consuming (Fait and Hsi, 2005) and b) even when the collection is tagged, the metadata included are static and not context/user sensitive. Specifically, there are no specifications dealing with contextual or cultural aspects, which leads to confusion in which context or society a learning object can or should be used.

• The existing portals and repositories do not make adequate use of the latest presentation technologies to make the content attractive. This way, an extraordinary opportunity to add “edutainment” or “infotainment” quality to these repositories remains unexploited.

• Content is not consistently multi-lingual and whichever translations exist, they are not based on a unified translation key.

• Intellectual Property Rights (IPR) issues are not addressed in a systematic way and are therefore inducing significant operational risk on content providers. IPR issues also pose a major problem for any attempts on massive content aggregation.

• There is no sustainable business model in place that can assure a financially viable exploitation of the available digital resources. In that sense, financial sustainability is tightly connected to the level of content aggregation and that is due to the significant economies of scale that emerge.

4.3 Social tagging of digital educational resources

In the previous sections, the use of pre-defined, standardised educational metadata was discussed. In the present section, focus turns to the second option offered in the field of characterising Learning Objects with metadata, namely the use of metadata directly contributed by the end-user without making used of a standard, i.e. ‘social metadata’.

4.3.1 Introduction Web 2.0 applications and user behaviors are becoming the mainstream paradigm on the World Wide Web. Tim O’ Reilly describe Web 2.0 as a platform: “…delivering software as a continually-updated service that gets better the more people use it, consuming and remixing data from multiple sources, including individual users, while providing their own data and services in a form that allows remixing by others, creating network effects through an “architecture of participation”, and going beyond the page metaphor of web 1.0 to deliver rich user experiences” (O’ Reilly, 2005) Thus, Web 2.0 implements Tim-Berners Lee original vision for a “read-write web” where everyone could add, edit and comment web pages and resources (Berners-Lee, 1999)


46

This has led to an enormous increase of the digital resources available on the web today. As a result, both the discovery of new resources and the recall of known ones on the World Wide Web, become an increasingly complex problem. Within this context, the issue of characterizing digital resources tents to move from the formal description based on formal classification systems (for example, with metadata, such as the IEEE Learning Objects Metadata for educational resources) to a less formal user-based tagging (that is, adding keywords to digital objects). The act of adding keywords, also known as tags, to any type of digital resource by users (rather than resources’ authors) is referred to as Social Tagging. The term of social tagging has emerged for those applications that encourage groups of individuals to openly share their private descriptions (or tags) of digital resources with other users, either by using a collection of tags created by the individual for his/her personal use (refereed to as folksonomy) or by using a collective vocabulary (refereed to as collabulary) (Anderson, 2007).

4.3.2 Advantages and disadvantages of social tagging There are several anticipated benefits expected from the use of social tagging of digital educational resources, which are the following (Seldow, 2006; Bateman et al., 2007; Vuorikari, 2007; Hayman, 2007; Ullrich et al., 2008):

Individual users (practicing teachers and/or learners) are able to provide and use terms that are meaningful to them and create with this way a personal collection of tags. This can facilitate the searching and recalling of already used and known resources.

These tags pay attention to the individual users’ intents, reflecting their personal way of organizing and locating learning objects. This offers a unique and personalized way of classification which is delivered by users’ tags and not by a traditional classification standard.

By sharing these tags in an open manner with other users, groups of users with common vocabularies can act as a “human filter” for each other.

Identifying the most popular tags within a given community of users, a community based vocabulary can be produced eliminating redundant and irrelevant to the community description elements.

Tags generated by large communities bare the potential to discern contextual information from tags’ aggregation, facilitating an educational wisdom of the crowd.

Social tagging can enable the formation of social networks around educational tags. These networks can reflect the interests and expertise of users contributing to the tag development.

Analyzing user generated tags can enrich peer interaction and peer awareness around educational content.

Huge amounts of content can be annotated and thus enriched with meta-information without the considerable costs accompanying professional standards-based tagging.

Adding freeform tags or keywords is much quicker and user friendly than going through a restricted vocabulary defined within a relevant ontology.


47

On the other hand, common problems with social bookmarking and social tagging of digital educational resources are (Seldow, 2006; Bateman et al., 2007; Vuorikari, 2007; Hayman, 2007; Ullrich et al., 2008):

The use of tags with personal meaning from different users can create difficulties in the process of re-using digital educational resources.

The whole system is sensitive to meta-noise, meaning that misspelled, inappropriate or irrelevant metadata can be inserted, effectively reducing the quality and relevance of the tagging. Unclear tags due to spelling errors and synonyms tags can create difficulties in the process of searching and retrieving resources that has been characterized with these tags.

The lack of standards for the structure of tags (e.g. singular vs. plural, capitalization, etc.) can cause additional problems in searching and retrieving of appropriate digital educational resources.

Overall, tags are not connected to each other by a reference structure, which in formal systems is used to link related terms and narrower or broader terms. The semantics of the tags rest with the user, holding absolutely no semantic value for the system and are not connected to each other through any ontology. Therefore, they can not be used to extract correlations or any other kind of high level aggregation rules.

Tags that do not follow a structured ontology go against the model of Semantic Web since they offer small opportunity for automatic classification, summarization and manipulation of content.

Notwithstanding the aforementioned shortcomings, folksonomy techniques are becoming increasingly popular and are extensively used in important Web sites such as Flickr (http://www.flickr.com/), YouTube (http://www.youtube.com/) and del.icio.us (http://del.icio.us). These sites are among the most successful, proving thus the usefulness of the social tagging approach.

4.3.3 Social tagging in education and science museums/centres In the world of education, a first step in this area was the implementation of the MELT project (http://info.melt-project.eu), funded under the Educational Content Action Line of eContent Plus Programme in 2006. The MELT project began with the assumption that we also need metadata that more accurately reflect how learning resources are actually used in schools; and that teachers themselves should be given an opportunity and tools so that they can add their own metadata to resources they have used. The work of MELT focuses on the use of curriculum-based content that is provided from the involved Ministries of Education. In the world of science museums and centres, social tagging and folksonomies are appealing as they appear to fill gaps in current documentation practice and enable a desired level of social engagement (Hammond et al. 2005, Mathes 2004, Quintarelli 2005, Smith 2004). Tags are typically supplied by the museum/centre’s audience in an online environment. Tagging enables a departure from the authored voice of the museum or the science centre and, through the distributed contribution of many individuals, the construction of additional means of access to collections. For science museums and centres, acknowledging such alternative perspectives is a significant departure that reflects a growing understanding of these organisations’ place in a diverse community. Social tagging is one of a number of Internet-based technologies


48

science museums and centres have used to encourage public engagement with their educational content (Durbin 2004). Users are enabled to use personal narrative to define the significance of exhibition artefacts in projects such as Every Object Has a Story (Victoria & Albert Museum, Ultralab et al., 2005) and steve (www.steve.museum). These projects derive their user-centred approach from constructivist educational theory that emphasizes individual meaning-making as central to personally significant encounters online and in site (Hein, 1998; Samis, 1999). Social Tagging appeals to museums and science centres because it embodies a rather similar philosophy: tagging represents a dialog between the viewer and the exhibit, and the viewer and the museum (Bearman and Trant, 2005). Taken individually, a tag is a user’s assertion that an exhibit is about something (in some way, at some time). In the museum and in the science centre context, tagging offers a way for people to connect directly with physical laws and natural phenomena, to own them by labelling or naming them - one of the aspects of sensemaking (Golder and Huberman, 2006). Tagging lets users assert their own connections and associations between objects and phenomena in ways that reflect personal perspectives and interests. Tagging further enables re-discovery of activities previously performed; users’ tags record salient characteristics of personal interest and support subsequent searches (Chun et al., 2006). Tagging in a science museum and science centre context may differ from other implementations of social tagging (including the shared bookmarking services such as del.icio.us (http://del.icio.us) and Connotea (http://www.connotea.org/)) because these organizations have existing relationships with visitors that define a social and cultural context for the tagging activity. While these sites provide interesting models, they are not in the museum domain, and therefore do not address many relevant issues, concerning especially the relationship between publicly-contributed terminology and institutionally-authored documentation. They are also private initiatives without a public research agenda. For example museums and science centres invest a lot in programmes for teachers and students; tagging could become part of the museum’s tool-set for fostering and maintaining these relationships. Tagging could also facilitate teacher or student use of the exhibition and the relevant materials. Rather than being motivated by personal gain (Vander Wal, 2005), these kinds of users donate time and knowledge. These ideas are already partially introduced in the framework of small scale projects in museums and galleries: The Cleveland Museum of Art plays on such an altruistic motivation in the links to its online tagging tool that say “Help others find this object” (Cleveland Museum of Art and Hiwiller, 2005). At the Powerhouse Museum, in Sydney Australia, the Electronic Swatchbook project (Powerhouse Museum and Chan, 2005) appears to violate one of the roles of social Tagging - to provide immediate feedback - by collecting terms for museum review and future use. Perhaps the most significant challenge tagging poses to the science museum and science centre is that it is a visitor-initiated activity; the viewer of an exhibit supplies its significance. Tagging represents an investment in the museum’s collection by an individual. The visitor adds value for themselves, for the museum, and for other visitors by revealing different perspectives and contexts. These enhance, and possibly subvert, institutional perspective. In this framework the FP6 Technology Enhanced Learning project CONNECT (www.ea.gr/ep/connect) developed a web based tool that supports the organisation of both real and virtual field trips in the exhibition areas of science centres and museums across Europe, according to the needs of the school


49

curriculum and offered an authoring tool to the science teachers to enrich the visit with the necessary curriculum materials. Teachers were capable to add digital content to the exhibits offering personalised and tailored educational visit experiences to different groups of students according to their level and their learning profiles (Sotiriou et al 2007). Folksonomies constructed in social tagging environments are direct evidence of what people see as significant. Looking at the types of tags supplied by those outside museums and studying how they correlate (or do not) with data now made available by museums can provide insight into users’ perceptions, identify areas of disconnect, and help museums and science centres adapt to meet their missions and the current challenges.

4.3.4 Social tagging tools During the last years, a number of tools for facilitating social tagging of digital educational resources have been developed. Typically, these tools allow users to create their own tags and share digital resources, as well as, to browse the resources categorized by others. The main tools in this category are presented to the following sections Connotea Connotea7 is an open-source web–based reference management and social bookmarking tool for scientists created by Nature Publishing Group (Lund et al., 2005). Its main functionalities are: (i) online storage of bookmarks, (ii) simple, non – hierarchical structuring of bookmarks, (iii) access to the bookmarks list of different users, and (iv) automatic discovery of citation details for any article or book that it is added to the system.

Figure 12: Connotea Home Page

7 http://www.connotea.org/


50

CiteULike CiteULike8 (Emamy & Cameron, 2007) is a web–based tool for facilitating scientists, researchers and academics to store, organize, share and discover links to academic scientific and research papers. Similar to Connotea, CiteULike automatically extracts citation details and stores a link to the paper, along with a set of user–defined tags. By tagging scientific papers, users are building an explicit domain specific folksonomy that describes this paper in a potentially meaningful manner to the other members of the scientific community.

Figure 13: CiteULike Home Page

MELT MELT9 (http://info.met-project.eu) is a product of a Content Enrichment project supported by the European Commission’s eContentplus Programme, that builds on the existing technical architectures developed in the earlier CELEBRATE project. MELT aims to address the problem that metadata created by an expert indexer related to the learning resources type may not always reflect how a resource is really used in classrooms by experienced practicing teachers. MELT facilitates teachers who have used a digital educational resource to create their own tags by providing a social tagging system for this purpose.

8 http://www.citeulike.org/ 9 http://www.melt-project.eu/Melt-Portal/Index.iface


51

Figure 14: MELT Home Page

4.3.5 Comparison of social tagging tools In this paragraph, we are summarizing the main functionalities that a Social Tagging Tool should support in order to promote the advantages of social tagging and on the other hand to reduce the disadvantages of this process. Table 2 presents these functionalities in comparison with the functionalities that each of previous presented tools are supporting. The scope of the first seven functionalities, which are presented below, is to promote the advantages of social tagging. On the other hand Auto-Suggested and Guided Tagging contribute to the reduction of the social tagging disadvantages.

Table 2: Comparison of social tagging tools functionalities

Functionalities Connotea CiteULike MELT Submit Digital Educational Resources [Only URL] [Only URL] - Tagging Digital Educational Resources Comment Digital Educational Resources Rate Digital Educational Resources - - Search Digital Educational Resources Create Social Network Browse via Tag Clouds - - Auto-Suggested Tagging - - Guided Tagging - - -


52

Based on the table above, it should be mentioned that Cannotea and CiteULike are online reference management and social bookmarking services with limited functionalities, while MELT is using CELEBRATE’s learning object repository allowing users to add their own tags to the resources of this repository. However, none of these tools allow social tagging of digital educational resources of any format i.e. image, video, text, URLs and also none of the tools support the functionality of Guided Tagging.


53

5 The OSR content enhancement strategy: outcomes of Educational Design

In the context described in the previous parts of this document, the OSR project is setting out to explore the opportunities offered and challenges posed by the enrichment of science learning digital resources with standardised and social educational metadata, as part of an agenda focused on bridging formal and informal science learning contexts in order to make science learning opportunities more accessible and appealing to learners across the lifelong learning spectrum. A particular interest of the project lies in the potential offered by such an approach for the development of synergies between school science education and informal science learning experiences offered by science museums and centres. These intentions of the project have been ‘translated’ in the context of the Educational Design work into three tangible inputs into the next steps of the project. They comprise: a) the OSR standards-based educational metadata structure (‘OSR IEEE LOM Application Profile’, or simply ‘OSR Application Profile’); b) flexible schemes for the combination of digital resources into wider meaningful learning experiences appropriate for the user and context of use (‘OSR Educational Pathways’); and c) an account of options and opportunities offered for the exploitation of the potential of social tagging in the OSR project. In the following sections of this document, these three outcomes are presented in a fully functional, finalised, yet initial form (as of May 2010). It should be stressed that as the project will evolve, the project concepts and constructs dealt with in Educational Design may well mature further too, evolving into newer versions incorporating the experience and new insights gained. Thus, as the outcomes presented here are the results of intensive work in the first year of a three-year project, they should be seen as a starting point rather than an end.

5.1 OSR IEEE LOM Application Profile: The structure of the OSR educational metadata

In the light of the analysis of the input received as a response to the Educational Design Requirements Elicitation Questionnaire (Annex I) circulated within the consortium (see second section of this document on methodology), the structure of the OSR educational metadata was defined taking the form of the OSR Application Profile, as described in this section. More precisely, the OSR IEEE LOM Application Profile has been developed following the methodology, which is proposed by the IMS Global Learning Consortium and European Committee for Standardization (CEN/ISSS) and has been described in the previous sections. Moreover, the recommendations derived from the comparison of existing IEEE LOM Application profiles has been taken into account for the definition of approapriate vocabularies for the selected metadata elements of the OSR IEEE LOM Application Profile. Finally, the proposed OSR IEEE LOM Application Profile has been validated with the project partners based on the input


54

received as a response to the Educational Design Requirements Elicitation Questionnaire presented in Annex I.

5.1.1 Use and adaptations of the standard In the light of the review of the IEEE LOM standard and the existing IEEE LOM Application Profiles presented in previous sections of this document, the project is making the following points in connection with the OSR Application Profile:

• Subject Domain: The Subject Domain of the learning objects (ex. Physics, Mathematics, Foreign Languages, Natural Sciences, etc.) is a critical point for building an IEEE LOM Application Profile. The IEEE LOM Standard provides for that purpose the “9. Classification” category and more specifically the “9.1 Classification. Purpose” element. This element should take the value “discipline”, in order the subject domain to be specified. The “9.2.2 Classification. Taxon Path. Taxon” element can accommodate a specific taxonomy for the desired subject domain. All the above Application Profiles follow this practice by using appropriate vocabularies, which are customized to their needs, so as to describe certain subject domains. In the context of the OSR project, a suggested vocabulary covering the science curriculum characteristics is used. This vocabulary is presented in Annex II of this document

• Learning Resource Type: The Type of the learning resource (ex. activity, course, image, lecture, exam etc.) which is going to be characterized is a meaningful aspect for the search and retrieval in learning object repositories. For this scope, the IEEE LOM Standard provides the “5.2 Educational.Learning Resource Type” element which is related to a suggested vocabulary. From the previous analysis, we can conclude that many of the above Application Profiles don’t use this exactly this suggested vocabulary of the IEEE LOM Standard, but propose extensions of this vocabulary or propose their suggestions for new vocabularies according to their needs. In the context of the OSR project, an extended vocabulary is proposed for the description of the learning resource type. This vocabulary has been validated with the project partners and the results of this process are presented further below.

• Educational Objectives: Another important point in Application Profile building is the definition of the Educational Objectives that the use of the learning objects intends to achieve. The element “9.1 Classification. Purpose” should take the value “educational objective”, so that the educational objectives are specified. The “9.2.2 Classification. Taxon Path. Taxon” element provides the appropriate place where vocabularies for this scope can be accommodated. As we can notice, some of the Application Profiles presented further above, such as JORUM and RDN/LTSN, use a simple vocabulary for defining educational objectives. In the context of the OSR project, a vocabulary based on an adaptation of Bloom’s Taxonomy of educational objectives (and its revisions) is proposed, as explained in the following section. The element “9.2.2.1 Classification. Taxon Path. Taxon. ID” provides the place for the relevant vocabulary, while a free text description of the educational objective accompanying the vocabulary can be placed under “9.2.2.2 Classification. Taxon Path. Taxon. Entry”. The rationale followed with regard to the handling of educational objectives in the OSR Applicaton Profile is presented in the following section.


55

The OSR vocabulary for educational objectives Educational objectives constitute one of the relatively few fields in education that have been formalized to a considerable extent, and could therefore allow for the use of a predefined vocabulary in the context of the OSR Application Profile. Bloom’s Taxonomy of Educational Objectives (Bloom, 1956) and its subsequent revisions and extensions (Krathwohl, Bloom & Masia, 1973; Harrow, 1972; Simpson, 1972; Dave, 1975; Anderson, Krathwohl et al,2001; Fisher, 2005) provide indeed a widely known framework for the classification of educational objectives, which is accepted and used to a considerable extent by academics and practitioners in education worldwide. The OSR project conducted an analysis of the taxonomies produced by Bloom and his colleagues or later inspired by his work, and produced an adapted educational objectives vocabulary which serves the purposes of annotation of digital content from science centres/museums that could be used in formal and informal learning contexts. In this context, the typology proposed by Gammon (2003) was also considered, as it originates in the world of science museums and centres, focusing on the assessment of learning taking place in museum environments. The types of learning as categorised by Gammon are covered by the adopted vocabulary. It should be noted that the vocabulary is deliberately based on Bloom’s Taxonomy and its revisions and extensions rather than on Gammon (2003), as the former constitutes a comprehensive taxonomy that clearly subsumes the latter context-specific categorisation. In relation and comparison to Bloom’s Taxonomy, the OSR vocabulary for the classification of educational objectives has the following characteristics:

• It includes all three domains of learning as initially defined by Bloom, namely cognitive, affective and psychomotor. While most of the attention of both Bloom’s team and next generations of researchers was directed to the cognitive domain (Bloom, 1956; Anderson, Krathwohl et al, 2001; Fisher, 2005), a comprehensive approach of learning cannot ignore the affective (Krathwohl, Bloom & Masia, 1973) and psychomotor aspects of learning (Harrow, 1972; Simpson, 1972; Dave, 1975), least so in the context of science learning. In this context, physical performance (cf. the psychomotor domain) of certain kinds (e.g. precision of fine movements during an experiment) may well be of importance in various instances of science learning; what is more, the affective domain appears to be even more important in the context of science museums and centres, where science communication by definition aims to mobilise the public/the learners in terms of affective elements such as motivation, attitudes, emotions. The inclusion of affective and psychomotor objectives next to the traditionally focused-upon cognitive objectives is seen as a contribution of the project to a more comprehensive approach to science education and informal learning that users of OSR will be encouraged to recognise and materialise.

• Based on the experiences gained through the interaction of the project team

with users and teachers and science museum/centre staff (e.g. in the framework of user workshops and early trials), the number of vocabulary elements was reduced to four items per list, by merging certain elements of the original taxonomies. In addition, in the cognitive domain experience with users showed that it is adequate, for the purposes of addressing the target


56

groups of the project, to use the ‘knowledge’ and ‘cognitive process’ dimension of the Revised Bloom Taxonomy, but not the verbs/statements which combine these two dimensions at a deeper level of analysis (Fisher, 2005). This practice of simplification is not unknown in the domains of educational research and practice that use Bloom’s Taxonomy with a greater emphasis on providing the non-expert with a simple, easy-to-grasp-and-overview system of terms for the categorisation of learning objectives. Nevertheless, the OSR vocabulary retains a clear relation to the original full-length lists, as presented further below, so that its items can be directly linked to the elements of the Taxonomy, thus allowing for interoperability with other content organisation systems that use the Taxonomy or other vocabularies based on it.

• Minor changes were made on the language used in Bloom’s Taxonomy and its

revisions and extentions, so as to make it more transparent to the target groups of OSR. The verbs of the Taxonomy (e.g. ‘remembering’) are reproduced in the OSR vocabulary in infinitive form (e.g. ‘to remember’) in order to connotate more directly a description of an education objective. In addition, each short vocabulary element is accompanied by a longer description, based on the statements of the Taxonomy but also made simpler, synthesising elements of various practical presentation and analyses available.

• It is acknowledged that while the original Taxonomy and the relevant OSR

vocabulary provide tools for the classification of educational objectives, they do not provide full descriptions of the objectives themselves: knowing the vocabulary classifying the educational objective attached to a learning object, one knows about the nature and type of this objective, but does not know the educational objective per se. Therefore, the analysis conducted foresees the provision of free-text fields next to the selections of vocabulary where users will be able to define the educational objectives of the digital learning resources they annotate.

The produced vocabulary was tried in a preliminary form in user requirement elicitation workshops (WP3) and reformed into the present version based on the experiences gained in those workshops. It should be noted that the question of the vocabulary for the educational objectives will be revisited in the next project stages in the light of the feedback from the field during the trials and validation (WP6), and particularly in connection with the aspects of the trials refering to social tagging. In the following tables, the OSR vocabulary for the classification of educational objectives is presented.


57

Table 3: The OSR cognitive educational objectives (processes) vocabulary in relation to the Taxonomy of Educational Objectives

Cognitive objectives: processes

(on knowledge, comprehension, critical thinking) Note: The following classification of cognitive educational objectives should be read as a ‘scale’: a

gradual move towards higher-order thinking (from simple remembering through to transforming information and creating new ideas). Each level builds on and subsumes the previous levels.

Revised Bloom Taxonomy10 (short)

Revised Bloom Taxonomy (description)

OSR (short)

OSR (description)

To remember To help the learner recognize or recall information To remember

To help the learner recognize or recall information

To understand To help the learner organize and arrange information mentally

To understand To help the learner organize and arrange information mentally

To apply To help the learner apply information to reach an answer To apply

To help the learner apply information to reach an answer

To analyse To help the learner think critically on causes and results

To evaluate To help the learner make judgments and decisions based on reflection and assessment

To create To help the learner think originally, predict, and create new ideas

To think critically and creatively

To help the learner think on causes, predict, make judgments, create new ideas

Table 4: The OSR cognitive educational objectives (knowledge) vocabulary in relation to the Taxonomy of Educational Objectives

Cognitive objectives: Knowledge

(on the type of knowledge) Revised Bloom

Taxonomy11 / OSR (short)

Revised Bloom Taxonomy / OSR (description)

Factual knowledge Knowledge of basic elements, e.g. terminology, symbols, specific details, etc Conceptual knowledge

Knowledge of interrelationships among the basic elements within a larger structure, e.g. classifications, principles, theories, etc

Procedural knowledge

Knowledge on how-to-do, methods, techniques, subject-specific skills and algorithms, etc

Meta-cognitive knowledge

Knowledge and awareness of cognition, e.g. of learning strategies, cognitive tasks, one’s own strengths, weaknesses and knowledge level, etc

10 Anderson, Krathwohl et al (2001) 11 Anderson, Krathwohl et al (2001)


58

Table 5: The OSR affective educational objectives vocabulary in relation to the Taxonomy of Educational Objectives

Affective objectives (on interests, attitudes, opinions, appreciations, values, emotions)

Note: The following classification of affective educational objectives should be read as a ‘scale’: a gradual move towards higher-order processes (from simple reception of stimuli through to a values-

based behaviour). Each level builds on and subsumes the previous levels.

Bloom Taxonomy12 (short)

Bloom Taxonomy (description)

OSR (short)

OSR (description)

To receive To help the learner focus and pay attention to stimuli (passively)

To pay attention To help the learner focus and pay attention to stimuli, passively

To respond

To help the learner react to stimuli and actively participate in the learning process

To respond and participate

To help the learner react to stimuli and actively participate in the learning process

To value

To help the learner attach values to stimuli and commit themselves to the learning process

To recognise values

To help the learner attach values to stimuli

To organize a value system

To help the learner build an internally consistent system of values, a philosophy of life

To internalize a value system

To help the learner develop behaviour characteristics that are consistently formed by a system of values

To form and follow a system of values

To help the learner build and behave according to a consistent system of values

12 Krathwohl, Bloom & Masia (1973)


59

Table 6: The OSR psychomotor educational objectives vocabulary in relation to the Taxonomy of Educational Objectives

Psychomotor objectives

(on motor skill development and performance) Note: The following classification of psychomotor educational objectives should be read as a ‘scale’: a gradual move from the simplest behaviour to the most complex. Each level builds on and subsumes the

previous levels. Simpson13 Taxonomy (short)

Simpson Taxonomy (description)

Dave14 Taxonomy (short)

Dave Taxonomy & OSR (description)

OSR (short)

Perception To help the learner use sensory cues to guide motor activity

Set

To help the learner get ready to act according to their mental, physical, and emotional dispositions (mindsets)

- - -

Guided Response

To help the learner achieve the early stages in learning a complex skill through imitation, trial and error

Imitation

To help the learner observe and pattern behaviour after someone else (performance may be of low quality); copy action of another

To imitate and try

Mechanism

To help the learner reach the intermediate stage in learning a complex skill, through habitual responses and confident movements

Manipu-lation

To help the learner perform certain actions by following instructions and practicing; reproduce activity from instruction or memory

To perform confidently following instructions

Complex Overt Response

To help the learner reach proficiency and skilful performance of motor acts which involve complex movement patterns

Precision

To help the learner refine performance and become more exact, with few errors; execute skill reliably, independent of help

To perform independently, skilfully and precisely

Adaptation

Skills are well developed and the individual can modify movement patterns to fit special requirements

Articu-lation

To help the learner coordinate a series of actions, achieving harmony and internal consistency; adapt and integrate expertise to satisfy a non-standard objective

Origination

Creating new movement patterns to fit a particular situation or specific problem.

Naturali-sation

To help the learner achieve high level performance and become natural, without needing to think much about it; automated, unconscious mastery of activity and related skills at strategic level

To adapt and perform creatively

13 Simpson (1972) 14 Dave (1975)


60

5.1.2 Presentation of the OSR IEEE LOM Application Profile As described above, the OSR Application Profile is based on the IEEE LOM (2002) specifications, being the result of relevant selection of elements and adaptation in the proposed vocabularies, so that the Application Profile best reflects the needs for the annotation of science learning digital content available in or through science museums and centres. The purpose of this metadata structure is to facilitate organizing, searching and retrieving digital science museum/centre learning resources. The metadata elements represent categories to which each digital learning object may be linked. Metadata information may be added to a digital object either by choosing between entries in pre-defined vocabulary lists, or by providing open-ended text. It should be noted that in this context anything can be considered as a digital learning object, from a single file (e.g. a photo, a piece of text, etc.) to a large coordinated system of files, digital exhibits, web pages, etc. The metadata elements and their realisations into specific vocabulary lists or open text fields are presented in Table 7 below. For some of the metadata elements, notes or comments are added which point to possible options for further work or revisions in the subsequent stages of the project. These notes or comments reflect the current state (May 2010) of the relevant ongoing dialogue within the project consortium.

Table 7: The OSR Application Profile Metadata Element Vocabulary Notes and comments 1. The Title of the learning resource

short open-text description The title will be in the local language / the language of the content (Element 2). However, it may be advisable to implement an additional sub-element (1a) that will allow entering the title in English too (if English is not the original language), to facilitate possible exploitation of the content across linguistic barriers.

2. The human Language used within the learning resource to communicate with the intended user

choice from a pre-defined vocabulary list:

• List of languages

It will be necessary to allow for multiple choices of languages when applicable, as there are multilingual digital objects.

3. The general Description of the content of the learning resource

short open-text description: • few sentences reflecting

the content and purpose of the resource

The description will be in the local language / the language of the content. However, it may be advisable to implement an additional sub-element (3a) that will allow entering a short description in English too (if English is not the original language), to facilitate possible exploitation of the content across linguistic barriers.

(Table continued)


61

Metadata Element Vocabulary Notes and comments 4. The Educational Objectives of the learning resource

Note: In the present revised version of the Application Profile, the Metadata Element of the Educational Objectives has been analysed into four sub-elements with corresponding vocabularies.

4.1.a The Educational Objectives of the learning resource: Cognitive Domain (processes)


• to remember (to help the learner recognize or recall information)

• to understand (to help the learner organize and arrange information mentally)

• to apply (to help the learner apply information to reach an answer)

• to think critically and creatively (to help the learner think on causes, predict, make judgments, create new ideas)

AND (optionally) short open-text description:

• one sentence concisely describing the cognitive educational objective served by the resource (expanding on the vocabulary item selected)

In the user interfaces, it will be useful to insert the following explanatory captions below the title of the element: The main intended cognitive process(es) in the learner as they use this resource & Note: The classification of cognitive processes should be read as a ‘scale’ representing a gradual move from simple remembering towards higher-order thinking. Each level builds on and subsumes the previous levels. It will be necessary to allow for multiple choices of educational objectives in the cognitive domain (processes), as one resource may serve more than one objective of this type. To avoid excessive information load, the user should be encouraged to insert the most important objective(s) only, as well as being reminded that they should concentrate on their own perception of the served objective(s), i.e. what and how they expect/advise users to learn using this resource.

(Table continued)


62

Metadata Element Vocabulary Notes and comments 4.1.b The Educational Objectives of the learning resource: Cognitive Domain (knowledge)


• factual knowledge (knowledge of basic elements, e.g. terminology, symbols, specific details, etc)

• conceptual knowledge (knowledge of interrelationships among the basic elements within a larger structure, e.g. classifications, principles, theories, etc)

• procedural knowledge (knowledge on how-to-do, methods, techniques, subject-specific skills and algorithms, etc)

• meta-cognitive knowledge (knowledge and awareness of cognition, e.g. of learning strategies, cognitive tasks, one’s own strengths, weaknesses and knowledge level, etc)

• AND (optionally) short open-text description:

• one sentence concisely describing the kind of knowledge to be learned by using the resource (expanding on the vocabulary item selected)

In the user interfaces, it will be useful to insert the following explanatory caption below the title of the element: The type of knowledge the learner should gain through the use of this resource It will be necessary to allow for multiple choices of educational objectives in the cognitive domain (knowledge), as one resource may serve more than one objective of this type. To avoid excessive information load, the user should be encouraged to insert the most important objective(s) only, as well as being reminded that they should concentrate on their own perception of the served objective(s), i.e. what and how they expect/advise users to learn using this resource.

(Table continued)


63

Metadata Element Vocabulary Notes and comments 4.2 The Educational Objectives of the learning resource: Affective Domain


• to pay attention (to help the learner focus and pay attention to stimuli, passively)

• to respond and participate (to help the learner react to stimuli and actively participate in the learning process)

• to recognise values (to help the learner attach certain values to stimuli)

• to form and follow a system of values (to help the learner build, and behave according to, a consistent system of values)

AND (optionally) short open-text description:

• one sentence concisely describing the affective educational objective served by the resource (expanding on the vocabulary item selected)

In the user interfaces, it will be useful to insert the following explanatory caption below the title of the element: The main interests, attitudes, opinions, values the learner should develop through the use of this resource & Note: The classification of affective educational objectives should be read as a ‘scale’ representing a gradual move towards higher-order processes (from simple reception of stimuli through to values-based behaviour). Each level builds on and subsumes the previous levels. It will be necessary to allow for multiple choices of educational objectives in the affective domain, as one resource may serve more than one objective of this type. To avoid excessive information load, the user should be encouraged to insert the most important objective(s) only, as well as being reminded that they should concentrate on their own perception of the served objective(s), i.e. what and how they expect/advise users to learn using this resource.

(Table continued)


64

Metadata Element Vocabulary Notes and comments 4.3 The Educational Objectives of the learning resource: Psychomotor Domain


• to imitate and try • to perform confidently

following instructions • to perform independently,

skilfully and precisely • to adapt and perform

creatively AND (optionally) short open-text description:

• one sentence concisely describing the psychomotor educational objective served by the resource (expanding on the vocabulary item selected)

In the user interfaces, it will be useful to insert the following explanatory caption below the title of the element: The movement and coordination skills the learner should develop through the use of this resource & Note: The classification of psychomotor educational objectives should be read as a ‘scale’ representing a gradual move from the simplest behaviour to the most complex. Each level builds on and subsumes the previous levels. It will be necessary to allow for multiple choices of educational objectives in the psychomotor domain, as one resource may serve more than one objective of this type. To avoid excessive information load, the user should be encouraged to insert the most important objective(s) only, as well as being reminded that they should concentrate on their own perception of the served objective(s), i.e. what and how they expect/advise users to learn using this resource.

5. Keywords characterizing the topic of the learning resource

short open-text description: • a limited number of

words/short phases reflecting the topic

6. The underlying organizational Structure of the learning resource


• atomic (a resource that is indivisible in this context)

• collection (a set of resources with no specified relationships between them)

• networked (a set of objects with relationships that are unspecified)

• hierarchical (a set of objects whose relationships can be represented by a tree structure)

• linear (a set of objects that are fully ordered, i.e. connected with ‘previous’ and ‘next’ relationships)

This information, although useful, may not be necessary, or it may be too demanding for some users. In subsequent project phases it will be examined whether this element should remain in the Application Profile, and whether it is adequately defined and commonly understood within the consortium.

(Table continued)


65

Metadata Element Vocabulary Notes and comments 7. The Aggregation Level of the learning resource


• educational content (any learning resource, from a single file to a complex exhibit or a whole exhibition)

• educational pathway (a plan for using a meaningful combination of various instances of educational content)

8. The Author’s Name of the learning resource

short open-text description: • institution/person that has

created/authored/produced the resource

9. The Publisher’s Name of the learning resource

short open-text description: • institution/person that is

providing/distributing the resource, e.g. the science museum/centre, the user who generated it

10. The Metadata Creator’s Name of the learning resource metadata


inserted the metadata

11. The Metadata Validator’s Name of the learning resource metadata


confirmed the inserted metadata

12. The human Language of the metadata


• list of languages

It will be necessary to allow for multiple choices of languages when applicable, as there may be multilingual metadata sets.

(Table continued)


66

Metadata Element Vocabulary Notes and comments 13. The technical Format of the learning resource file


• text/plain • text/html • text/css • text/xml • text/rtf • application/pdf • application/zip • application/word

processing • application/xml • application/slides

presentation • application/spreadsheet • application/database • application/asp • application/java • application/flash • image/jpeg • image/gif • image/tiff • image/png • audio/avi • audio/mp3 • video/mpeg • video/quicktime • video/mov • 3D/… • bibliographic

records/UNIMARC

It will be necessary to allow for multiple choices of format when applicable, as there may be multi-format objects.

14. The Size of the learning resource file in KBs


• up to 250KB • from 250KB to 500KB • from 500KB to 1MB • from 1MB to 5MB • more than 5MB • not intended for download

15. The Technical Requirements to use the learning resource

short open-text description: • operating system, web

browser, bandwidth, etc.

(Table continued)


67

Metadata Element Vocabulary Notes and comments 16. The Learning Resource Type


• image of object (still picture)

• video • diagram/graph/chart/plot • table • index • animation • simulation • experiment • narrative/explanatory text • exercise/problem • self assessment test • questionnaire • physical visit plan • virtual visit plan • lesson plan • project plan • lecture • scientific article/text • other article/text • whole website / web

collection • game • other software application • collection of links

It will be necessary to allow for multiple choices of learning resource type when applicable. In subsequent stages of the project it may be useful to group the elements of this list according to a classification (e.g. by distinguishing pedagogical from technological concepts).

17. The Interactivity Type


• active learning / learning by doing: user input/action/decision required (e.g. simulations, experiments, exercises/problems, tests, questionnaires,, visit/lesson plans)

• expositive/passive learning: user absorbing the presented information (e.g. video, audio, pictures, graphs, texts, hypertexts, lectures)

• mixed: a combination of active and expositive learning (e.g. hypertext with embedded simulation applet)

(Table continued)


68

Metadata Element Vocabulary Notes and comments 18. The Interactivity Level


• low (e.g. observation/reading, or push-button, or select-link only)

• medium (e.g. limited data entry by the user)

• high (e.g. some user controls affecting object behaviour)

• very high (e.g. object behaviour totally shaped by the user)

19. The Intended End-User Role


• teacher • student • other learner / visitor • parents / families • science museum educator • other science

communication professional

• occasional information collector (e.g. journalist)

It will be necessary to allow for multiple choices of intended end-user roles when applicable.

20. The Context choice from a pre-defined vocabulary list:

• school-connected15 • in the science

museum/centre (physical visit)

• on the web (virtual visit)

It will be necessary to allow for multiple choices of context when applicable.

21. The Typical Age Range of the intended user of the learning resource


• less than 6 • 6-9 • 9-12 • 12-15 • 15-18 • 18-25 • 25+ • all ages

It will be necessary to allow for multiple choices of typical age range when applicable.

22. The Difficulty of the learning resource


• very easy • easy • medium • difficult • very difficult

The level of difficulty is subjectively understood, as defined/felt by the creator/user of the resource.

(Table continued) 15 Combined with one of the following two categories.


69

Metadata Element Vocabulary Notes and comments 23. The approximate/typical Learning Time to work with the learning resource

• up to 10 minutes • up to 1 hour • up to 2 hours • more

This information may be too difficult to define in some cases of informal learning in the science museum/centre. It might be advisable to provide an open-text option, too.

24. Whether use of this learning resource requires Payment


• payment required • use is free of charge

25. Whether Copyright or Other Restrictions apply to the use of this learning resource

short open-text description: • description of restrictions,

conditions to use, etc.

26. The Classification within a science learning classification system


• see science education vocabulary in Annex II

It will be necessary to allow for multiple choices of classification categories when applicable.

5.2 The OSR Educational Pathways

Learning science (or learning about science) is not the same experience and does not carry to same meaning for everyone. In addition to the varying perceptions of science learning, its nature, objectives and workings, the diversity of science learning instances is also attributable to the variety of personal and institutional circumstances in which it may occur. Thus, the characterisation of science learning objects alone cannot generate adequate momentum for effective and sustainable exploitation of the rich content of digital repositories, unless this content can be accessed by the intended users in purpose-appropriate, meaningful ways. This challenge is addressed by the OSR project through the employment of the concept of Educational Pathways.

5.2.1 The concept of Educational Pathway The concept of Educational Pathway in OSR reflects the priority given by the project to responding to the needs of the diverse communities of potential users of the OSR services. Thus, an Educational Pathway in the OSR project describes the organization and coordination of various individual science learning resources into a coherent plan so that they become a meaningful science learning activity for a specific user group (e.g. teachers, students, other museum visitors, etc.) in a specific context of use. Further, Educational Pathways directly serve the priority assigned by the project to the integration of resources scattered in various science museums/centres into the same learning experience rather than the mere selection of resources from a single museum or science centre. It should be kept in mind that an OSR Educational Pathway may include only the use of digital content at a distance, without physically visiting the science museum or


70

centre (‘virtual visit’), or a combination of using digital content (at a distance or onsite) with a physical visit to the science museum or centre (‘physical visit’)16. In the OSR approach, a Pathway is understood as a dynamic rather than static conceptual tool. In the envisaged optimal function of the OSR community, creators of Pathways may revisit, revise and continually develop their Pathways, or even use Pathways created by others as a basis for creating their own new versions, in a process reflecting social learning as a course of personal and communal gradual development in the learning community.

5.2.2 OSR user roles and use contexts: Defining the dimensions of digital-resource-based science learning

Central to the definition of the OSR Educational Pathways is the definition of the user roles and use contexts anticipated. In other words, Pathways represent various combinations of users and contexts, with quite varying characteristics among them, sharing however an interest in using digital resources available in science museums and centres for science learning purposes – formally or informally. The main OSR stakeholders are defined according to their roles as users of the OSR service as follows:

• Teachers: school teachers wishing to integrate the use of such resources in their teaching.

• Students: school students who may use such resources either as part of their curricular learning, or in out-of-school learning (e.g. in free time or with family)

• Other learners / visitors (‘lifelong learners’): people of all ages who may use such resources out of personal interest or by chance, either deliberately to learn science/about science, or simply learning informally as a by-product of leisure activities; a distinguishable part of this group may be parents / families interested in enjoyable science learning experiences.

• Science museum educators or science communication professionals: Staff who prepare science learning or awareness raising experiences for the visitors/users of their institutions (science museums and centres). An additional subgroup here might also be other professionals too related to science communication, including journalists who may search for content relevant to the promotion of informal science learning.

Correspondingly, then, the contexts of use of the OSR service may be organised into the following three categories:

• In the school (combined with one of the following two categories) • In the science museum/centre (physical visit) • On the web (virtual visit),

in the combinations presented in Table 8.

16 Physical visits without an element of use of digital content use are beyond the scope of the OSR project.


71

Table 8: Contexts of use of the OSR service

In the science museum/centre

(physical visit) On the web

(virtual visit) In connection

with the school In connection

with the school In no connection with the school

In no connection with the school

In these contexts, individuals and groups may get involved in the use of digital content either in ways pre-designed by someone (e.g. a teacher, or a museum educator), or employing their own creative ways of exploring and interacting with the digital content. The OSR Educational Pathways can then be seen as instances located in a system of possible combinations of use contexts, user roles, and varying levels of user independence (Table 9).

Table 9: Contexts of use, user roles, and user independence

In connection with the school In no connection with the school

In the science museum/centre (physical visit)

On the web (virtual visit)

In the science museum/centre (physical visit)

On the web (virtual visit)

Teachers usually pre-structured

(or exploratory)

usually pre-structured

(or exploratory)

As independent lifelong learners:

usually exploratory (or pre-structured)



Students usually pre-structured

(or exploratory)

usually pre-structured

(or exploratory)





(Other) lifelong learners - - usually exploratory

(or pre-structured) usually exploratory (or pre-structured)

Science museum educators or science communication professionals

[structuring activities for

others]


others]


others]


others]

Such a system allows for possible dimensions of digital-resource-based science learning such as the following:

• Use of museum and science centre digital resources in school science education

o Teacher-guided (top-down) o Student-driven (bottom-up)

• Use of museum and science centre digital resources in non-formal17 science learning

17 The terms ‘formal’, ‘non-formal’ and ‘informal’ learning are used on the basis of existing EU definitions [e.g. A Memorandum on Lifelong Learning. Brussels, 30.10.2000. SEC(2000) 1832], with the following meanings: Formal learning: Learning typically provided by an education or training institution, structured (in terms of learning objectives, learning time or learning support) and leading to certification. Formal learning is intentional from the learner’s perspective. Non-formal learning:


72

o Curator-guided (top-down) o Visitor-driven (bottom-up)

• Use of museum and science centre digital resources in informal science learning

o Curator-facilitated (top-down) o Visitor-driven (bottom-up).

In this context, a distinction between pre-structured and open18 pathways appears to be useful. An OSR Educational Pathway is defined as pre-structured when it provides a rigid pre-defined ‘route’ through a set of science learning resources (mainly relevant to more formal learning contexts, e.g. the case of school science education, with specific curriculum references and teaching processes). On the other hand, an OSR Educational Pathway is defined as open when it is more flexible and informal in its approach, allowing for considerable unbound user decisions, initiative and creativity in the ways the user will explore and exploit the science learning resources (as in the case of an adult independent visitor or a family, or even a teacher who has decided to involve her/his students in an open-ended exploration of the resources).

5.2.3 The Educational Pathway Patterns Going one step closer to practical implementation, the OSR Educational Pathway Patterns are the templates offered by the project for designing, expressing and representing Educational Pathways for a certain user group and type of visit. Two main types of Patterns seem to be capable of describing the various possible pathways: a Pre-Structured and an Open Educational Pathway Pattern, corresponding to the pre-structured and open educational pathways as described in the previous section. The proposed two Educational Pathway Patterns correspond to the various user groups as presented in Table 10.

Learning that is not provided by an education or training institution and typically does not lead to certification. It is, however, structured (in terms of learning objectives, learning time or learning support). Non-formal learning is intentional from the learner’s perspective. Informal learning: Learning resulting from daily life activities related to work, family or leisure. It is not structured (in terms of learning objectives, learning time or learning support) and typically does not lead to certification. Informal learning may be intentional but in most cases it is non-intentional (or ‘incidental’/ random). NB: The distinction between non-formal and informal learning is not usual in the field of science learning, and in particular providers such as science museums or centres may not feel comfortable with the distinction. In the present context, when no distinction is made between ‘non-formal’ and ‘informal’, the term ‘informal’ refers to the usual kinds of provision offered by science museums or centres. 18 The term ‘semi-structured’ has also been proposed as an alternative to the term ‘open’, as it is felt that ‘open’ may imply an approach that is too un-specific to OSR (e.g. implying an experience like using Google on the web). The term ‘open’ is retained at this stage, but will be revised if experience from the next project phases points to such a need.


73

Table 10: Educational Pathway Patterns and user groups

School community (teachers and students)

Pre-Structured Educational Pathway Pattern (potentially also Open)

Prepared by: • Teachers • Science museum

educators etc.

Enriched with social metadata by: • Teachers • Students

‘Lifelong learners’

Open Educational Pathway Pattern (potentially also Pre-Structured)

Prepared by: • Science museum

educators etc. • Users / lifelong

learners

Enriched with social metadata by: • Learners

5.2.3.1 Structure of the OSR Educational Pathway Patterns In many cases, learning experiences should be ideally embedded in a context which provides the means for the preparation of the learner for the learning experience before it takes place, as well as for facilitating the retention and future exploitation of the outcomes of the learning experience for a longer time after it has taken place. This is a fundamental principle in formal education, but can also be seen as a useful dimension (even if not that prescriptive) in informal learning environments. For this reason, the OSR Educational Pathway Patterns propose the organization of the science learning experience in three steps:

i) Pre-visit19: activities preparing for the interaction with the digital learning science resources

ii) Visit: activities involving interaction with the digital science learning resources in or outside the science museum/centre

iii) Post-visit: activities rounding up and concluding the learning experience, after the interaction with the digital science learning resources.

From these, the Visit phase is the core of the learning experience and indispensable in any Pattern. The Pre-visit and Post-visit phases are absolutely essential for the realization of effective connections between school science education with learning activities involving work with science museum/centre content; however these ‘auxiliary’ preparatory and follow-up phases may well or may not be relevant to and desirable for open visits by any lifelong learner (e.g. if the designer of an informal learning experience feels that the adoption of the three-phase scheme implies a linearity of sequential nature that does not correspond to the intended experience). Indeed, the degree of freedom or prescription in the design of a pathway has proven to be the most debated aspect of the OSR approach in the consortium, which brings together two considerably separate ‘worlds’: those of formal school education and informal learning in science museum and centres.

19 The term ‘visit’ is used here metaphorically, and does not necessarily imply a physical visit to a science museum or centre. It is used in a technical sense in this document, to indicate processes before, during, and after interaction with the digital learning resource(s) in question. If felt necessary, in the next project steps it may be replaced by other more accurate or user-friendly terminology (e.g. ‘pre-experience’, ‘experience’, ‘post-experience’; or ‘engage’, ‘interact’, ‘find out more’; or even topic specifically, such as for example ‘travelling with a light beam’, ‘virtual visit to European museums’, ‘more resources and ideas’; etc).


74

Thus, although each pattern should include sections corresponding to these three phases, in the case of an open pathway pattern the pre-visit and post-visit phases should be seen as possible but not obligatory. In addition to the three phases, there is an introductory section outlining the identity of the Educational Pathway and providing guidance for any preparations necessary before the launch of the learning activity. Each section consists of a number of fields, for each one of which a description and/or guideline is provided.

5.2.3.2 The Educational Pathway Patterns developed From the various possible Educational Pathway Patterns that the project could develop, the most complex are those describing structured visits bridging formal and informal science learning through a school ‘visit’ (physical or virtual). Open ‘visits’ by independent informal learners, on the other hand, can be seen as simpler, little pre-defined experiences. Structured visits of non-school users that may be offered by some science museums or centres fall somewhere between the two ends of the ‘complexity and structure’ spectrum, their exact position depending on the degree of formality applied to the design of the visit by the science museum/centre. Therefore, the present document proposes two structures as tools for use and experimentation at this stage of the project20:

• The OSR Educational Pathway Pattern for a Pre-Structured Visit by the School Community

• The OSR Educational Pathway Pattern for an Open Visit by Lifelong Learners.

These two examples can guide the formulation of other Educational Pathways in the following project stages, based on the experience gained through the use of these initial tools.

5.2.4 OSR Educational Pathway Pattern for a Pre-Structured Visit by the School Community

5.2.4.1 Introductory note From the various possibilities of interaction with the OSR resources, structured visits of the school community correspond to the most complex, detailed and pre-defined Educational Pathways, reflecting the mapping sought between formal and informal learning practices. In the case of an Educational Pathway for a Pre-Structured Visit by the School Community, the teacher or the museum educator selects school science subject matter (e.g. complex physical phenomena typically causing difficulties to students) to present it through student-centred and student-friendly multidisciplinary educational activities involving the use of digital science learning resources available 20 An alternative terminology that has been proposed as more user-friendly is ‘pathways for educators/mediators’ (addressing those who will use the OSR content to ‘educate’ or ‘inform’ or ‘involve’ others) and ‘surf and learn’ (addressing those independent users who use the OSR content for their own benefit/pleasure). The need for and usefulness of this and other alternative terminology will be examined in the next project phases.


75

through the OSR Portal. The Learning Pathway should represent a learning experience connecting work in the classroom or school lab with virtual or physical visits to the OSR science museums/centres. The integration of resources scattered in various science museums/centres into meaningful learning experiences is a priority (rather than selecting resources from a single museum or science centre).

5.2.4.2 The underlying pedagogical approach for the structured visit For the three steps of the learning process (Pre-visit, Visit, Post-Visit), the model of Inquiry-Based Learning is chosen as the guiding principle for structuring the activities foreseen by the structured Educational Pathways. Inquiry-Based Learning is currently the most influential approach to science learning, and particularly so in the field of school science education. According to it, learning should be based around learners’ questions, as they work together to solve problems rather than receiving direct instructions from the teacher. The teacher should function as a facilitator helping students in the process of discovering knowledge themselves. In the science context in particular, learners use their background knowledge (of principles, concepts, theories) together with their science process skills to construct new explanations which allow them to understand the natural world; and learners are likely to begin to understand the natural world if they work directly with natural phenomena, using their senses to observe and using instruments to extend the power of their senses. This approach to science learning is part of a greater world of constructivist models of learning, which see learning as the result of ongoing changes in our mental frameworks as we attempt to make meaning out of our experiences. In classrooms where students are encouraged to make meaning, they are generally involved in “developing and restructuring [their] knowledge schemes through experiences with phenomena, through exploratory talk and teacher intervention” (Driver, 1989). In practical terms, it is proposed that teacher and learner activity be described in the Educational Pathways as an iterative process consisting of the following five phases: Teaching Phase 1: Question Eliciting Activities

• Provoke curiosity: The teacher tries to attract the students’ attention by presenting/showing to them appropriate material.

• Define questions from current knowledge: Students are engaged by scientifically oriented questions imposed by the teacher.

Teaching Phase 2: Active Investigation

• Propose preliminary explanations or hypotheses: Students propose some possible explanations to the questions that emerged from the previous activity. The teacher identifies possible misconceptions.

• Plan and conduct simple investigation: Students give priority to evidence, which allows them to develop explanations that address scientifically oriented questions. The teacher facilitates the process.

Teaching Phase 3: Creation

• Gather evidence from observation: Teacher divides students in groups. Each group of students formulates and evaluates explanations from evidence to address scientifically oriented questions.


76

Teaching Phase 4: Discussion

• Explanation based on evidence: The teacher gives the correct explanation for the specific research topic.

• Consider other explanations: Each group of students evaluates its explanations in light of alternative explanations, particularly those reflecting scientific understanding.

Teaching Phase 5: Reflection

• Communicate explanation: Each group of students produces a report with its findings, presents and justifies its proposed explanations to other groups and the teacher.

The above model is proposed as a guide of appropriate teaching practice built around the observation of objects or phenomena in the natural world – in this case physically or virtually, directly or indirectly, in the science museum/centre. Apparently, the Educational Pathway Pattern is flexible and open to other educational approaches, too, if considered more appropriate in certain circumstances. However, in any case it is advisable to retain the organization of the activities in a three-step scheme (before, during, after the ‘visit’).


77

The Educational Pathway Pattern for a Pre-Structured Visit by the School Community A) Introductory section and preparatory phase The following basic information about the intended learning experience is to be defined at the outset. This information should allow the teacher to assess the relevance of the resource to his/her teaching needs and particular circumstances, and provide him with guidance for the preparation of the learning experience. Note that most of this information can be directly linked to specific elements of the OSR Application Profile presented in section 5.1. The formalisation proposed there for certain elements is to be applied accordingly in this introductory section too. Title: Give a title that helps easily recognize the content focus and purpose of the Educational Pathway. Short description: A description of no more than 30 words outlining the scope of the Educational pathway, descriptive enough to help the user in the first instance to estimate its possible relevance to her/his interests. Keywords: A limited number of words/short phases reflecting the topic and scope. Target audience: The intended end user: teacher with students, teacher, students, other… Age range: Up to 6, 6-9, 9-12, 12-15, 15-18… Context: The places that the Educational Pathway involves: school, science museum/centre, independently on the web. Time required: The approximate time typically needed to realize the Educational pathway. This could be distinguished into the amount of time required for school-based work and science museum/centre-based work. Technical requirements: Description of any special technologies, infrastructure and/or technical expertise required for the realization of the Educational Pathway. Author’s background: What was the main function of the person who prepared the Educational Pathway: school teacher; museum educator; parent; other.


78

Connection with the curriculum: Reference to the items of the science learning vocabulary mainly covered by the Educational Pathway, and prerequisite knowledge Learning objectives: Short description of the objectives of the described science learning experience Guidance for preparation: Guidance provided by the creator of the Pathway about any necessary arrangements that will need to be made by the interested teacher before launching the activities described in the following sections. B) Pre-visit Teaching Phase 1: Question Eliciting Activities

• Provoke curiosity: Describe ways and materials (resources already available in the OSR Portal, or other) that the teacher will present to the students in the classroom to attract their attention to the targeted subject matter. Make sure they are easily available to the interested user in the OSR Portal, and give directions for finding them. Possibly and if appropriate, integrate them into one practical resource in the appropriate format (e.g. a slides presentation).

• Define questions from current knowledge: Formulate the scientifically oriented questions that the teacher will present to the students to provoke their engagement in thinking about the target subject matter based on their existing knowledge. Make these questions digitally available and easily usable, e.g. by integrating them in the materials described in the previous step.

Teaching Phase 2: Active Investigation Note: This is a transitional phase on the borderline between the Pre-visit and Visit sections of the Educational Pathway. ‘Active Investigation’, and in particular the step of ‘Planning and conducting simple investigation’ can take place either before or during the ‘visit’, or both, depending on whether the teacher decides to use OSR resources of an ‘exhibit nature’ (exhibits, simulations, experiments, etc.) at this stage (on the web or during a physical visit to a science museum/centre). However the use of physical observation is concentrated mainly in the next Teaching Phase, under the ‘Visit’ section of the Educational Pathway.

• Propose preliminary explanations or hypotheses: Describe ways in which the teacher can encourage students to propose possible explanations to the questions that emerged from the previous activity. The teacher should be guided here to identify possible misconceptions in students’ thinking. If applicable, locate or make relevant assistance materials available in the OSR Portal, and give directions for finding them. If appropriate, you may consider integrating them in the materials described in the previous steps (e.g. a slides presentation).


79

• Plan and conduct simple investigation:

Describe ways and materials (resources already available in the OSR Portal, or other) that the teacher can use to facilitate the students to focus on evidence as a source of answers to scientific questions. This is the phase in which students are being prepared for the subsequent phase of evidence gathering during observation. Locate or make relevant assistance materials available in the OSR Portal, and give directions for finding them. If appropriate and relevant, it is possible to guide the teacher to use OSR resources of an ‘exhibit nature’ (exhibits, simulations, experiments, etc.) at this stage – in which case this activity should be moved to the ‘Visit’ section of the Educational Pathway. However it should be noted that the use of physical observation is concentrated mainly in the next Teaching Phase of ‘Creation’, under the ‘Visit’ section of the Educational Pathway.

B) Visit (Teaching Phase 2: Active Investigation) Note: ‘Active Investigation’, and in particular the step of ‘Planning and conducting simple investigation’ can take place in either the Pre-Visit or the Visit phase of the experience, or in both, depending on whether the teacher decides to use OSR resources of an ‘exhibit nature’ (exhibits, simulations, experiments, etc.) at this stage (on the web or during a physical visit to a science museum/centre). However the use of observation for gathering evidence is concentrated mainly in the Teaching Phase of ‘Creation’ described below. Teaching Phase 3: Creation

• Gather evidence from observation: This is the core element of the ‘Visit’ phase, and can be realized either in the school classroom/lab, by remotely using science learning resources made available by the science museums/centres on the web, or during a physical visit which will involve the use of digital resources. Locate the appropriate resource in the OSR Portal. Explain its use to the teacher, and provide access to any accompanying user support materials. The selected resource (e.g. a simulation, an experiment, an animation, a graph or other exhibit of similar nature) must provide students with an opportunity to collect evidence addressing the scientific questions posed in the previous stages through direct or indirect observation phenomena of the natural world. Provide guidance to the teacher organize and manage the activity most effectively and efficiently. It is recommended to introduce at this stage group work. Guide the teacher to divide students in groups, each of which will be facilitated by the teacher to formulate and evaluate explanations to the scientific questions based on the collected evidence. If applicable, locate or make relevant assistance materials available in the OSR Portal, and give directions for finding them.

Teaching Phase 4: Discussion Note: This is a transitional phase on the borderline between the Visit and the Post-visit sections of the Educational Pathway. ‘Discussion’ can take place either during or after the ‘visit’, or both, depending on whether the teacher considers that the use of


80

the digital ‘exhibits’ is necessary (or feasible) at this stage. Ideally, ‘Discussion’, and particularly the step of ‘Explanation based on evidence’, should take place in front of the ‘exhibit’, to reinforce the link between the physical experience of using the resource and the mental processing of the observed information by the students.

• Explanation based on evidence: Guide the teacher to provide the correct explanation for the researched topic. Describe ways and materials (resources already available in the OSR Portal, or other) she/he can use to this end, and give directions for finding them. If appropriate, integrate them into one practical resource in the appropriate format (e.g. a slides presentation).

• Consider other explanations: Guide the teacher to facilitate the student groups to evaluate their own explanations in the light of alternative explanations, particularly those reflecting scientific understanding. Describe ways and materials (resources already available in the OSR Portal, or other) the teacher can use to this end, and give directions for finding them. If appropriate, integrate them into one practical resource in the appropriate format (e.g. a slides presentation).

C) Post-visit (Teaching Phase 4: Discussion) Note: This is a transitional phase on the borderline between the Visit and the Post-visit sections of the Educational Pathway. Ideally, ‘Discussion’ should take place in front of the ‘exhibit’, to reinforce the link between the physical experience of using the resource and the mental processing of the observed information by the students. However, if necessary or preferred, it can also be organized as a post-visit activity leading into the next phase of ‘Reflection’. Teaching Phase 5: Reflection

• Communicate explanation: Guide the teacher to facilitate each student group to reflect on the previous experiences and produce a report with its findings, presenting and justifying its proposed explanations to other groups and the teacher. Make available or direct to materials (resources already available in the OSR Portal, or other) which the teacher can use to help the students familiarize themselves with and become effective in scientific writing.

Follow-up activities and materials Describe and direct the user to any follow-up activities or materials that can be used to ‘wrap-up’ the main ‘visit’ experience. These could include appropriate learning assessment and/or reminder materials (e.g. quizzes, games, other user-friendly tests), hints for further activities, suggestions for other relevant ‘visits’, etc. Sustainable contact Describe and direct the user to any existing possibilities for maintaining contact with the digital resource and its provider, or with other users of the same learning experience.


81

5.2.5 OSR Educational Pathway Pattern for an Open Visit by Lifelong Learners

5.2.5.1 Introductory note Among the possible Educational Pathway Patterns, the pattern for the description of open visits by independent informal learners can be seen as the simplest, least pre-defined learning experience examined in the OSR project. In this case, the museum educator/science communication professional, or even an experienced, motivated end-user, selects digital learning objects and combines them to form a meaningful, self-contained, user-friendly informal learning experience. The integration of resources scattered in various science museums/centres into the same learning experience is a priority (rather than selecting resources from a single museum or science centre). A considerable degree of variation in the ‘degree of structure’ of the open pathway is expected to arise during the use of the OSR system on the field, reflecting the varying degrees of user freedom in the context of informal science learning. In its extreme un-structured form, the open pathway can merely relate to random browsing and/or exploring of a set of aggregated learning objects. In such a case, implying any form of prescribed linearity of the experience should be avoided. More generally, the debates within the OSR consortium clearly show a very strong culture among science museum and centre professionals which emphasise leaving the choice and order of activities or experiences totally open, with as least intervention as possible. This aspect of the ‘open’ visit should be deemed possible in the OSR tools, without being restrictive. It should be added that science museums and centres see the ways in which end users themselves will combine learning objects free of any interventions, as a valuable source of information about users’ preferences and emerging understandings of the resources. In this context, the pathway pattern for an open visit proposed in the following sections should be seen as an initial proposition to be tested. It should be technically realised in the most flexible way to accommodate the widest possible variety of approaches across the spectrum of formal and informal learning experiences.

5.2.5.2 The underlying pedagogical approach for the open visit Although the Inquiry-Based Learning approach adopted for the description of structured educational pathways may well be relevant to open visits, too, it is felt that its structured nature may not correspond well with many of the possible formats of an open visit. Therefore, in this case the much wider Resource-Based-Learning conceptual framework (see in the third section of this document) is applied as the basis for the conception of the open visit. To allow for the highest possible flexibility, the present Pattern makes minimal use of different sub-phases, retaining however the basic organization in a three-step scheme of activities before, during, and after the ‘visit’. The core of the learning experience constitutes the ‘visit’ phase, with ‘pre-visit’ and ‘post-visit’ being left optional to the discretion of the designer of the pathway.


82

The Educational Pathway Pattern for an Open Visit by Lifelong Learners A) Introductory section and preparatory phase The following basic information about the intended learning experience is to be defined at the outset. This information should allow the user to assess the relevance of the resource to his/her learning needs, preferences and circumstances, and provide him with guidance for the preparation of the learning experience. Note that most of this information can be directly linked to specific elements of the OSR Application Profile presented in section 5.1. The formalisation proposed there for certain elements is to be applied accordingly in this introductory section too. Title: Give a title that helps easily recognize the content focus and purpose of the Educational Pathway. Short description: A description of no more than 30 words outlining the scope of the Educational Pathway, descriptive enough to help the user in the first instance to estimate its possible relevance to her/his interests. Keywords: A limited number of words/short phases reflecting the topic and scope. Target audience: The intended end user: independent informal learner, other… Age range: Up to 6, 6-9, 9-12, 12-15, 15-18, 18-25, 25+,… Context: The places that the Educational Pathway involves: science museum/centre, independently on the web. Time required: The approximate time typically needed to realize the Educational Pathway. Technical requirements: Description of any special technologies, infrastructure and/or technical expertise required for the realization of the Educational Pathway. Author’s background: What was the main function of the person who prepared the Educational Pathway: museum educator; parent; school teacher; other… Science learning elements: Reference to the items of the science learning vocabulary mainly covered by the Educational Pathway


83

Learning objectives: Short description of the objectives of the described science learning experience Guidance for preparation: Guidance provided by the creator of the Pathway about any necessary arrangements that will need to be made by the interested user before launching the activities described in the following sections. B) Pre-visit (optional) Orientation information Describe and direct the user to any information available on the context and elements of the learning activity, which may prepare and orient the use before the ‘visit’. Such information may typically be available on the web (e.g. on the museum’s website), but in cases it may also relate to other media, such as TV programmes, printed materials (e.g. museum leaflets) etc. Building pre-experiences Describe and direct the user to any information or activities that might exist and which would be a useful pre-experience preceding the main intended ‘visit’. Such content may for example refer to other learning objects on the web, or, in the case of an open pathway addressing children and families, elements of the school curriculum which children should have some knowledge of. Support or guidance available before the visit Describe and direct the user to any support or guidance mechanism or contact that may exist for the preparation of the ‘visit’. B) Visit (the minimal core of the learning experience) Provoke curiosity: questions to ask, things to observe (optional) Describe in simple terms the questions that the user could ask, or the observation or information he/she could concentrate on, during the ‘visit’ to get the most of the learning potential offered by the experience. Direct the user to any relevant digital resources. The core experience Direct the user to the digital resources constituting the core of the ‘visit’ and describe in detail the way in which the ‘visit’ should be conducted, focusing on information that will help the user’s orientation through the resources involved. If appropriate, explain the rationale behind the proposed ordering of the activities, or state and explain the freedom in which the learning experience can be shaped by the user. Support or guidance available during the visit (optional) Describe and direct the user to any support or guidance mechanism or contact that may exist to support the ‘visit’ in real time.


84

Any other relevant information (optional) Provide any other information that does not fall under the previous categories but is necessary or useful for the effective / efficient realisation of the ‘visit’. C) Post-visit (optional) Follow-up activities and materials Describe and direct the user to any follow-up activities or materials that can be used to ‘wrap-up’ the main ‘visit’ experience. These could include appropriate learning assessment and/or reminder materials (e.g. quizzes, games, other user-friendly tests), hints for further activities, suggestions for other relevant ‘visits’, etc. Sustainable contact Describe and direct the user to any existing possibilities for maintaining contact with the digital resource and its provider, or with other users of the same learning experience.


85

5.3 Social tagging opportunities and options in OSR

In these first steps in the OSR project lifecycle, the aim is to identify important issues connected with the use of social tagging and folksonomies in science museum/centre digital learning content. Such questions may focus on ways of getting, using, and understanding social tagging data, as well as the potential social impacts of such tagging processes. In the subsequent project phases the different social tagging options should become variables that the project team will control and combine in different iterations of tagging tools in order to see what experiences best benefit from different realisations, in the context of a wider exploration of how best to use social tagging and folksonomic strategies for science museum and science centres educational content. The project should create the conditions for exploring these new opportunities in the framework of a series of real-life experiments and trials with the user communities. The metadata tagging approach should be context sensitive, allowing tagging by the end-user in the context of use as far as possible, ‘on-site’ rather than just in a ‘laboratory mode’. The tagging data that will be collected should be structured to enable comparative studies that will highlight possible differences between expert and emerging non-expert vocabularies. Museums and science centres have invested significantly in the development of standards and systems for documenting museum collections. As with other formal knowledge organization systems, the high overhead of their implementation – both cognitive and institutional – has hindered broad availability of large amounts of museum data. Social tagging and folksonomies offer a less formal, more participatory, and highly distributed way to augment museums’ institutional documentation with content that reflects the perspectives and interests of their communities. The social tagging approaches realised so far in other research have typically gone as far as allowing for user-contributed keywords. The OSR project takes a decisive step ahead in this respect, experimenting with offering the opportunity to end-users to provide their own perceptions of certain standardized metadata elements of the ‘Application Profile’, too, which are considered crucial to user experience and decisions. As such elements have been identified, for example, metadata on ‘Educational Objectives’ and ‘Context’. It should be of interest to examine how users’ assessment of the educational objectives and the appropriate usage of a learning object compare with the perceptions of the professionals who have formally annotated the resources. In this way, the project will bring standardized metadata techniques with folksonomic approaches even closer together, providing unprecedented versatility and insight into the effectiveness of professionals’ metadata tagging and end-users’ perceptions of the use of digital resources.


86

Of particular interest is also the creation of a ‘community based on a scientific resource’, which may create additional learning value in the context of online communities of practice. The project should look closely at the potential offered by the social tagging procedures to facilitate the building of coherent and sustainable user communities, with a sense of common reference to, and a shared ‘ownership’ of, the results of their tagging. This could be realized in ways that will make social taggers sharing common characteristics (e.g. using the same tags) recognizable and reachable by each other. Data on communities’ tagging behaviours should be available for analysis. Table 8 presents the main folksonomy activities included in the OSR approach.

Table 8: OSR folksonomy activities Folksonomy activity Description

Tag a resource

The user will be able to socially tag a resource both free from the predefined vocabularies, as well as by making selections from the OSR vocabularies for selected metadata elements. The system will propose similar existing (and possibly also most popular) tags while the user types free keywords.

Search resources based on a tag

The user will be able to select a tag and find all resources already tagged with this term. Results may additionally include resources annotated with similar tags.

Search users based on a tag

The user will be able to identify all users that have tagged a resource with the same tag. Additionally, they will be able to find all users that have used a particular tag on different resources.

Search tags based on a resource

The user will be able to see all tags associated with a particular resource, so as to observe different relations and potential learning paths.

Create a community based on a resource

Users associated with a resource may be provided with additional Web 2.0 tools, so that the creation of ‘resource-originated’ communities may be fostered.

Figure 15: A simplified rendering of a possible data model to be used for the OpenScienceResources approach. The data model is significantly more complex than a simple relationship between user, tag, and data. This complexity – reflecting the social context of tagging – distinguishes the proposed approach from a number of tagging projects already in place.


87

6 References Anderson, L.W., Krathwohl, D.R., Airasian, P.W., Cruikshank, K.A., Mayer, R.E., Pintrich, P.R., Raths. J. and Wittrock, M. C. (eds.) (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives. Addison Wesley Longman.

Anderson P. (2007). What is Web2.0? Ideas, technologies and implications for education. JISC Technology & Standards Watch. Available at: http://www.jisc.ac.uk/media/documents/techwatch/tsw0701b.pdf

Bateman, S., Brooks, C., McCalla, G. and Brusilovsky, P. (2007). Applying Collaborative Tagging to E – Learning. In Proc. of 16th International World Wide Web Conference (WWW 2007), Banff, Alberta, Canada, 8 – 12 May 2007

Bearman D. and Trant J. (2005). Social terminology enhancement through vernacular engagement: Exploring collaborative annotation to encourage interaction with museum collections, D-Lib Mag., 11. Available online at: http://www.dlib.org/dlib/september05/bearman/09bearman.html.

Berners-Lee, T. (1997). Metadata Architecture. Available at: http://www.w3.org/DesignIssues/Metadata.html

Berners-Lee, T. (1999). Weaving the Web. Oricon Business Books.

Beswick, N. (1990). Resource-base learning. London: Heinemann.

Bloom, B. S. (ed) (1956). Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain. New York: David McKay.

Bransford, J. D.; Brown, A. L. & Cocking, R. R. (Eds.). (1999). How People Learn: Brain, Mind, Experience, and School. Washington, D.C.: National Academy Press.

Bybee, R. D., Trowbridge, L.W., Carlson Powell, J. (2008). Teaching Secondary School Science: Strategies for Developing Scientific Literacy (9th Edition). ISBN -13: 978-0-13-230450-4.

Chun, S., R. Cherry, et al. (2006). Steve.museum: An Ongoing Experiment in Social Tagging, Folksonomy, and Museums. Museums and the Web 2006: selected papers from an international conference, J. Trant and D. Bearman, Eds. Albuquerque, NM, Archives & Museum Informatics. http://www.archimuse.com/mw2006/papers/wyman/wyman.html

Cleveland Museum of Art and Hiwiller D. (2005). Cleveland Museum of Art Cataloging by Crowd Prototype Term Collection Tool. Available online at: http://www.steve.museum/reference/ClevelandPrototype-2005.pdf.

Collins, A. (1986). A sample dialogue based on a theory of inquiry teaching (Tech. Rep. No. 367). Cambridge, MA: Bolt, Beranek, and Newman, Inc.

Dave, R. H. (1975). Developing and Writing Behavioural Objectives. Educational Innovators Press.

DeBoer, G. E. (1991). A history of ideas in science education. New York: Teachers College Press.

Department of Education and Training, New South Wales, Department of Education and Training (DET) Learning Resource Metadata (DETLRM), Version 2.0, 2007

Driver, R. (1989). The construction of scientific knowledge in school classrooms. In R. Miller (Ed.). Doing science: Images of science in science education. New York: Falmer Press.

Durbin G. (2004). Learning from Amazon and eBay: User-generated material for museum web sites, Museums and the Web 2004: Proceedings, Available online at: http://www.archimuse.com/mw2004/papers/durbin/durbin.html.

Duval, E., Smith, N. and Van Coillie, M. (2006) Guidelines and support for building Application profiles in e-learning, Cen Workshop Agreement CWA 15555. Available at: ftp://ftp.cenorm.be/PUBLIC/CWAs/e-Europe/WS-LT/cwa15555-00-2006-Jun.pdf

Duval, E., Wayne, H., Stuart, S. and Stuart L. W. (2002). Metadata Principles and Practicalities. D-Lib Magazine, vol. 8. Available at: http://www.dlib.org/dlib/april02/weibel/04weibel.html


88

Emamy K., Cameron R. (2007). Citeulike: A Researcher’s Social Bookmarking Service. Available at: http://www.ariadne.ac.uk/issue51/emamy-cameron/

EUN Partnership (2007) The EUN Learning Resource Exchange Metadata Application Profile, Version 3.0. 2007.

European Commission (2000). A Memorandum on Lifelong Learning. Brussels, 30.10.2000. SEC(2000) 1832.

Fait H. and Hsi Sh. (2005). From Playful Exhibits to LOM: Lessons from building an Exploratorium Digital Library. Available: http://exploratorium.us/partner/nsdl/PDFs/jcdl2005_fait.pdf

Falk, J.H. & Dierking, L.D. (2000) Learning from museums. Walnut Creek, CA: AltaMira Press.

Falk, J.H. & Dierking, L.D. (2002) Lessons Without Limit.

Fisher, D. (2005). Table on Revised Bloom’s Taxonomy. Oregon State University Extended Campus. Available at http://oregonstate.edu/instruct/coursedev/models/id/taxonomy/#table

Gammon, B. (2003). Assessing learning in museum environments: A practical guide for museum evaluators. London: Science Museum. Available at http://sciencecentres.org.uk/events/reports/indicators_learning_1103_gammon.pdf

Gardner, T. (1991a). Making schools more like museums. Education Week, 6(6), October 9.

Gardner, T. (1991b). The unschooled mind: How children think and how schools should teach, New York.

Gardner, T. (1992). Education for understanding during the early years: Excerpts from the Unschooled mind.

Germann, P. J. (1991, April). Developing science process skills through directed inquiry. American Biology Teacher, 53(4), 243-47.

Golder S.A. and Huberman B.A. (2006). Usage patterns of collaborative tagging systems, J. Inform. Sci, 32, pp.198-208.

Greenberg, J. (2001). ‘Metadata Questions in Evolving Educational Internet-Based Terrain’. In Greenberg, J. (Ed.) Metadata and Organizing Educational Resources on the Internet. New York: The Haworth Information Press, 1-11.

Hammond T., Hannay T. et al (2005). Social bookmarking tools (2000)(I): A general review, D-Lib Mag., 11. Available online at: http://www.dlib.org/dlib/april05/Hammond/04hammond.html.

Harrow, A. (1972). A taxonomy of psychomotor domain: a guide for developing behavioral objectives. New York: David McKay.

Hayman, S. (2007). Folksonomies and Tagging: New development in social bookmarking. Education.au. Available at: http://www.educationau.edu.au/jahia/webdav/site/myjahiasite/shared/papers/arkhayman.pdf

Hein G.E. (1998). Learning in the Museum, New York, Routledge.

Hill, J. R and Hannafin1, M. J. (2001). ‘Teaching and learning in digital environments: The resurgence of resource-based learning’. Educational Technology Research and Development, Vol 49, No 3, pp. 37-52.

Hmelo-Silver, C. E. (2004). Problem-Based Learning: What and How Do Students Learn? Educational Psychology Review, 16(3), 235–266.

Hofstein, A. and Rosenfeld, S. (1996). Bridging the gap between formal and informal science learning. Studies in Science Education, 28: 87-112.

Hong J., Chen H. and Hsiang J. (2000). A Digital Museum of Taiwanese Butterflies, Digital Libraries (San Antonio, Texas) ACM 260-261.

Hounsell, D., & McCune, V. (2002). Teaching-Learning Environments in Undergraduate Biology: Initial Perspectives and Findings. Edinburgh: Economic & Social Research Council, Department of Higher and Community Education.


89

IEEE LOM (2002) Draft Standard for Learning Object Metadata, IEEE Learning Technology Standards Committee (LTSC), Available at: http://ltsc.ieee.org/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdf

Igelsrud, D., & Leonard, W. H. (Eds.). (1988, May). Labs: What research says about biology laboratory instruction. American Biology Teacher, 50(5), 303-06.

IMS Global Learning Consortium (2005a) “IMS Application Profile Guidelines Overview”. Available at: http://www.imsglobal.org/ap/apv1p0/imsap_oviewv1p0.html

IMS Global Learning Consortium (2005b) “IMS Application Profile Guidelines Technical Manual”. Available at: http://www.imsglobal.org/ap/apv1p0/imsap_techv1p0.html

Jonassen, D., & Reeves, T. (1996). Learning with technology: Using computers as cognitive tools. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 693-719). New York: Macmillan.

Joyce, M. Z. & Tallman, J. I. (1997). Making the Writing and Research Connection with the I-Search Process. How-To-Do-It Manuals for Librarians, Number 62. Neal-Schuman.

Knowlton, D. S. & Sharp, D. C. (Eds.) (2003). Problem-based learning in the information age. San Francisco: Jossey-Bass.

Kolodner, J.L. (Ed.), (1993). Case-Based Learning. Kluwer Academic Publishers, Dordrecht, Netherlands.

Koulaidis, V. (2003). ECSITE Directors’ Forum, Athens 7-8, 2003, Education systems (formal and informal)-Benchmarking the promotion of RTD culture and public understanding of science.

Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1973). Taxonomy of Educational Objectives, the Classification of Educational Goals. Handbook II: Affective Domain. New York: David McKay Co., Inc.

The Learning Federation, Australia New Zealand LOM (ANZ-LOM): Metadata Application Profile, Version 1.1, 2008

Linn, M. C., Davis, E. A. & Bell, P. L. (Eds) (2004). Internet environments for science education. Mahwah, NJ: Lawrence Erlbaum Associates

Lorin W. Andersin, David R. Krathwohl; et al. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives. Addison Wesley Longman.

Lund, B., Hammond, T., Flack, M. and Hannay, T. (2005). Social Bookmarking Tools (II) – A Case Study Cannotea. In D – Lib Magazine, (ISSN 1082 – 9873), 11(4). Available at: http://www.dlib.org/dlib/april05/lund/04lund.html

Mason, B. (2006) Digital Libraries in Support of Science Education: A Case for Computational Physics, Computing in Science & Engineering, 8(4), 62-65.

Mathes A. (2004). Folksonomies – Cooperative classification and communication through shared metadata. Available online at: http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html.

McGreal, R. (2004) Introduction, in McGreal R. (Ed.), Online Education Using Learning Objects. Open and Distance Learning Series, London: Routledge/Falmer, 1-16

National Science Board. (1991). Science & engineering indicators-1991. Washington, DC: U.S. Government Printing Office.

Nickerson, R. S. (2004). Teaching reasoning. In J. P. Leighton & R. J. Sternberg (Eds.), The nature of reasoning (pp. 410-442). New York: Cambridge University Press.

Norman, G.R., & Schmidt, H.G. (1992). The psychological basis of problem-based learning: A review of the evidence. Academic Medicine, 67(9), pp. 557-565.

NRC (National Research Council) (2000). Inquiry and the National Science Education Standards. A Guide for Teaching and Learning: National Academies Press.

O’ Reilly T. (2005). What is Web 2.0. Available at: http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html


90

Osborne, J. & Dillon, J. (2008). Science Education in Europe: Critical Reflections. A report to the Nuffield Foundation.

Osborne, M., & Freyberg, P. (1985). Learning in science: Implications of children's knowledge. Auckland, New Zealand: Heinemann.

PISA (2000). Knowledge and Skills for Life: First Results from PISA 2000. OECD. ISBN: 9789264196711.

PISA (2003). Learning for Tomorrow's World – First Results from PISA 2003. Available online at: http://www.pisa.oecd.org/dataoecd/1/60/34002216.pdf

PISA (2006). PISA 2006 Science Competencies for Tomorrow's World: Volume 1. OECD. ISBN: 9789264040007

Polsani, P. (2003) ‘Use and Abuse of Learning Objects’, Journal of Digital Information, 3 (4) Article No. 164, Available at: http://jodi.ecs.soton.ac.uk/Articles/v03/i04/Polsani/

Powell, A. (2005) Resource Discovery Network (RDN)/Learning and Teaching Support Network (LTSN) LOM Application Profile (RLLOMAP),Version 1.1, 2005

Powerhouse Museum and Chan S. (2005). Electroninc Swatchbook, Available online at: http://www.powerhousemuseum.com/electronicswatchbook.

Quintarelli E. (2005). Folksonomies: Power to the people, ISKO Italy – UNIMIB Meeting, Milan.

Rakow, S. J. (1986). Teaching science as inquiry. Fastback 246. Bloomington, IN: Phi Delta Kappa Educational Foundation.

Reichel, M. and Kohlhase A. (2006). Embodied, Constructionist Learning: Social Tagging and folksonomies in E- earning Environments . mICTE 2006. Conference Proceedings.

Rocard, M., Csermely, P., Jorde, D., Lenzen, D., Walberg-Henriksson H. and Hemmo, V. (2007) Science Education Now: a renewed pedagogy for the future of Europe, European Commission, ISBN – 978-92-79-05659-8.

Salmi, H. (2001). Public understanding of science: universities and science centres, Management of University Museums. Education and skills. OECD, Paris. 151-161.

Salmi, H. (2003). ‘Science centres as learning laboratories: experiences of Heureka, the Finnish Science Centre’. International journal of Technology Management, Vol 25, No 5, pp. 460-476.

Samis P. (1999). Artwork as interface, Arch. Mus. Inform., 13, pp. 191-198.

Sampson, D. & P. Karampiperis (2004) ‘Reusable Learning Objects: Designing Metadata Management Systems supporting Interoperable Learning Object Repositories’. In Rory McGreal (Ed.), Online Education Using Learning Objects (ISBN 0415335124), Chapter 16, pp. 207-221, Taylor & Francis Books Ltd.

Sampson, D. (2004) The Evolution of Educational Metadata: From Standards to Application Profiles, in Proc. of the 4th IEEE International Conference on Advanced Learning Technologies (ICALT 04), ISBN: 0769521819, 1072-1073, Joensuu, Finland, IEEE Computer Society, August 2004.

Sampson, D., Karampiperis, P. and Zervas, P. (2005) ASK-LDT: A Web-Based Learning Scenarios Authoring Environment based on IMS Learning Design, International Journal on Advanced Technology for Learning (ATL) (ISSN 1710-2251), 2(4), 207-215, ACTA Press, October 2005.

Sampson, D., Karampiperis, P. and Zervas, P. (2006) "Authoring Web-based Learning Scenarios based on the IMS Learning Design: Preliminary Evaluation of the ASK Learning Designer Toolkit", in Proc. of the 4th ACS/IEEE International Conference on Computer Sytems and Applications (AICCSA-06), 217, Dubai/Sharjah, UAE, March 2006.

Sandoval, W. A., & Bell, P. (Eds.). (2004). Design-based research methods for studying learning in context. [Special Issue] Educational Psychologist, 39(4).

Schmidkunz, H. & Lindemann, H. (1992). Das forschend-entwickelnde Unterrichtsverfahren. Problemlösen im naturwissenschaftlichen Unterricht. Westarp Wissenschaften, Essen.

Seldow, Α. (2006). Social Tagging in K – 12 Education: Folksonomies for Student Folk. Available at: http://mrseldow.gradeweb.com/custom/Social_tagging_in_K12_Education_Seldow_4_3_06.pdf


91

Simpson, E. J. (1972). The Classification of Educational Objectives in the Psychomotor Domain. Washington, DC: Gryphon House.

Smith G. (2004). Folksonomy: social classification, Available online at: http://atomiq.org/archives/2004/08/folksonomy_social_classification.html

Sotiriou S. et al (2007). Designing the classroom of tomorrow by using advanced technologies to connect formal and informal learning environments, ISBN 978-960-6701-31-3, EPINOIA Ed.

Sotiriou, S. & Bogner, F. (2008). Visualising the Invisible: Augmented Reality as an Innovative Science Education Scheme. Advanced Science Letters, Vol 1, 114-122, 2008

STEDE (2002). Science Teachers Education Development Across Europe, 2nd Annual Report.

Stevenson A. (2005) JORUM Application Profile, Final Version 1.0, 2005

TIMSS (2003). Highlights from the Trends in International Mathematics and Science Study (TIMSS) 2003. Available online at: http://nces.ed.gov/pubs2005/2005005.pdf

Tinnesand, M., & Chan, A. (1987). Step 1: Throw out the instructions. Science Teacher, 54(6), 43-45.

Trant J and Wyman B. (2006). Investigating Socal Tagging and Folksonomy in Art Museums with steve.museum, World Wide Web 2006: Tagging Workshop Edinburgh, UK, ACM. Available online at: http://www.steve.museum/index.php?option=com_weblinks&task=view&catid=35&id=32.

Trant J. and Bearman D. Eds (2006). Museums and the Web: Selected Papers from an International Conference. Toronto, Archives & Museums Informatics. Available online: http://archimuse. Com/mw2006/speakers/index.html.

Trant, J. and Project, With The Participants In TheSteve.Museum (2006). Exploring the potential for social tagging and folksonomy inart museums: Proof of concept, New Review of Hypermedia and Multimedia, 12:1,83 - 105

UK Learning Object Metadata Core, Draft version 0.3_1204, (December, 2004), Modification date: 2008-06-10

Ullrich, C., Borau, K., Luo, H., Tan, X., Shen, L. & R. Shen (2008). ‘Why Web 2.0 is Good for Learning and for Research: Principles and Prototypes’. Proceedings of the 17th International World Wide Web Conference, ACM Publications.

Vander Wal T. (2005), Delicious lesson and social network ecosystems, Off the Top [blog], Available online at: http://www.vanderwal.net/random/entrysel.php?blog=1765.

VET Metadata Application Profile (VETADATA) Specification Document, version 1, January 2009

Victoria & Albert Museum, Ultralab and Culture Online (2005). Every object has a story, Available online at: http://www.everyobject.net.

Vuorikari, R. (2006). Can European teachers find curriculum related digital learning resources? The LIFE survey on curriculum related search possibilities in national and regional school portals in Europe. Life, European Schoolnet. Available online at: http://wiki.eun.org/life-wiki/index.php/Main_Page

Vuorikari, R. (2007). Folksonomies, Social Bookmarking and Tagging: State of the art. European Schoolnet. Available at: http://info.melt-project.eu

Vygotsky, L.S. (1980). Mind in Society. Harvard University Press.

Wiley, D. (2000). Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy. IN Wiley, D.A (Ed), The Instructional Use of Learning Objects: Online Version. Available: http://reusability.org/read/chapters/wiley.doc.

Wiley, D. (2002) Connecting Learning Objects to Instructional Design Theory: A definition, a metaphor and taxonomy, in Wiley D. (Ed.), The Instructional Use of Learning Objects, Association for Instructional Technology and the Association for Learning Communications and Technology, 1-35.

Wiley, D.A. Ed. (2002). The instructional use of learning objects. Agency for Instructional Technology (AIC). Available: http://www.reusability.org/read/


92

Zimmermann, B., Meyer, M., Rensing, C. and Steinmetz, R. (2007) Improving Retrieval of Reusable Learning Resources by Estimating Adaptation Effort. In Proc. of the First International Workshop on Learning Object Discovery & Exchange (LODE'07), Lassithi – Crete, Greece, 46-53.


93

7 Annexes

7.1 Annex I: Educational Design Requirements Elicitation Questionnaire

Introductory note: This questionnaire is addressed to the organizations acting as Content Providers in the project (science museums and science centres). Its purpose is twofold: a) to help assess each content provider’s familiarity with educational metadata and repositories of digital learning objects, so that the corresponding project processes of Educational Design are adjusted accordingly; and b) to provide the science museum and science centre experts with a tool for their input in the process of designing the organizational and conceptual structure that will underlie the OSR activities. Please take the time to complete this questionnaire in detail to help us integrate your expertise and field experiences most effectively in these early but crucial phases of the design. 1. General experience with educational metadata 1.1 What is your experience with metadata standards like IEEE LOM and IMS Learning Design?

( ) Excellent ( ) Higher than average ( ) Average ( ) Lower than average ( ) No experience

1.2 Would you consider your knowledge and experience of characterizing Learning Objects (educational

resources) with educational metadata as: ( ) Excellent ( ) Higher than average ( ) Average ( ) Lower than average ( ) No experience

1.3 Would you consider your knowledge and experience of using educational metadata for retrieving

Learning Objects as: ( ) Excellent ( ) Higher than average ( ) Average ( ) Lower than average ( ) No experience

1.4 Have you ever used an educational metadata authoring tool?

( ) No ( ) Yes

1.4.1 If yes, please specify which authoring tools you have used in the past: (please mark all answers that

apply)

( ) Reload Editor ( ) LOM Editor ( ) LomPad ( ) Other


94

If other, please specify:

2. General experience with Learning Objects Repositories 2.1 Have you ever used a Learning Objects Repository?

( ) No ( ) Yes

2.1.1 If yes, please specify which Learning Object Repository you have used in the past: (please mark all answers that apply)

( ) Merlot ( ) GEM ( ) EducaNext ( ) EdNA ( ) Curriculum Online ( ) Other

If other, please specify:

2.1.2 Do you regard the functionalities of the Learning Objects Repositories which you have used as

appropriate for searching and retrieving science education resources?

( ) appropriate ( ) appropriate to some extent – sufficient ( ) appropriate to some extent – deficient ( ) not appropriate

Comment:


95

2.1.3 Did you manage to find science education resources from the Learning Objects Repositories which you

have used?

( ) I found science education resources ( ) I found science education resources to some extent – sufficient ( ) I found science education resources to some extent – deficient ( ) I did not find science education resources Comment:

3. Elements for searching science education resources Note: The table in the following pages presents some metadata elements that can be used to facilitate organizing, searching and retrieving digital science museum/centre learning resources. They are categories organizing the information that can be attached to each digital learning object. Please note that anything can be considered as a digital learning object, from a single file (e.g. a photo, a piece of text, etc.) to a large coordinated system of files, digital exhibits, web pages, etc. The metadata information may be added to a digital object either by choosing between entries in pre-defined vocabulary lists, or by filling open-ended text fields. Please note that we will ask for your opinion on the vocabularies for some of these elements in the next section of the questionnaire. In the following table, please state the importance that you, based on your expertise and experience in the field of learning in science museums and centres, assign to each metadata element by putting an “X” into the relevant box.


96

Table 1: Importance of metadata elements for science education resources

Not important Low Importance Important High

Importance

1. The Title of the learning resource (few words reflecting the content and purpose of the resource) 2. The human Language used within the learning resource to communicate with the intended user (choice from a list of languages) 3. The general Description of the content of the learning resource (a short open-text description) 4. The Educational Objectives of the learning resource (short open-text descriptions under three domains: affective, psychomotor, and cognitive) 5. Keywords characterizing the topic of the learning resource (a limited number of words/short phases reflecting the topic) 6. The underlying organizational Structure of the learning resource (atomic, a collection, networked, hierarchical, linear; see next section) 7. The Aggregation Level of the learning resource (educational content, educational pathway; see next section) 8. The Author’s Name of the learning resource (who has created/authored/ produced the resource) 9. The Publisher’s Name of the learning resource (who is providing/distributing the resource, e.g. the science museum/centre, the user who generated it) 10. The Metadata Creator’s Name of the learning resource metadata (who has inserted the metadata) 11. The Metadata Validator’s Name of the learning resource metadata (who has confirmed the inserted metadata) 12. The human Language of the metadata (choice from a list of languages) 13. The technical Format of the learning resource file (choice from a list; see next section) 14. The Size of the learning resource file in KBs (choice from a list; see next section) 15. The Technical Requirements to use the learning resource (list choices, e.g. the operating system, the web browser, etc.)

Not important Low Importance Important High Importance16. The Learning Resource Type (digital image of object, diagram, simulation, experiment, narrative text, visit plan, lesson plan, etc; see next section) 17. The Interactivity Type (the learning mode supported by the resource: active, expositive, mixed; see next section) 18. The Interactivity Level (the degree to which the learner can influence the aspect


97

or behavior of the resource: very low, low, medium, high, very high; see next section) 19. The Intended End-User Role of the learning resource (teacher, student, informal learner/visitor, museum educator; see next section) 20. The Context where the use of the learning resource is intended to take place (school, science museum/centre, independently on the web; see next section) 21. The Typical Age Range of the intended user of the learning resource (see next section) 22. The Difficulty of the learning resource (very easy, easy, medium, difficult, very difficult) 23. The approximate/typical Interaction Time to work with the learning resource (two aspects: a) time spent with the digital object during a physical or virtual visit; and b) if relevant, educational time required for the corresponding activity at school; see next section) 24. Whether use of this learning resource requires Payment 25. Whether Copyright or Other Restrictions apply to the use of this learning resource 26. The Classification within a science learning classification system (energy, electricity, forces, etc.; see next section)


98

4. Your opinion on some of the proposed vocabularies for the metadata elements used for searching science education resources Note: In Table 1 above, you will have noticed the vocabularies proposed for some of the metadata elements (inserted in parentheses after each element). In the present section, we would like to focus on some of these vocabularies, asking for your opinion and suggestions. If you would like to express yourself on any metadata elements and their vocabularies which are not discussed in this section, please use Section 5 further below, which is reserved for your general comments. 4.1 For the metadata element that characterizes the Structure [Element 6, Table 1] of the learning resource, a

proposed vocabulary is the following:

− atomic (a resource that is indivisible in this context) − collection (a set of resources with no specified relationships between them) − networked (a set of objects with relationships that are unspecified) − hierarchical (a set of objects whose relationships can be represented by a tree structure) − linear (a set of objects that are fully ordered, i.e. connected with ‘previous’ and ‘next’

relationships) 4.1.1 Do you regard the above vocabulary as an appropriate vocabulary for this element?

( ) appropriate ( ) appropriate to some extent – sufficient ( ) appropriate to some extent – deficient ( ) not appropriate Comments/suggestions for improvement:

4.2 For the metadata element that characterizes the Aggregation Level [Element 7, Table 1] of the learning

resource, a proposed vocabulary is the following:

− educational content (any learning resource, from a single file to a complex exhibit or a whole exhibition)

− educational pathway (a plan for using a meaningful combination of various instances of educational content)

4.2.1 Do you regard the above vocabulary as an appropriate vocabulary for this element? ( ) appropriate ( ) appropriate to some extent – sufficient ( ) appropriate to some extent – deficient ( ) not appropriate Comments/suggestions for improvement:

4.3 For the metadata element that characterizes the technical Format [Element 13, Table 1] of the learning resource a proposed vocabulary is the following:

− text/plain − text/html − text/css − text/xml − text/rtf − application/pdf − application/zip − application/msword − application/xml − application/mspowerpoint − application/msexcel − application/msaccess − application/asp


99

− application/java − application/flash − image/jpeg − image/gif − image/tiff − image/png − audio/avi − audio/mp3 − video/mpeg − video/quicktime − video/mov − 3D/… − bibliographic records/UNIMARC

4.3.1 Do you regard the above vocabulary as an appropriate vocabulary for this element?


4.4 For the metadata element that characterizes the Size [Element 14, Table 1] of the educational resource a


− Up to 250KB − From 250KB to 500KB − From 500KB to 1MB − From 1MB to 5MB − More than 5MB



4.5 For the metadata element that characterizes the Learning Resource Type [Element 16, Table 1] a


− Image of object (still picture) − Video − diagram/graph/chart/plot − table − index − animation − simulation − experiment − narrative/explanatory text − exercise/problem − self assessment test − questionnaire − physical visit plan − virtual visit plan − lesson plan − project plan − lecture


100

− scientific article/text − other article/text



Comments/suggestions for improvement:


101

4.6 For the metadata element that characterizes the Interactivity Type, i.e. the learning mode that is

supported by the educational resource [Element 17, Table 1] a proposed vocabulary is the following:

− active learning / learning by doing: user input/action/decision required (e.g. simulations, experiments, exercises/problems, tests, questionnaires,, visit/lesson plans)

− expositive/passive learning: user absorbing the presented information (e.g. video, audio, pictures, graphs, texts, hypertexts, lectures)

− mixed: a combination of active and expositive learning (e.g. hypertext with embedded simulation applet)




4.7 For the metadata element that characterizes the Interactivity Level, i.e. the degree to which the learner

can influence the aspect or behavior of the science learning resource, [Element 18, Table 1], a proposed vocabulary is the following:

− very low (e.g. observation/reading only) − low (e.g. push-button or select-link only) − medium (e.g. limited data entry by the user) − high (e.g. some user controls affecting object behaviour) − very high (e.g. object behaviour totally shaped by the user)





102

4.8 For the metadata element that characterizes the Intended User Role [Element 19, Table 1] of the

educational resource a proposed vocabulary is the following:

− teacher − student − general visitor/informal learner − museum educator




4.9 For the metadata element that characterizes the Context [Element 20, Table 1] where the use of the

educational resource is intended to take place a proposed vocabulary is the following:

− school − science museum/centre (physical visit) − independent use on the web (virtual visit)




4.10 For the metadata element that characterizes the Typical Age Range [Element 21, Table 1] of the intended

user of the educational resource a proposed vocabulary is the following:

− -6 − 6-9 − 9-12 − 12-15 − 15-18 − 18-25 − 25+ − mixed age/families



4.11 For the metadata element that characterizes the approximate/typical Interaction Time [Element 23,

Table 1] to work with the learning resource the two proposed vocabulary are the following:

A: Interaction during a visit (physical or virtual) − 1-3 minutes − 5 minutes − 10 minutes


103

− more B: Didactic hour equivalents in school-based work (if relevant) − ¼ hour − ½ hour − 1 hour − 2 hours − more



4.12 For the metadata element that characterizes the Classification [Element 26, Table 1] of a learning

resource within a science learning classification system, the proposed vocabulary has been derived from the COSMOS project (http://www.cosmosportal.eu/cosmos/) and is presented in Annex A of this document

4.12.1 Do you regard the above vocabulary as an appropriate vocabulary for this element? ( ) appropriate ( ) appropriate to some extent – sufficient ( ) appropriate to some extent – deficient ( ) not appropriate Comments/suggestions for improvement:

5. Any further comments or suggestions:

Other/general comments and suggestions:

Thank you very much for your cooperation and important contribution


104

7.2 Annex II: Science Learning Content Vocabulary for the “Classification” Element [Element 26] of the OSR Educational Metadata Structure

Astronomy Asteroid belt Asteroids Astrobiology Astrometry Astronauts Astroseismology Atmospheres Aurora Big Bang Binary stars Black holes Brown dwarfs Comets Comets and meteors Constellations Coordinates Cosmic background radiation Cosmic rays Cosmology Crater Dark energy Dark matter Density waves Dust Dwarf galaxies Earth Eclipses Einstein ring Elliptical galaxy Escape velocity Extrasolar planets Extraterrestrial life Formation Galactic wind Galaxies Galaxy clusters Gamma ray bursts Gas


105

Giants Globular clusters Gravitational lenses Halos Hertzsprung-Russell diagram HII region Hubble expansion Inflation Intergalactic medium Interstellar medium Irregular galaxy Jets Kuiper belt objects Light curve Lunar eclipse Main sequence Mass loss Meteor Meteorite Microlensing effect Milky Way Moon Near-earth objects Nebula Neutron stars Nucleosynthesis Open clusters Orbit Origin and evolution of the universe Orrery Phases Phases of the Moon Planetary nebula Planets Pulsars Quasars Redshift Rockets Rotation curve Satellites: natural satellites Satellites: artificial satellites Seasons Solar activity Solar eclipse


106

Solar system Solar system - other Solar-terrestrial relations Space flight Space ships Space stations Spiral galaxy Star chart Stars Sun Sunspots Supernova Supernova remnants Theory of relativity Tides Universe – generally Variable stars Zodiac Zodiacal light Atoms and molecules Atomic structure Atoms – generally Bonding – generally Covalent bonds Electrons – generally Ionic bonds Molecules – generally Nucleus: protons, neutrons Other types of bonding Role of electrons in reactions Changing materials Burning Chemical changes Physical changes Solubility Water cycle States of matter Chemical reactions Acids, alkalis and bases Catalysts Conservation of mass Displacement reactions Enzymes Equations and formulae


107

Exo/endothermic Oxidation and reaction Patterns in reactions Reaction rates Reactions with metals Reactivity series Reversible reactions Thermal decomposition

Earth science Atmosphere and oceans: biosphere Chemical weathering Igneous rocks Lithosphere and tectonic processes Metamorphic rocks Physical weathering Rock formation - generally Rocks and soils - generally Sedimentary rocks Weathering - generally Electricity and magnetism AC/DC Ampere's Law Charge Circuits - generally Components in circuits: batteries, etc Coulomb law Domestic appliances Electric charge - generally Electric current Electric motors Electrical heating and costs Electrical quantities - generally Electrical resistance/conductivity Electricity generation/National Grid Electromagnetism - generally Electrostatic forces Electrostatic phenomena and uses Generators and transformers Magnetic materials Magnetism - generally Mains electricity - generally Mains electricity safety Maxwell's equations Parallel circuits


108

Series circuits Voltage Elements, compounds and mixtures Alkali metals Chromatography Compounds - generally Distillation Elements Filtration Halogens Mixtures Noble gases Periodic table Separation - generally Separation - other Transition metals

Energy Conduction, convection and evaporation Conservation and dissipation Energy - using electricity Energy resources Energy transfer and storage Kinetic energy Potential energy Radiation Radiation transfer Temperature and heat Thermodynamics Work and power Energy and nutrient transfer Biomass Carbon and nitrogen cycles Energy and ecosystems Food as fuel Food chains and webs

Environment Adaptation and competition Biodiversity Care of animals/plants/habitats Interdependence Micro-organisms Pollution Population abundance Predation


109

Sustainable development

Fields Central field Conservative force field Electric field Electromagnetic field Gravitational field Magnetic field Potential Forces and motion Acceleration Air resistance Angular acceleration Angular velocity Centre of mass Circular motion Collision Combining forces Conservation of momentum Elastic collision Electric force Escape velocity Foucault pendulum Friction Gravitational force and gravity Horizontal throw Impulse Inelastic collision Inertia Kepler's laws Lorentz force Machines Magnetic force Mass Moment of inertia Moments Newton's laws Nuclear force Oscillations Pendulum Period Phase Pressure Rectilinear motion


110

Rigid body Rotation Universal law of gravitation Velocity Vertical throw Weight

Green plants Flowering plants/life cycle/parts of plants Photosynthesis Plant nutrition and growth Seeds Transport and water in plants Humans and other animals Aerobic and anaerobic respiration Breathing Circulatory system - blood Circulatory system - heart Enzymes in digestion Eyes Fetal development Growth and life cycle Homeostasis Hormones - generally Hormones and fertility Human health - generally Human health: alcohol Human health: bacteria/viruses Human health: defence mechanisms, including immunisation Human health: diet Human health: medicines Human health: other harmful substances, including drugs Human health: smoking Human health: teeth Insulin Menstrual cycle Nervous system - generally Nutrition and digestion - generally Puberty/adolescence Reproductive system Senses Skeleton and muscles Stimulus and response Stomach acid and bile Transport of reactants/products


111

Life processes Biotechnology Cell processes - generally Cell structure Cell types - generally Cell types - other Chromosomes Epithelial Fertilisation Meiosis Mitosis Organs Ova Parts of the body Root hair Sperm Tissues

Light Colour Light sources Properties of light - generally Reflection Refraction Refraction Index Vision Obtaining and using materials Electrolysis Extraction of metal from ore Fossil fuels Fossil resources - generally Hydrocarbons Metals - generally Nitrogenous fertilizers Plastics/polymers Useful substances from rocks and minerals

Radioactivity Alpha radiation Background radiation Beta radiation Gamma radiation Half-life Nuclear decay Nuclear fission Nuclear fusion


112

Uses of radioactivity, including radioactive dating Scientific enquiry Analogies Application of science - generally Asking questions Benefits and drawbacks of scientific/technological developments Choosing equipment Contexts for science Creativity in science Experimental models Fairness of test/comparison Ideas and evidence in science Identifying patterns/anomalies Misconceptions Prediction compared to results Primary information Recognising limitations of evidence/data/assumptions Recording observations/measurements Safety Scientific communication Scientific investigations - generally Scientific prediction Secondary information SI units Using or evaluating a technique Using science to explain Solids, liquids and gases Changes of state Density Gas pressure and diffusion Grouping materials Melting/boiling points Particle theory Properties of materials

Sound Audible ranges Hearing - generally Hearing: noise Loudness Pitch Properties of sound - generally Sound sources Speed in media The ear


113

Ultrasound Tools for science Accelerometers Detectors Detectors: CCD camera Dynamometers Fieldwork equipment Laboratory equipment - generally Laboratory glassware Laboratory measuring instruments, including sensors and meters Microscope Observatories Sensors Thermometers Useful materials and products Everyday materials Variation, inheritance and evolution Asexual reproduction Classification/keys Cloning, selective breeding and genetic engineering DNA Environmental causes of variation Evolution - generally Extinction Fossil record Genetic causes of variation and mutation Inheritance - generally Inherited diseases Monohybrid inheritance Sex determination Variation - generally

Waves Diffraction Doppler effect Electromagnetic spectrum Gamma rays Information transmission, analogue and digital signals Infrared Longitudinal waves Microwaves Optics Radio waves Reflection Refraction


114

Seismic waves Transverse waves Ultraviolet Visible light Wave amplitude Wave characteristics - generally Wave frequency Wave speed Wavelength X-rays

Technological applications Horology Industrial devices Lifecycle of products Energy production and energy resources research Musical instruments Nanotechnology Photography and cinematography Robotics Sound techniques Telecommunications Transport (air, water and ground) Writing ad press Metal processing Paper production Textiles Pharmaceutics Mining Nautical tools Glass production Ceramics production Wood production

History of Science and Technology Scientists and inventors First Scientific Revolution Second Scientific Revolution Science: historical and contemporary examples

Documents

Towards the Development of a Common Digital Repository for ... 2.1 OSR Educational Design.pdf · as the “free-choice learning” sector…Any public education reform effort that