1. Protus 2.0: Ontology-based semantic recommendation in programming tutoring system Presentor: Boban Vesin Boban Vesin, Aleksandra Klašnja-Milićević Higher School of Professional Business Studies Novi Sad, Serbia e-mail: {vesinboban, aklasnja}@yahoo.com Mirjana Ivanović, Zoran Budimac Department for Mathematics and Informatics Faculty of Science, Novi Sad, Serbia e-mail: {mira, zjb}@dmi.uns.ac.rs Opatija, Croatia, 2012.

2. 2 Contents Introduction Personalization of content Used technologies Protus 2.0 architecture Ontologies in Protus 2.0 Implemented rules Learner’s interface Conclusion

3. 3 Introduction Semantic Web technologies Educational environments Ontologies Ontologies provide a vocabulary of terms whose semantics are formally specified Ontologies need additional rules to make further inferences Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

4. 4 Introduction The major goal of learning systems is to support a given pedagogical strategy Ontologies can be associated with reasoning mechanisms and rules to enforce a given adaptation strategy in learning system Protus - PRogramming TUtoring System Adaptation of the teaching material and navigation in a course based on the principles of Learning styles recognition for a particular learner The main objective of the presentation is to present new version of Protus that completely relis on Semantic web technologies Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

5. 5 Personalization of content Customization of content to match characteristics specified by the learner model Protus 2.0 provides two general categories of personalization based on recommender systems –Content adaptation –Learner interface adaptation Adaptation based on the learning style of the learner Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

6. 6 Learning styles identification Index of Learning Styles (ILS) ILS assesses variations in individual learning style preferences across four dimensions or domains: –Information Processing: Active and Reflective learners, –Information Perception: Sensing and Intuitive learners, –Information Reception: Visual and Verbal learners, –Information Understanding: Sequential and Global learners. Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

7. 7 Characteristics of learners Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

8. 8 Used technologies OWL - Ontology Web Language Protégé - ontology editor –SWRLTab SWRL - Semantic Web Rule Language Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

9. 9 Protus Different courses and domains Highly modular architecture Five central components: –the application module –the adaptation module –the learner model –session monitor –domain module Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

10. 10 Overall architecture of Protus Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

11. 11 An excerpt of domain ontology Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

12. 12 An excerpt of resource topology Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

13. 13 Learner model ontology Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

14. 14 Ontology for learner observation Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

15. 15 Teaching Strategy ontology Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

16. 16 Implemented rules In Protus: –the interface elements for sequential navigation are hidden/shown –Different presentation methods –Adding of links to related or more complex content Three groups of rules: –learner-system interaction rules –off-line rules –recommendation rules Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

17. 17 Examle of implemented rules The form of the rules: antecedent -> consequent Following rule updates learner model: Learner(?x)  Interaction(?y)  hasInteraction(?x,?y)  Concept(?c)  conceptUsed(?y,?c)  Performance(?p)  hasResult(?y,?p)  hasGrade(?p,?m)  swrlb:greaterThan(?m, 1)  isLearned(?c, true)  hasPerformance(?x,?p) Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

18. 18 User Interface of Protus Web pages for students –online tutorial with numerous resources –testing knowledge –communication with teachers and other students Learning styles identification Initial assessment is based on the ILS Questionnaire Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

19. 19 ILS Questionnaire Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

20. 20 Result of ILS questionnaire Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

21. 21 Information Processing: User interface for Activists User interface for Reflectors Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

22. 22 Information Perception Recommendation of Additional material option for Sensing learners Recommendation of Syntax rules option to Intuitive learner Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

23. 23 Information Reception: Example of lesson for Visual learners Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

24. 24 Information Reception: Example of lesson for Verbal learners Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

25. 25 Information Understanding Elements for Global Learners Navigation for Sequential learners Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

26. 26 User interface of Protus 2.0 Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

27. 27 Conclusion We presented how Semantic Web technologies and in particular ontologies can be used for building Java tutoring system Architecture for such adaptive and personalized tutoring system that completely relies on Semantic Web technologies was presented Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion

28. Protus 2.0: Ontology-based semantic recommendation in programming tutoring system Presentor: Boban Vesin Boban Vesin, Aleksandra Klašnja-Milićević Higher School of Professional Business Studies Novi Sad, Serbia e-mail: {vesinboban, aklasnja}@yahoo.com Mirjana Ivanović, Zoran Budimac Department for Mathematics and Informatics Faculty of Science, Novi Sad, Serbia e-mail: {mira, zjb}@dmi.uns.ac.rs Opatija, Croatia, 2012.