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Dr. habil Erica Melis ActiveMath- Group Deutsches Forschungszentrum

Educational Technologies WS2006. Dr. habil Erica Melis ActiveMath- Group Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI). About the Field and the Course. Intelligent assistent systems for learning components of ITSs AI-techniques and related ones Practical applications

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Dr. habil Erica Melis ActiveMath- Group Deutsches Forschungszentrum

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  1. Educational Technologies WS2006 Dr. habil Erica Melis ActiveMath- Group Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)

  2. About the Field and the Course • Intelligent assistent systems for learning • components of ITSs • AI-techniques and related ones • Practical applications • Interdisciplinary and empirically validated • Learn actively!!! • Test described software on Web if available • Make suggestions yourself • Hands-on experience and authoring in projects

  3. Scheme of the Course • http://www.activemath.org/teaching/edtechsws0607 • register with Matrikelnummer • Projects • Start as soon as possible • Author interactive script in ActiveMath • Inform george@activemath.org about groups til end of week • Not everything on the slides…

  4. Approximate Plan of the Course 18.10.Introduction and overview 25.10.Introduction to ActiveMath XML- Knowledge Representation 8.11. Student Modelling 15.11. Web technologies and security 22.11. Tutorial Planning and instructional design 29.11. Media Principles 6.12. Interactive exercises 13.12. Authoring tools, CTAT 20.12. Diagnosis: model tracing and domain reasoning 10.1. Diagnosis: constraint based 17.1. Tutorial dialogues 24.1. Action analysis and ML techniques 31.1. Cognitive tools 7.2. Meta-cognitive support 14.2. student projects

  5. Independent of time & place Individual tutoring better learning (modalities visualization..) (Semi)-automatic assessment Information for teachers cost effective Knowledge resources from the Web Distance learning Virtual Universities Training on the job Military training Training for disabled Why Technology-Enhanced Learning ?

  6. Admitted Not Admitted Data1：From “Statistics Bulletin on Economic and Social Development in P.R.China 2004 ” Admission Proportion for High Education 19% 4,200,000 81% 17,905,000

  7. Data 2：From CNNIC (China Network Information Center) “Statistic Report on the Development Status of Network in China ” Increment Rate 18.9% Million Person 94M 199M in America From ComScore 79M 69M 45.8M 26.5M 16.9M

  8. History: First Generation of Tutors, CAI 1960ies: Programmed instruction 1970ies: CAI.. PLATO, SHIVA • IF the correct response THEN present new element ELSE goto • Computer-Aided Instruction (CAI) or CAL • store and retrieve data, exercise bank with answers • pre-defined branches of problem solving • no ‚understanding‘ of problems, few anticipated wrong answers • Independent of student‘s understanding, preferenes, behaviour • linear (not individualized) progression of instruction • no diagnosis of errors

  9. Internal domain representation knowledge base Problem solver, inference engine (XPS) -> cause of errors -> more appr. response Exercise bank Limited dialogue and QA Student modeling Domain Expert Module Model learners‘ errors Tutoring module(intervention modalities) Ped+cognitive theories developed more autonomous student Lab- and realistic evaluations bandwidth of user interface, more variety of responses more interaction History: Second Generation of Tutors, ITS 1970ies…Scholar [Carbonell,Brown] 1990ies…PACT [Anderson]

  10. Exercise bank CAI…ITS1 Architectures Knowledge Base Exercise bank Expert system selector User interface

  11. History: Third-Generation Learning Systems • More student modeling: emotional, motivational, affective, situational learning from massive log data • Natural language tutorial dialogues • Explorative, interactive, inquiry learning • Collaborative learning • Support of meta-cognition • Web-based systems • Multimedia and (adaptive) hypermedia based on pedagogy • Semantic knowledge representation (semantic web) • Retention tests, social skills, performance/learning • AHA, Tectonica, ActiveMath, ELM-ART, Edutella, Wayang Outpost, iHelp, Algebra Cognitive Tutor, BEETLE, Help Tutor

  12. Graphical user interface A Generic ITS Architecture IntelligentTutorial Component Curriculum Planner Problem Selector Problem Solver Domain KR Student Solution Graph Curriculum Action Interpreter Solution Evaluator Interaction History Student Model Feedback Generator

  13. Graphical author interface Andes Architecture Authoring Environment Student Environment Workbench Problem Presentation Assessor (BN) Physics Rules Action Interpreter Physics Problem Solver Solution Graph Student Model Problem Definition Help System tutoring strategy Procedural help Conceptual help Example study help

  14. ActiveMath MVC Architecture

  15. Some Intelligent Systems • Cognitive Tutors (Koedinger et al) • ELM-ART (Weber et al) • Andes, Atlas-Andes (vanLehn et al) • Cabri-Geometre (Balacheff et al) • Wayang Outpost (Wolff,Arroyo,Murray) • ActiveMath: www.activemath.org (Melis et al) • Belvedere (Suthers) • I-Help (Greer et al) • Tectonica, AHA (Murray et al, deBra+Aroyo) • AutoTutor, BEETLE (Graesser et al, Moore et al) • Help-Tutor (Aleven, Koedinger)

  16. Interdisciplinary Field AI CoLinguistics TEL Cognitive* Psychology Web-Technology Multimedia Pedagogy Content

  17. Contributions of AI • Knowledge representation • User modelling • Intelligent user interfaces • Presentationplanning, intelligent sequencing • Diagnosis • Data mining, Machine Learning • Problem solving systems/automated reasoning • Agent-based (help) systems • Adaptive hypermedia

  18. AI: User modeling • Bayesian nets Probability distribution events, causes, evidences conditional dependences diagnostic/causal update

  19. AI: Knowledge Representation Frames in Cognitive Tutors Problem WME: (make-wme composed-cen-insc isa problem key-quantities (angle-KHP-measure arc-KP-measure angle-KQP-measure) key-reasons (angle-KHP-measure ...) questions (question1) given-relational-quantities (central-angle-KHP inscribed-angle-KQP) table composed-cen-insc-table ) Relation WME... inscribed-angle... inputs (arc-KP-measure) output angle-KQP-measure Quantity WME ... angle-KHP-measure...unit..dimension..labels..

  20. AI: Knowledge Representation • Semantic networks • DAML/OIL/OWL decision logics for XML-Representation • Metadata(publ, mathematical, pedagogical) Ontology

  21. IEEE LTSC, LOM IMS Global Learning Consortium Apple Cisco IBM Microsoft Sun WebCT Universities …. Open e-Book Meta data Interoperability of services Interoperabilty of content (ontologies) Architectures Presentation of content Wiki Security Web-Languages and Technologies Standardization!!!

  22. Goals Content sequence Strategies, Methods Media, tools Competencies Didactic Socratic Inquiry Discovery LearnNew Rehearse Collaborate Proximal development Szenarien, Feedback choice Contributions from Pedagogy difficulty exercises Handling errors Frequent mistakes Feedback Multiple solutions MultiMedia user modeling (competencies)

  23. Bloom: taxonomy of educational objectives

  24. PISA Competencies • Compute • Apply • Model • Argue • Solve problem • Collaborate • Use tools • Meta-cognition…

  25. Contributions Cognitive Psychology • Behaviourisms vs. constructivisms [Piaget, Vygotski] • Feedback • motivation: personalized, self-guided, social, active [Decy&Ryan...] • zone of proximal development [Vygotsky] • gender-specific • meta-cognition [White…] • adaptive support [Mandl...] • multi-modality • structured presentation of solutions [Catrambone] Effective design vs on-line book with animations

  26. Cognitive Psychology: Multimedia Learning • Multimedia Principle • Integration Principle • Modality Principle • Redundancy P. • Coherence Principle • Personalization P. • Learner control

  27. Cognitive Psychology: some results • Self-explanation of worked-out examples [Renkl,Chi,Merrinboer,Siegler] • Why does tutorial dialog help? [Chi etal 2001] • even if human tutors don‘t know tutoring • no-content prompts • ask, don‘t tell ? • students own communication? • Learning from errors/impasses only (?) • Conceptual change (Vosniadou) • Influence of motivation, self-efficiacy [Bandura] • Evaluation of systems

  28. Conclusion • Pursue learning • Learn actively and believe in yourself • Ask questions if you don‘t understand • Discover the world of research

  29. Student Projects 1.Visualization of the pedagogical knowledge domain Analyze and visualize the structure of pedagogical tasks 2. SLOPERT exercise generator Explore the problem space and create a ActiveMath exercises. 3. Learner Model for iCMap Catch and analyze events generated by iCMap 4. Domain Viewer: Render an ActiveMath domain (concepts, relations) 5. Exercise generation with extended randomizer to support intervals and (adaptive) randomizing over a set of elementary functions and their compositions

  30. Student Projects 6. Mathematical Rendering Tester: Support authors by rendering mathematical formulae on the fly 7. Analyzing Online Collaborative Data Generate Machine Learning classifiers from log data 8. E-Portfolio Viewer Implement an interactive viewer for the IMS eP Spec

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