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Combining ITS and eLearning – Opportunities and Challenges

Combining ITS and eLearning – Opportunities and Challenges. German Reseach Center for Artificial Intelligence (DFKI) ARIES Lab, University of Saskatchewan, Canada. Deutsches Forschungszentrum für Künstliche Intelligenz. Erica Melis , Jim Greer , Christopher Brooks, Carsten Ullrich.

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Combining ITS and eLearning – Opportunities and Challenges

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  1. Combining ITS and eLearning – Opportunities and Challenges German Reseach Center for Artificial Intelligence (DFKI) ARIES Lab, University of Saskatchewan, Canada Deutsches Forschungszentrum für Künstliche Intelligenz Erica Melis, Jim Greer, Christopher Brooks,Carsten Ullrich

  2. Why think about it again? Web technology + commercial Application-oriented Very wide-scale usage Broad audience Standardization Potential collaborative authoring Efficient organization of learning Usage of communication tools • CogSci + AI community • Research-oriented • Lab evaluations • Restricted audience • “one-offs” • Few authors, designers • Individual tutoring • CSCL ITS eLearning

  3. Make eLearning more intelligent Make ITS more open OneSizeFitsAll Simple feedback Not much data Little cognition and learning - Weak community tools Massive multi-media content Content-based ontologies Common communication tools Accessibility Service-approach Reusable components Scalability ITS eLearning • Student modelling • Feedback generation • Diagnosis • Meta-cognitive support • Cognition and learning • Support of learning activities • Special content • CSCL • Fix abstract domain map • Clearly defined audience • Single use components/system • Little scalability

  4. (Le)ActiveMath: eLearning • Multi-lingual • Integrates ITS components (Siette, iCMap, ..) • One central student model • Integration with LMS • Web presentation (of mathematics) • KR: Semantic XML-representation for maths+ • Semantic search

  5. (Le)ActiveMath: ITS • Adaptivity through instructional planning • Suggestions via „agents“ • KR: misconceptions, competencies, dependencies • Ontologies: content+topic map • Individual feedback (authored and generated) • Tutorial dialogues for symb differentiation • Interactive concept mapping • Lab evaluations • Large classroom evaluations

  6. ActiveMath: Contents LeActiveMath Calculus Universität Augsburg University of Glasgow 150 students… 300 pages de, en, es Statistics HTW Saarland 50 students, 200 pages de Optimization, Operations Research Mary State University 100 pages ) 3x50 students ru, en 1st year Calculus U. Westminster, London 50 pages (exc) 250 students en Algebra Interactive! Arjeh Cohen TUe 30 pages en Analysis Individuell Uni Koblenz Uni Saarland 20 students 300 pages de Matheführerschein FH Dortmund 3 schools 50 pages 100 exercises de Fractions Gesamtschule Bellevue 100 pages 70 students de

  7. iHelp: eLearning • Scorm compliant LMS • Powerful RBAC with IMS-SS • Wealth of learner data • Data mining • Semantic Web application • Integrates collaboration tools • Uses LMS as LOR

  8. iHelp: ITS • Data mining for metadata • Ecological approach • (learning objects in context) • Selecting relevant learning objects • Expertise location • Adaptive content modules • Awareness support • for collaboration • for competition

  9. Challenges: reuse and interoperability • Distributed architecture • Distributed content • Collaborative authoring • Make available and reuse domain reasoners • Use blackbox services

  10. Challenges: knowledge representation • Collaborative authoring… • 2-level ontologies • Ontology-mappings • Ontologies in context, contextualized metadata • Semantic versioning • Learning metadata • Validate metadata • Extensibility of representations

  11. Challenges ITS Decision Analysis Semantic Web Intelligent LMS Learning Object & Metadata Pedagogical Principles Recommender Systems Instructional Design

  12. Challenges: ?? • Support critical thinking • And other meta-cognitive competencies • Self-organzational models of learning

  13. Conclusion • Mutual benefits and chances to get widely used • New challenges • Bridge the gap!

  14. OMDoc Knowledge Representation <definition id="monoid/def_monoid" for="monoid" <metadata> <depends-on> <xref theory="structures/structure" /> </depends-on> <Title xml:lang="en">Definition of a monoid</Title> </metadata> <CMP xml:lang="en" format="omtext"> A monoid is a <ref xref="structures/def_structure"> structure </ref> <OMOBJ> <OMA> <OMS cd="elementary" name="ordered-triple"/> <OMV name="M"/> <OMS cd="semigroups" name="times"/> <OMS cd="semigroups" name="unit"/> </OMA></OMOBJ> in which <OMOBJ> <OMA> <OMS cd="elementary" name="ordered-pair"/> <OMV name="M"/> <OMS cd="semigroups" name="times"/> </OMA> </OMOBJ> is asemi-group with<ref xref="semigroups/def_unit">e</ref> <OMOBJ> <OMS cd="semigroups" name="unit"/> </OMOBJ>. </CMP> <FMP><OMOBJ> ... </OMOBJ></FMP> </definition>

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