Problem-based learning in formal and informal learning environments

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Problem-based learning in formal andinformal learning environmentsGoran Shimic a & Aleksandar Jevremovic ba Military Academy , Belgrade , Serbiab Singidunum University , Belgrade , SerbiaPublished online: 12 Jul 2010.

To cite this article: Goran Shimic & Aleksandar Jevremovic (2012) Problem-based learning informal and informal learning environments, Interactive Learning Environments, 20:4, 351-367, DOI:10.1080/10494820.2010.486685

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Problem-based learning in formal and informal learning environments

Goran Shimica* and Aleksandar Jevremovicb

aMilitary Academy, Belgrade, Serbia; bSingidunum University, Belgrade, Serbia

(Received 3 January 2009; final version received 28 March 2010)

Problem-based learning (PBL) is a student-centered instructional strategy in whichstudents solve problems and reflect on their experiences. Different domains needdifferent approaches in the design of PBL systems. Therefore, we present one casestudy in this article: A Java Programming PBL. The application is developed as anadditional module for the Learning Management System (LMS). This way the LMSis extended by PBL functionality and the LMS learning resources can be used in PBL.

Keywords: problem-based learning; intelligent tutoring systems; learningmanagement systems; web applications

Introduction

This article describes a case study in self-directed problem-based learning (PBL).PBL is a student-centered instructional strategy in which students (individually ororganized in groups) solve problems and reflect on their experiences. PBL is stronglyfounded in specific domain expertise and it emphasizes critical thinking and problemsolving skills with students.

Different domains need different approaches in the design of PBL systems. Acommon property of these systems is the existence of a problem which learners needto solve using certain procedural knowledge. This is the starting point of the researchpresented here.

In the next section, we give a brief overview of past research pertaining to thedesign and development of PBL systems. Section 3 presents the motivation. Systemdesign and architecture are analyzed in Section 4, and the case study is described inSection 5. Section 6 presents the evaluation of the system.

PBL in e-learning

Intelligent Tutoring Systems (ITS) in many domains provide examples of computersupported PBL. There are a number of PBL ITS in medicine. For example, SlideTutor(Crowley & Medvedeva, 2006) is designed to aid in learning dermatopathology.Students make their own hypotheses about the assigned problem. They choose themost appropriate concepts from the domain ontology and try to prove their hypothesis.CIRCSIM-Tutor (Mills, Even, & Freedman, 2004) teaches cardiovascular physiology

*Corresponding author. Email: [email protected]

Interactive Learning Environments

Vol. 20, No. 4, August 2012, 351–367

ISSN 1049-4820 print/ISSN 1744-5191 online

� 2012 Taylor & Francis

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by Socratic dialogs with the student to help him or her with reasoning towards thecorrect solution. In the field of mathematics tutoring, Cognitive Tutor (Anthony, Yang,& Koedinger, 2005) is a well-known tool which builds a cognitive model of a student ashe or she interacts with the application. Cognitive Tutor profiles the students andindividualizes instructional interaction by checking their solutions of given problems. InKERMIT (Knowledge-Based Entity Relationship Modeling Intelligent Tutor)(Suraweera & Mitrovic, 2004), PBL and student modeling is implemented byconstraints. In addition to traditional applications, and in the spirit of Web 2.0,some PBL systems are accessible over the Internet. For example, ELM ART (Weber &Brusilovsky, 2001) is a Web-based tutor designed for assisting in learning the LISPprogramming language. ELMART problem adaptation is based on an episodic learnermodel. An episode represents a part of the learning session related to a specific problemand to the learner’s attempts to solve this problem.

The adaptation of the PBL process in ITS is based on a specialized learnermodel. The adaptation means that the problem, which an ITS assigns to thelearner, is changeable according to the actual state of the learner model. In otherwords, the model will be changed every time the ITS processes the learner’ssolution.

Learning management systems (LMS) represent the most widely used e-learningsystems over the Web. LMS improve both the self-paced and the instructor-ledlearning processes (Beck, Stern, & Haugsjaa, 1996). Unfortunately, these systems donot support PBL as it is implemented in many ITS. LMS are designed to be domainindependent, with a rich palette of administration functionalities. They are focusedon delivering reusable, well-structured learning content. Therefore, it is usually thedeclarative knowledge of the individual learner that can be evaluated in LMS.

Motivation

The aim of the research described in this article is to develop a separate softwaremodule which supports PBL in a specific LMS. Based on the actual teaching needs ofa class at Singidunum University, a course in Java programming is considered.

Java is one of the most popular programming languages, but there are just a fewsystems for Java tutoring on the Web. Some of them are focused on learning specificskills. For example, Swing Tutor and AWT Tutor (http://nasa1.ifsm.umbc.edu/learnJava/tutorLinks/) are designed for learning interface programming by usingJava GUI classes. There are certain shortcomings to using these systems. Learnersare not able to navigate freely through the system. They are led step by step throughthe learning space without the possibility of navigating back. Neither does the systemshow the ‘‘whole picture’’ of the course (structure, organizations, tasks and goals),nor can the students see their results from previous attempts. The non-intuitive andcomplex user interface represents additional disadvantages of these two tutors.

Another example of Java tutoring is Java Sprint (http://www.javasprint.com/) –the commercial pluggable component for the Eclipse IDE. Unlike the previousexamples, Java Sprint presents the most important Java programming concepts.Unfortunately, this tool cannot be used over the Web and is designed for just oneclient per application. The dependence on the Eclipse platform represents anotherdownside to using this tool.

The facts mentioned above motivate us to develop custom components for a Javaprogramming PBL. For research purposes, we have tested a new approach in system

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http://nasa1.ifsm.umbc.edu/learnJava/tutorLinks/


http://www.javasprint.com/

design. As an added benefit, this new approach provided us with enhancedpedagogical capabilities.

The PBL system design

General model

The general architecture of a PBL system represents the starting point of our research.Every PBL system is comprised of at least five modules. Learners interact with thesystem through the communication module. This is the most frequently mentionedmodule in the architecture of e-learning systems (Brusilovsky, 2003). The problemgenerator and the problem solver represent the other necessary modules in PBLsystems. The former generates the problem for the learner, while the latter generatesthe system solution based on each delivered problem. The expert module checks thelearner solution by comparing it with the system solution. The differences are used fordiagnosing and for updating the state of the learner model. This is the responsibilityof the expert module. In the next iteration, the problem generator modifies theproblem based on the changes in the learner model. This means the learner modelmakes the system adaptable according to the learner’s actual needs.

Extended model

Note that the basic architecture does not contain any explicit mechanisms that canprovide help to the learner. Short feedback messages or other similar mechanismsare often used in this case. If the PBL system is a part of another system (e.g. anLMS), the environmental system learning resources can be used for this kind ofsupport. In this case, the basic architecture has to be extended with some newconcepts (Figure 1).

These concepts are designed according to the actual e-learning standardsimplemented in most LMS (SCORM 2004 specification, IMS CP reference).

Figure 1. The additional concepts which improve contextual help.

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The item represents the organization unit of the course (learning unit). The item hasa goal, which is about mastering the specific skill. Also, the learner state is describedby his or her skills.

If the problem is related to the course item, the variety of problem types will beproportional to the course structure. More items need more problem types. On theother side, the help system will be more effective if the item resources describe smallerpieces of knowledge. This way, help becomes more contextual.

When learners use the PBL system, their state is in constant change. The systemhelps each learner with a specific type of problem by using the resources which arerelated to the same item that the problem pertains to. Help is also harmonized withthe learner’s state. Since the state is described by skill level, the system tries to findthe most appropriate resources to improve the current level. Also, the system tries toformulate a diagnosis of a concrete problem. The reasoning engine is used forperforming these advanced tasks.

Self-directed PBL in Java programming

The design of a Java programming PBL system is based on the language buildingblocks. The class (UDClass) represents the main language concept (Figure 2). Itconsists of data members (attributes) and function members (methods). Since ourresearch is focused on basic programming skills, learners will need to be capable ofdefining and using the variables and methods of the Java programming language.

To enable computer analysis of student solutions and the creation of the correctsolution, it is necessary to structure and decompose problems. For this purpose, theconceptual model that supports structuring and decomposing is developed. Themethod is divided into one or more blocks. Different types of methods exist,depending on visibility, number of arguments, return value, and method content.They may consist of functional calls – other methods, defined in the same class or inother classes. Four block types are considered: Sequential, conditional, cyclic, and

Figure 2. Concepts used in the Java programming PBL.


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single statement blocks. Likewise, blocks may contain embedded blocks. Also,blocks consist of different types of statements: Declarations, definitions, valueassignments, and examinations, etc. Each statement is made up of elements (e.g.variable types, names, values, and different kinds of operators).

Problem types

Different problem types are defined in relation to the concepts mentioned above.Moreover, problems can be layered by their complexity. Figure 3 depicts therelations between the problem types and programming concepts. The basic levelproblems are related to the basic statements such as variable or array definitions. Thevalue assignments and data retrieval are also featured in basic level problems.

The second level of problem types includes the implementation of different blocks(sequential, cyclic, etc.). The whole method implementations are included in the 3rdlevel problems. There are several problem types depending on the method properties(e.g. type and number of arguments, required functionality, and return values). Thehighest level problems require definition of the whole class (or classes) to accomplishmore general functionalities.

The class content is hierarchically structured (Figure 3). Every block and everymethod generally consists of a declaration and implementation part (named headerand body). This property is crucial for generating the system solution and foranalyzing the learner solution.

The system generates the problems dynamically. Names, types, and values (of thevariables, methods, and classes) are defined randomly. These data represent theproblem parameters, where a sample problem may consist of three or moreparameters. The system uses the data to create the system solution and to check thelearner solution.

Figure 3. Sample class concepts used in the analysis.


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For example, one of the 3rd level problem types is the single argument methodwith the IF clause. This kind of problem consists of eight parameters: The methodreturn type and name, the argument type and name, the criteria value, logicaloperator type (used in IF clause), and two return values (when the IF clause is trueand when the IF clause is false). The learner solution diagnostics in the PBL systemare based on checking the presence of these parameters.

Processing of learner solutions

The PBL system processes a learner solution through several phases (Figure 4).Solution embedding and compiling are the first two phases of learner solutionprocessing.

Embedding means the system completes the learner solution with the necessarycode in order to compile it. The solutions of the first level problems (see previousparagraph) have to be embedded in the empty class declaration. The second levelproblem solutions require double embedding – by method and (method) by class.The 3rd level solutions have to be wrapped in a class.

The system checks the solution syntax by compiling the code. If the compilerdoes not return any error messages, then the solution syntax is correct. On the otherhand, the PBL system processes the errors by catching the diagnostics (javax.tools.-Diagnostic) returned from the compiler and customizes them according to thelearner’s needs.

If the solution syntax is correct, the PBL system performs the next phase –invoking and testing. Java reflections classes (Forman & Forman, 2005) are usedfor this purpose (java.lang.reflect package). These classes provide the means forinstantiating and inspecting the user defined classes. The system is able to checkthe declarations (types and names of the class members) as the method behavior.

The 2nd and higher type solutions are tested by invoking the requested methods.Then the PBL system passes different parameter values to the method, picks up theresults, and compares them with the expected values. If compared values do notmatch, the system performs the highest level checking – semantic checking of thecode inside the method. In this case, the method body is parsed (in the manner

Figure 4. The learner solution processing.


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described in the section Problem types) and processed by the appropriate expert class(Figure 5).

The expert plays the role of a dispatcher by recognizing the problem type andforwarding the input data to the specialized expert. It compares the solutions andcreates the diagnosis. The feedback engine requests the links of the item resourcesbased on the problem and the diagnosis. Finally, the PBL system delivers to thelearner the feedback messages, the helpful links and the system solution.

Usage example

The complexity of the PBL system structure has no impact on the user interfacesimplicity. The learner is free to navigate through the PBL resources. If learners wantto browse the learning (help) material first, they can do so directly. Otherwise, if theywish to practice programming skills then links to different problem types areavailable (Figure 6). In the practicing mode, the learner’s first step is to choose theproblem type.

In the next step, the PBL system generates and delivers the appropriate problem(Figure 7). If the learner tries to reload the page, a new problem will be generated.This way, the learner is directed to use the system in the appropriate way. Same types

Figure 5. Mechanism of detailed method checking.

Figure 6. Choosing the problem type.


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of problems have similar complexity. This means reloading the problem, withouttrying to solve it, is a useless strategy for the learner.

Learners attempt to create their own solution in the 3rd step. They enter the solutioncode by using the simple text editor. This way the learner is forced to adopt the languagesyntax precisely. At the same time, the system provides the learner with contextual help.If learners suspect that the solution is not written correctly, they can access contextualhelp via the help button on the same page. This help is displayed in a separate windowas a distilled learning content related to the concrete problem type (Figure 8).

After users submit their solution, the system processes it in the mannerdescribed in the section Processing of learner solutions. Although the exampleshown (Figure 9) is free of syntax errors (no compiler messages), the learnermethod does not return the expected results, and the PBL system performs amore detailed check. The result of this process is feedback which contains adetailed description of semantic errors. The method declaration and methoddefinition are considered separately in the system. If the learner solution containserrors in the declaration part, the definition will not be considered. The parts ofthe method definition are handled similarly. This approach provides two benefits:The programming skills of the learner grow gradually, and the process of detailedsolution analysis generally consumes less time.

In the last step of an iteration, learners can read the feedback and can comparetheir solutions with the system solution. Learners can also use contextual help and/orrequest the next problem of the same or different type in the same form. All supportfrom the system is available from a single, immediately accessible source. We believethis to be one of the most important advantages of the Java PBL system.

Figure 7. Solving the problem.


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Evaluations

We have available two phases of Java PBL system usage. In the first phase, it wasmade accessible just through the university intranet. We used these periods toevaluate the system performance and to discover the benefits which it provides forthe students in formal learning environments.

Usage in formal learning environments

At the beginning of the two months long course, students were divided into twoequal groups. Group G1 (33 students) was experimental, and group G2 (33students) control. Group G1 used the system during the lab exercises, and groupG2 did not. Both groups were chosen from students who regularly attend classes.In the second half of the course (after they have finished the content covered inthe system), both groups of students were able to attend the experimental test.The experimental test was performed in a controlled environment and lasted 2 h.Every student was able to attend the experimental test in one term (terms wereET1 to ET4, in different weeks during the second month of the course), accordingto personal preference (Figure 10).

The experimental test included contents that are covered by all types of tasks inthe PBL system (e.g. the tasks about array manipulation, cyclic structures, andconditional branching were included in all tests). The next table (Table 1) presentsthe final results of both groups.

Themean of the experimental groupwas 7.79,with a standard deviation of 0.96 andvariance of 0.92. For the control group, the mean was 7.12, the standard deviation was0.99 and the variance was 0.98 (for a statistically significant p – value less than 0.01with64 df), Students from the treated group preferred early test terms (E1 and E2), whilestudents from the control grouppreferred the last two test terms (E3 andE4).Althoughthis raises some questions about the comparability of the two groups since they useddifferent tests, the tests were designed to be comparable. We interpret this behavior to

Figure 8. Contextual help.


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mean that students from the treated group had more confidence in their Javaprogramming knowledge, while students from the control group needed more time toprepare.

Regardless of the results, the strongly controlled conditions (by the teacherassistant) in the limited space (computer labs), during the short period of time (one ortwo classes), represent the main disadvantage of the internal usage of the PBL system.

Figure 9. Feedback messages.

Figure 10. Course timeline.


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Table 1. Results of students from experimental (G1) and control (G2) groups with t-test.

Final students results

Group 1 Group 2

Student Result Student Result

S 101 9 S 201 8S 102 8 S 202 8S 103 9 S 203 7S 104 7 S 204 6S 105 8 S 205 7S 106 7 S 206 6S 107 8 S 207 7S 108 7 S 208 8S 109 9 S 209 6S 110 7 S 210 7S 111 8 S 211 7S 112 9 S 212 6S 113 8 S 213 6S 114 8 S 214 6S 115 8 S 215 6S 116 6 S 216 9S 117 7 S 217 8S 118 7 S 218 8S 119 7 S 219 7S 120 8 S 220 7S 121 9 S 221 7S 122 6 S 222 8S 123 8 S 223 8S 124 7 S 224 9S 125 8 S 225 8S 126 6 S 226 7S 127 8 S 227 6S 128 7 S 228 6S 129 8 S 229 9S 130 9 S 230 8S 131 8 S 231 7S 132 8 S 232 6S 133 10 S 233 6

Intermediate values used in calculation

Mean 7. 79 Mean 7. 12SD 0.96039 SD 0.9924SEM 0.17 SEM 0.17N 33 N 33Standard error of difference 0.240DF 64I 2.7731

Confidence interval

G1M–G2M 0.67Cont.int (95%) 0.1940–0.15

p value and statistical significancep value 0.0073Significant Very


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In the second phase, Java PBL was released as a free resource on the Internet(http://pbl.singidunum.ac.rs). Our intent was to gain knowledge of the behavior of awider group of users in less controlled conditions than those of a typical classroom.In this situation, the Java PBL system practically represents the informal learningenvironment. Until now, more than five hundred users from 21 countries have usedthe system. Their logs provide us with the ability to perform different kinds ofresearch and draw various conclusions.

Usage of learning materials

Problem solving represents the main activity during the PBL process. The learningmaterials provide an additional resource in this process. First, the learners are presentedwith the problem. On the same form they follow the link to the appropriate learningmaterial (contextual help). These materials are divided in four categories:

Primitive typesArraysBranched structuresCyclic structuresThe usage of these four groups is nearly equal (Figure 11), but the detailed

analysis of the ways in which the learners accessed the materials produces differentresults.

For example, 51% of the learners had used the materials about the branchedstructures (Figure 12) through the problems about the basic IF clauses (method’s

Figure 11. The overall usage of the learning materials.

Figure 12. IF clause learning material vs. problem types.

Figure 13. Cyclic structure materials vs. problem types.


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http://pbl.singidunum.ac.rs

Figure

14.

Pattern1–problem

solver.


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argument has to be compared with proposed criteria). From the other IF clauseproblem types this percentage is less (just 5% and 11%).

This means the learners try to learn gradually. They mostly use the learningmaterials in the beginning, when they try to solve the basic problem types. Later,the learners deal with the more complex types by using adopted knowledge. Thelast piece of the pie is about the problems with the switch-case structures.The switch-case branched structure differs from others and therefore these materialsare also frequently used (about 33%). A similar pattern is followed in analyzing thematerials about arrays (declaration, definition, manipulation of the array elements).They used materials for the simplest problem type (37% of the four problem types)the most.

Another interesting example is the usage of learning materials about cyclicstructures (Figure 13). These kinds of problems are more complex thanprevious ones.

There are three types of cyclic structures implemented as problem types in theJava PBL system. They are of approximately equal complexity. Nevertheless, themost frequently used material is about the DO-WHILE loops (40%). Byanalyzing the errors in the learner solutions we arrive at the following conclusion:The DO-WHILE loops have a slightly more complex syntax compared to othertypes.

On the other side, the least used material is about the WHILE loops (24%). Thiswas expected since this kind of cyclic structure bears most similarity to itscounterparts in other programming languages. Therefore, the learners with previousprogramming experience can easily master WHILE loop problems.

The learners’ behavior

Three behavior patterns were recognized by analyzing user actions logs. Thesepatterns cannot be used as explicit user models or profiles. Nevertheless, they aresimilar to the episodic learner model and are useful for research purposes.

Figure 15. Pattern 2 – passive learner.


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The pattern 1 problem solver tries to solve the problem without reading thematerials first (Figure 14).

Pattern 1.a – with many iterations; session of 10–30 minutes of exercise fordifferent types of problems.Pattern 1.b – the initial search for a known problem; the user tries to find theproblem he or she can address. If such a problem is found and successfullysolved, the user moves on to the next problem type. The user is trying to solve aproblem in several attempts. Also, the user is not using learning materials untilafter an incorrect solution.Pattern 1.c – within 1–2 iterations; short sessions of exercises for a specific typeof problem.The Pattern 2 passive learner opens the problem(s) and the related learning

material without attempting a solution (Figure 15).Pattern 2.a – only open problems of same or similar type, in several sessions oneafter another, in a few minutes – probably using offline learning, without anyuser feedback.

Figure 16. Pattern 3 – learner and problem solver.


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Pattern 2.b – open problems with different types of materials for learning, in asession focusing on learning.The Pattern 3 learner and problem solver tries to solve the problem by using

related learning materials; first the system displays the problem, than the user readsrelated material, and finally tries to arrive at a solution. If a solution is correct, thenthe user switches to a different problem type. Otherwise, the user repeats theprevious steps (Figure 16).

Conclusion

The Java PBL system proved very useful in acquiring basic skills of Javaprogramming. It provides much faster student progress as demonstrated byhypothesis confirmation for the summary results of testing. Inconsistency ofhypothesis confirmation in the last two exams indicates that the conventionalpractice shows competitive results in the long run. Eventually, the effects of JavaPBL are lost in favor of practical training exercises with the assistance of a teacher.This weakness is partly due to a limited group of problems that are used duringthe experiment and that become obsolete in the later period of exercise (terms 3and 4).

Another important consideration is about the learner behavior. Threebehavior patterns are recognized. In the first, problem solvers have learnedJava by multiple attempts on the same type of problems. The second recognizedpattern is the passive learner. This type of user simply reads the problems andlearning materials without attempting to produce a solution. The third recognizedpattern can be named ‘‘learner and problem solver’’. This type of user isemploying the same navigation path for each problem type. He opens theproblem, learns related material and tries to give the correct solution. These threepatterns indicate the different learning styles used in PBL. This is anotherconfirmation that Java PBL system can be successfully used for different learningstyles. Our interpretation is that the learners search for the least time consumingway to access the programming skills they need. They want to spend minimumtime, but do as much as possible; and they do not have the patience to read thebooks and look for the content related to the posed problem.

The next conclusion is about the learning materials. These materials represent themain learning resource in the LMS. Their role is changed in Java PBL. Users findthat they are useful in a practical learning context because they are directly related tothe problems. They are accessible during user attempts to solve the problems. Themost frequently used materials are about the simplest and the most complexproblem types.

Our study shows that the PBL system can be incorporated in an LMS. The actualLMS does not have support for the PBL. Owing to modular architecture andstandardization of the learning resources, these two kinds of systems can coexist witheach other. The LMS learning resources are designed to provide the learners withdeclarative knowledge (definitions, explanations, illustrations, lectures). However,this kind of knowledge (know-what) is not helpful enough with solving the concretedomain problems (know-how). On the other side, if the learner does not have enoughdeclarative (descriptive) knowledge, he or she will not be able to perform tasksrelated to this knowledge.


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Notes on contributors

Dr Goran Shimic is Assistant Professor at the Military Academy, Belgrade, Serbia, and amember of the GoodOldAI research network. (E-mail: [email protected], URL: http://goodoldai.org/goran_simic).

Aleksandar Jevremovic is Teacher Assistant of Computer Networks and Artificial Intelligence,Department of Computer and Information Science, Singidunum University. (E-mail:[email protected]).

References

Anthony, L., Yang, J., & Koedinger, K. (2005). Evaluation of multimodal input for enteringmathematical equations on the computer. In ACM Conference on Human Factors inComputing Systems (CHI 2005) (pp. 1184–1187). New York, NY: ACM.

Beck, J., Stern, M., & Haugsjaa, E. (1996). Applications of AI in education. ACM Crossroads,3(1), 11–15.

Brusilovsky, P. (2003). A distributed architecture for adaptive and intelligent learningmanagement systems. In AIED03 online supplementary proceedings, Vol.4: Workshoptowards intelligent learning management systems. http://www.it.usyd.edu.au/*aied/vol4/vol4_brusilovsky.pdf

Crowley, R., & Medvedeva, O. (2006). An intelligent tutoring system for visual classificationproblem solving. Artificial Intelligence in Medicine, 36(1), 85–117.

Forman, I., & Forman, N. (2005). Java reflection in action. Greenwich, CT: ManningPublications Co.

IMS CP reference. Retrieved from, www.imsglobal.orgJava pluggable component for Eclipse IDE. Retrieved from http://www.javasprint.com/Java tutoring systems: Swing Tutor and AWT Tutor. Retrieved from http://nasa1.ifsm.umbc.

edu/learnJava/tutorLinks/Mills, B., Even, M., & Freedman, R. (2004). Implementing directed lines of reasoning in an

intelligent tutoring system using the atlas planning environment. In Internationalconference on information technology: ITCC 2004 (pp. 729–733). Washington, DC:IEEE Computer Society.

SCORM 2004 specification. Retrieved from www.adlnet.govSuraweera, P., & Mitrovic, A. (2004). An intelligent tutoring system for entity relationship

modelling. International Journal of Artificial Intelligence in Education (IJAIED), 14, 375–417.

Weber, G., & Brusilovsky, P. (2001). ELM-ART: An adaptive versatile system for web-basedinstruction. International Journal of Artificial Intelligence in Education, 12, 351–384.

Welch, B.L. (1947). The generalization of ‘‘student’s’’ problem when several differentpopulation variances are involved. Biometrika, 34, 28–35.


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Problem-based learning in formal and informal learning environments