Learning Object Metadata

Masoud Makrehchi, PAMI, UW

Learning Object Metadata

Masoud Makrehchi

PAMI

University of Waterloo

August 2004


Examples

• Multimedia Educational Resource for Learning and Online Teaching- MERLOT

• EdNa

• Campus Alberta Repository of Educational Objects- CAREO

• eduSource Canada- a network for learning objects repositories

http://www.merlot.org/Home.po

http://www.edna.edu.au/edna/page1.html

http://careo.ucalgary.ca/cgi-bin/WebObjects/CAREO.woa?theme=careo

http://www.edusource.ca/english/home_eng.html

http://www.edusource.ca/english/home_eng.html

Masoud Makrehchi, PAMI, UWLearning Object

Learning Objects

• Learning Objects can be defined as any digital resource and associated metadata, which can be used, re-used or referenced during technology support learning.

Content Object

Metadata


Learning Objects

• Learning objects– also known as

• digital objects• knowledge objects• educational objects• instructional objects• intelligent objects• reusable learning objects• data objects

– including small, independent chunks of digital information that can be reused in their original form or adapted to meet the needs of unique learners.


Learning Objects

• The content of a learning object can include– image – interactive game – assessment – digital video – multi-media file – instructional text – web site – sound file – simulation


Learning Objects

• Benefits of Using Learning Objects – personalized learning

– increased selection of learning material

– reduced development time

– reuse of resources

• Motivations– Multicultural and multilingual societies (Canada,

Austria, EU, USA and China)

– Long distances and expensive educational cost


Metadata

• Metadata is data about data. Metadata is information that describes content.

• Descriptive metadata is stored in a database.– Information such as the title, author, producer,

date of production, and a description of the content are just a few examples of metadata that is normally stored in the database.

• Metadata can be entered manually or it can be generated automatically.


Metadata

• Objective Metadata– are factual data, most of which can be generated

automatically – things such as physical attributes, date, author, operational requirements, costs, identification numbers, and ownership.

• Subjective Metadata– the more varied and valuable attributes of a learning

object determined by the person or group who creates the metadata, such as subject, category, and discription.


Metadata

Learning Object

Metadata

Subject------------------------------Content Creator------------------------------Contact Info------------------------------Availability------------------------------Target Audience------------------------------Title------------------------------Description------------------------------Keyword

More Subjective parts of Metadata



• In web based learning, the trend is to encode learning materials with meaningful and machine understandable metadata in order to facilitate modular and reusable content repositories.

• Learning object metadata is usually represented in XML or RDF format.



• In learning object repositories, Metadata automatically retrieved, filtered by learning object repositories but metadata is not automatically generated.

• Metadata is used not only in searching and access to the learning object repositories but also in reusing learning object materials and learning objects aggregation.

• Learning object metadata is the base of most operations on learning objects.



• Learning object repository stores both learning objects and their metadata in two different ways– Storing them physically together (CLOE)

– Learning Objects and their metadata stored separately (SchollNet and MERLOT)

• Most Learning Object Repositories are actually learning object metadata repository in which every metadata includes the link to the learning object resource (content is somewhere else).


Learning Object Metadata Standards

• Instructional Management Systems Project (IMS)• Advanced Distributed Learning Initiative (ADL) and

SCORM• Alliance of Remote Instructional Authoring and

Distribution Networks for Europe (ARIADNE)• Dublin Core Metadata Initiative• IEEE Learning Technology Standards Committee (LTSC)

Learning Object Metadata- IEEE 1484• Canadian Core Learning Object Metadata (CanCore)• World Wide Web Consortium (W3C)



Source: www.Schoolnet.Cahttp://www.schoolnet.ca/home/e/resources/metadata/newurl_business_education_5861_e.html



http://www.ischool.washington.edu/sasutton/IEEE1484.html


Country Metadata Schema Subject Domain Number of LOs Automatic Metadata

ARIADNE Europe IEEE LOM All 2498 In progress

SMETE USA IEEE LOM Science and Engineering 1645 N/A

Learning Matrix

USA IEEE LOM Science and Engineering 170 N/A

iLumina USA IEEE LOM Science and Engineering 880 N/A

MERLOT USA IEEE LOM All 7408 N/A

HEAL USA IEEE LOM

(CanCore)

Health Science N/A N/A

CAREO Canada IEEE LOM

(CanCore)

All 1576 4042 N/A

Learn-Alberta

Canada IEEE LOM

(CanCore)

K-12 N/A N/A

Edna Australia Dublin Core Education 15782 N/A

Lydia USA IEEE LOM (SCORM)

All 48 N/A

Source: Reusable Learning Objects: Survey of LOM-Based repositories, F. Neven, E. Duval



Research on Metadata

• The purpose of using Metadata – Access and usability of the information

resource (a book, a web page, a learning object, or even a service) learner, …

– Information Management, categorization, information integration and aggregation, reusability administrators and developers


Research on Metadata

• The purpose of using Metadata – Access and usability of the information

resource (a book, a web page, a learning object, or even a service) learner, …

– Information Management, categorization, information integration and aggregation, reusability administrators and developers

Data Mining and Machine Learning

Information Retrieval


Case Study

• We need data to develop machine learning and data mining techniques for LORNET.

• Learning object metadata data set – metadata + content object (raw data)– Preferably Labelled – We know gathering content data and converting

to text can be difficult or impossible (assume, a learning object can be just a java applet!) we have to work only with metadata


Case Study

• Canada’s SchoolNet– Most learning resources are not actual learning

object– Contains a huge number of metadata, mostly

informative.– More than 7000 learning resources in 17

categories (labeled metadata)


Canada’s SchoolNet

Case Study

http://www.schoolnet.ca/home/e

http://www.schoolnet.ca/home/e


Thank you!


Research Motivation

• Automatic generation of a number of metadata fields to facilitate the generating metadata repository.– In ARIADNE (an European-based Learning

Object initiative) project, working on the area of automatic metadata generation is currently in progress.


Proposed Schema

• Since metadata includes many objective and subjective parts, then in the proposed research we focus on only most important subjective parts (except Description part which is more challenging);– Subject/Category – Keywords


Metadata Subject Extraction

• Since LOR data is usually in form of web data (HTML or XML), then we can use tag information in document representation and feature selection

• Document representation– Document Vector and/or Ontology

• Dimensionality reduction (feature selection)– Information theoretic approach– Latent semantic indexing (SVD)

• Classification (supervised learning)– Soft computing approach (fuzzy classification rules)


Metadata Keywords Extraction

• Proposed algorithm– Term clustering in every LO data vector– Finding the optimum association between these

clusters and keywords in Metadata vector through an optimization process (for example a Genetic Algorithm)

– Extracting association rules


Information Requirements

• To train and test the proposed schema, we need a plenty of learning object data with their Metadata,– Learning Object data in text or HTML is

preferred.– Metadata is usually presented in RDF or XML

format, we prefer these kind of metadata.


Metadata

• Defines attributes for characteristic about each content object used in authoring of learning objects (i.e. Title, description, author, etc.).

• “It facilitates searching, management and linking granules of content. Allows users and authors of content to search, retrieve and assemble content objects according to parameters defined by users” (Hodgins, etal)



• Metadata allows people to search the repository for content.

• To support flexible access to the LO’s– an efficient search and retrieval system is

required.– LO metadata capture characteristics of LO’s

and their educational information.

Documents

Learning Object Metadata