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Protus 2.0: Ontology-based semantic recommendation in programming tutoring system

Opatija, Croatia, 2012. Boban Vesin, Aleksandra Kla šnja-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

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Protus 2.0: Ontology-based semantic recommendation in programming tutoring system

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  1. Opatija, Croatia, 2012. 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 Protus 2.0: Ontology-based semantic recommendation in programmingtutoring system Presentor: Boban Vesin

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

  3. Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion 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

  4. Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion 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

  5. Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion 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

  6. Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion 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.

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

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

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

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

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

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

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

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

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

  16. Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion 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

  17. Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion 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)

  18. Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion 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

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

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

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

  22. Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion Information Perception • Recommendation of Additional materialoption for Sensing learners • Recommendation of Syntax rulesoption to Intuitive learner

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

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

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

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

  27. Into Personalisation Technologies Arcitecture Ontologies Rules Interface Conlusion 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

  28. Opatija, Croatia, 2012. 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 Protus 2.0: Ontology-based semantic recommendation in programmingtutoring system Presentor: Boban Vesin

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