Adaptive and Intelligent Collaborative  Learning Support systems (AICLS)          Magnisalis Ioannis          Aristotle Un...
Presentation flow  – Background & Work up to now  – Research Directions & Future Roadmap  – Publications                Ar...
Background• Adaptive systems• Intelligent systems• CSCL   – Theory   (Pedagogic perspective)• Classification scheme   – Fo...
Work up to now 1/8•   Standards and widespread technologies    – IMS-LD, QTI, Moodle, LAMS, Webconference      tools,BPEL,...
Work up to now 2/8Adaptation Pattern Design Specification (IRMO)                                 db            or    manif...
Work up to now 3/8    During design…•    Linking APs to Script Design                                                Activ...
Work up to now 4/8Running AP                                                                               Advanced Study:...
Work up to now 5/8MAPIS Architecture(i.e. Mediating Adaptation        Patterns & Intelligent        Services)Problem: adap...
Work up to now 6/8Proof of concept: ‘Group Heterogeneity’ adaptation   as an example case study/scenarioAccording to the I...
Work up to now 7/8IMS-LD is interconnected with     GF component and                                                     1...
Work up to now 8/8IMS-LD is interconnected     with Moodle forum                                                 1In-task ...
Research Directions 1/3•    Implement courses in     real environments    – Educational    – Workplace•    Investigate use...
Research Directions 2/3            Re-Course screenshot                Adaptation Pattern: ‘Advance the Advanced’         ...
Research Directions 3/3As a next step we are already working in designing a complete course     in IMS-LD and Moodle provi...
Publications•   S. Demetriadis, I. Magnisalis and A. Karakostas, “Adaptation Patterns in Systems for    Collaborative Lear...
Technological background extensions and interests •   Java, Javascript, PHP, MySQL •   Web services, BPEL, Semantic Web • ...
Thank you for your attention!        Questions?                                                                          C...
Common•   SOFCLES project•   LEADFLOW4LD•   GSI•   CLFPs and APs
Scenario• GF• IA• Weights of hinttype• Adaptive behavior updated every time is run• Two types of implementation: Client (p...
Terms• Ubiquitous (Pros – Cons)• Collective Intelligence (Educational aspect) –  (folksonomy vs ontology, modest computing...
Semantics over MAPIS (SMAPIS)   Annotation   with:   OWL,   OWL-S,   RDF,   BPEL,   MPEG-21Catalog of Tools/serviceswith s...
Annotation example…           Aristotle University of Thessaloniki, Greece 2011
Scenarios…• Orchestration Design: Use in Dicsuss2 (small groups) in pyramid  script a tool with affordances…(IA indicators...
Orchestration Layered modelSMAPIS (LinkedDataparadigm)Learning Flow (OWL)                                     Meta-Adaptat...
Challenges of Ubiquitous…•   1. The “Accidentally” Smart Environment•   2. Impromptu (i.e. NO) Interoperability•   3. No S...
So What’s the Problem?• BPEL: Description of  how Web Services are  composed. Limitations:  No IOPEs, Allows  execution of...
Process Model in OWL-S• Process   – Potentially interpretable description of service provider’s     behavior   – Specifies...
SWS tasks            Aristotle University of Thessaloniki, Greece 2011
Tackling Semantic Interoperability…Lack of Semantic Interoperability is a major hurdle for• Discovery: Different terms use...
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Valldolid Magnisalis Ioannis

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Adaption Patterns, Architecture connecting IMS-LD to extrenal components

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Valldolid Magnisalis Ioannis

  1. 1. Adaptive and Intelligent Collaborative Learning Support systems (AICLS) Magnisalis Ioannis Aristotle University of Thessaloniki, Greece 2011
  2. 2. Presentation flow – Background & Work up to now – Research Directions & Future Roadmap – Publications Aristotle University of Thessaloniki, Greece 2011
  3. 3. Background• Adaptive systems• Intelligent systems• CSCL – Theory (Pedagogic perspective)• Classification scheme – Focus on Target of Intervention • Group formation (Pre-task) • Knowledge domain support (In-task) • Peer Interaction (In-task) • Assessment (Post-Task) - FUTURE Aristotle University of Thessaloniki, Greece 2011
  4. 4. Work up to now 1/8• Standards and widespread technologies – IMS-LD, QTI, Moodle, LAMS, Webconference tools,BPEL, Semantic Web & Ontologies …• Adaptation patterns modeling (IRMO design specification)• Architecture for AICLS systems (MAPIS)• Case studies – Pre-task (group formation) – In-task (Moodle forum): Mirroring & Meta- cognitive level Aristotle University of Thessaloniki, Greece 2011
  5. 5. Work up to now 2/8Adaptation Pattern Design Specification (IRMO) db or manifest MODELLED ENTITIES Interaction On Screen Analysis Representation INPUT RULES OUTPUT ADAPTAT I O N PAT T E R N • During design: – Define monitored parameters (e.g. from interaction analysis tools) – Rules (the adaptation model of the pattern) are hard-wired to the pattern – Define Output (form, content, etc.) Aristotle University of Thessaloniki, Greece 2011
  6. 6. Work up to now 3/8 During design…• Linking APs to Script Design Activity Flow Script Representation …………………………. AP: ‘Advance the SCRIPT PHASE: Advanced’ Individual Study …………………………. 6 / 37 Aristotle University of Thessaloniki, Greece 2011
  7. 7. Work up to now 4/8Running AP Advanced Study: The learner is Output of the Adaptation Pattern: The activity of prompted to study the “Advanced_Study” is presented to an advanced learner. In advanced material and answer contrast his partner – novice learner – is guided to normal some relevant questions. study (not shown in this screen capture) SLED screenshot of the adapted user interface for the advanced learner according to the implemented adaptation pattern 7 / 37 Aristotle University of Thessaloniki, Greece 2011
  8. 8. Work up to now 5/8MAPIS Architecture(i.e. Mediating Adaptation Patterns & Intelligent Services)Problem: adaptive LDs with external tools & use IMS- LD and EA with as little as possible interventionSolution: SOA architecture with Web services as main constituentRequirements:a) Interoperability,b) extensibility Aristotle University of Thessaloniki, Greece 2011
  9. 9. Work up to now 6/8Proof of concept: ‘Group Heterogeneity’ adaptation as an example case study/scenarioAccording to the IRMO specification this adaptation is described as follows:• Input: the outcome of a prior knowledge questionnaire which is used as a measure of learners’ expertise,• Model: Prior domain knowledge of each learner & Mean of Prior domain knowledge of all participants & number of groups & number of participants,• Rule: IF Group work is needed THEN provide new groups of mild heterogeneity (complex rule which entails calculations of a) number of groups, b) best distribution within them),• Output: Form New Groups mildly heterogeneous according to prior domain knowledge. Aristotle University of Thessaloniki, Greece 2011
  10. 10. Work up to now 7/8IMS-LD is interconnected with GF component and 1 MoodlePre-task adaptation (AP) 3 2 Aristotle University of Thessaloniki, Greece 2011
  11. 11. Work up to now 8/8IMS-LD is interconnected with Moodle forum 1In-task adaptation (AP) 2 3 Mirroring vs Meta-cognitive Aristotle University of Thessaloniki, Greece 2011
  12. 12. Research Directions 1/3• Implement courses in real environments – Educational – Workplace• Investigate use of various tools – Synchronous (e.g. Web-conferencing) – Asynchronous (e.g. Wikis)• Interconnect all these in an AICLS system under a framework A Webconference system as a candidate to incorporating design & external system to communicate with IMS-LD architecture players suggestions Aristotle University of Thessaloniki, Greece 2011
  13. 13. Research Directions 2/3 Re-Course screenshot Adaptation Pattern: ‘Advance the Advanced’ …………………………. Monitored Parameter(s) How is the Advanced Add parameter Learner Defined? SCRIPT PHASE: MaxIndividualof number Study 3 learners in Group Resourcescandidate tools to work upon are …………………………. What is Adapted Activity Re-Course Editor and Webcollage Otherintroduce adaptation patterns as A possible interface of a s/w component components/ services/tools in facilitating AP application the form of a toolbox Aristotle University of Thessaloniki, Greece 2011
  14. 14. Research Directions 3/3As a next step we are already working in designing a complete course in IMS-LD and Moodle providing adaptive support at three distinct levels in a pyramid script:• Pre-task adaptation: for example a questionnaire in Moodle to activate specific learning activities• In-task adaptation: providing hints and careful interventions in discussion within a Moodle forum according to participation levels monitored in Moodle and set into IMS-LD.• Post-task adaptation: Assessment of the CSCL process from the students can provide ratings for the hints introduced during in- task adaptation. The system should not use hints rated as not helpful in a next run. – Target is a system that evolves by its use (LEGO system) – Assessment that is adaptive itself – Built of folksonomies instead of Ontologies Aristotle University of Thessaloniki, Greece 2011
  15. 15. Publications• S. Demetriadis, I. Magnisalis and A. Karakostas, “Adaptation Patterns in Systems for Collaborative Learning and the Role of the Learning Design Specification”, Scripted vs. Free CS collaboration: Alternatives and paths for adaptable and flexible CS scripted collaboration Workshop in CSCL2009, Rhodes, 2009, pp. 43-47.• I. Magnisalis, S. Demetriadis, “Modeling adaptation patterns with IMS-LD specification: a case study as a proof of concept implementation", International Conference on Intelligent Networking and Collaborative Systems (INCoS 2009), Barcelona, 2009.• Ioannis D. Magnisalis, Stavros N. Demetriadis, Andreas S. Pomportsis, “Implementing Adaptive Techniques in Systems for Collaborative Learning by extending IMS-LD capabilities", International Conference on Intelligent Networking and Collaborative Systems (INCoS 2010), Thessaloniki, 2010 (accepted).• I. Magnisalis, S. Demetriadis, “Modeling adaptation patterns in the context of collaborative learning: case studies of IMS-LD based implementation", Technology- Enhanced Systems and Adaptation Methods for Collaborative Learning Support, (under revision).• Magnisalis, Ioannis; Demetriadis, Stavros; Karakostas, Anastasios; , "Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field," Learning Technologies, IEEE Transactions on , vol.4, no.1, pp.5-20, Jan. 2011 doi: 10.1109/TLT.2011.2• D. Meimaridou, I. Magnisalis, S. Demetriadis, A. Pomportsis, “Web conferencing to support blended learning in the school context: a case study in a Second Chance School ”, ICICTE 2011, (under review). Aristotle University of Thessaloniki, Greece 2011
  16. 16. Technological background extensions and interests • Java, Javascript, PHP, MySQL • Web services, BPEL, Semantic Web • Ontologies, Rule-based systems • Annotation, Rating systems • Wikis, Forum, Chat, web-conferencing tools Aristotle University of Thessaloniki, Greece 2011
  17. 17. Thank you for your attention! Questions? Contact: E-mail: imagnisa@csd.auth.gr Department of Informatics, AUTH: http://www.csd.auth.gr Multimedia lab: http://mlab.csd.auth.gr/ Aristotle University of Thessaloniki, Greece 2011
  18. 18. Common• SOFCLES project• LEADFLOW4LD• GSI• CLFPs and APs
  19. 19. Scenario• GF• IA• Weights of hinttype• Adaptive behavior updated every time is run• Two types of implementation: Client (php/java) or BPEL based data of complex scenario e.g. IA & GF: role reallocation or IA from various tools in an activity (e.g. drawing & forum & chat)• Linked data org
  20. 20. Terms• Ubiquitous (Pros – Cons)• Collective Intelligence (Educational aspect) – (folksonomy vs ontology, modest computing)• Pedagogy• Adaptation - Meta-adaptation• Orchestration (choreography) – Design vs Run- time http://www.kinecteducation.com/• Kinect http://projects.ict.usc.edu/mxr/faast/ http://www.rit.edu/innovationcenter/kinectatrit/ag gregator/categories/1
  21. 21. Semantics over MAPIS (SMAPIS) Annotation with: OWL, OWL-S, RDF, BPEL, MPEG-21Catalog of Tools/serviceswith semantics.Wookie ++  Input, Outputof Widgets (IRMO)More than WADL, WSDLhttp://code.google.com/apis/explorer/#_s=translate&_v=v2&_m=translations.list&q=good%20morning&target=ELhttp://code.google.com/apis/ajax/playground/?exp=libraries#translatehttp://code.google.com/intl/el-GR/apis/discovery/ Aristotle University of Thessaloniki, Greece 2011
  22. 22. Annotation example… Aristotle University of Thessaloniki, Greece 2011
  23. 23. Scenarios…• Orchestration Design: Use in Dicsuss2 (small groups) in pyramid script a tool with affordances…(IA indicators, SNA, text based with upload capability etc.)• Orchestration conducting: A forum selected at design time is not available. Shall I use a chat with same affordances?• EEE: A learner with low participation and rating(s) in a Web 2.0 (Moodle forum) is given the role of coordinator with extra material in next activity of Second Life (SL) and given tools in another activity of Augmented Reality/Virtuality (in class/SL he is permitted to use specific material that others in group cannot) - Kinect use in SL.• CompleX WSs (=BPEL) and annotated with semantics: Based on IRMO, input of WS2(IA indicators) is output of WS1, and output of BPEL complex process is an overall assessment of a learner in various group activities. Aristotle University of Thessaloniki, Greece 2011
  24. 24. Orchestration Layered modelSMAPIS (LinkedDataparadigm)Learning Flow (OWL) Meta-AdaptationData flow (BPEL, OWL-S) AdaptationIRMO, MAPISPedagogy (CLFP, AP) Design – Script Technology – Communication Aristotle University of Thessaloniki, Greece 2011
  25. 25. Challenges of Ubiquitous…• 1. The “Accidentally” Smart Environment• 2. Impromptu (i.e. NO) Interoperability• 3. No Systems Administrator• 4. Social Implications of Aware Technologies• 5. Reliability – Example: No 3G/wifi connectivity – No problem, use your phone to take a photo, use Lights to attract attention Aristotle University of Thessaloniki, Greece 2011
  26. 26. So What’s the Problem?• BPEL: Description of how Web Services are composed. Limitations: No IOPEs, Allows execution of a manually constructed composition• UDDI: Directory Service for Web Services. Limitations: keyword searches, limited capability search Aristotle University of Thessaloniki, Greece 2011
  27. 27. Process Model in OWL-S• Process – Potentially interpretable description of service provider’s behavior – Specifies service interaction protocol • Tells service user how and when to interact (read/write messages) – Specifies abstract messages: ontological type of information transmitted• Used for: Service invocation, planning/composition, interoperation, monitoring• All processes have: Inputs, outputs, preconditions and effects• Composite processes deal with: – Control flow – Data flow Aristotle University of Thessaloniki, Greece 2011
  28. 28. SWS tasks Aristotle University of Thessaloniki, Greece 2011
  29. 29. Tackling Semantic Interoperability…Lack of Semantic Interoperability is a major hurdle for• Discovery: Different terms used for advertisements and requests• Invocation: Different specs for messages and WS interface• Understanding: Interpreting the results returned by the Web service• Composing Services: Reconciling LD goals with goals of the WS• Negotiating contracts & communications: Different terminology and protocols used Aristotle University of Thessaloniki, Greece 2011

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