1. Adaptive and Intelligent CollaborativeLearning Support systems (AICLS)Magnisalis IoannisAristotle University of Thessaloniki, Greece 2011

2. Presentation flow Background & Work up to now Research Directions & Future Roadmap PublicationsAristotle University of Thessaloniki, Greece 2011 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) - FUTUREAristotle University of Thessaloniki, Greece 2011 4. Work up to now 1/8 Standards and widespread technologies IMS-LD, QTI, Moodle, LAMS, Webconferencetools,BPEL, Semantic Web & Ontologies Adaptation patterns modeling (IRMO designspecification) Architecture for AICLS systems (MAPIS) Case studies Pre-task (group formation) In-task (Moodle forum): Mirroring & Meta-cognitive levelAristotle University of Thessaloniki, Greece 2011 5. Work up to now 2/8Adaptation Pattern Design Specification (IRMO) dbormanifest MODELLED ENTITIES Interaction On ScreenAnalysisRepresentationINPUT RULES OUTPUTADAPTAT 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. Work up to now 3/8During designLinking APs to Script DesignActivity FlowScript Representation.AP: Advance theSCRIPT PHASE:Advanced Individual Study .6 / 37 Aristotle University of Thessaloniki, Greece 2011 7. Work up to now 4/8Running AP Advanced Study: The learner is Output of the Adaptation Pattern: The activity of prompted tostudythe Advanced_Study is presented to an advanced learner. Inadvanced 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 / 37Aristotle University of Thessaloniki, Greece 2011 8. Work up to now 5/8MAPIS Architecture(i.e. Mediating AdaptationPatterns & IntelligentServices)Problem: adaptive LDswith externaltools & use IMS-LD and EA with aslittle as possibleinterventionSolution: SOAarchitecture withWeb services asmain constituentRequirements:a)Interoperability,b)extensibility Aristotle University of Thessaloniki, Greece 2011 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 asfollows: Input: the outcome of a prior knowledge questionnaire which isused as a measure of learners expertise, Model: Prior domain knowledge of each learner & Mean of Priordomain knowledge of all participants & number of groups &number of participants, Rule: IF Group work is needed THEN provide new groups of mildheterogeneity (complex rule which entails calculations of a)number of groups, b) best distribution within them), Output: Form New Groups mildly heterogeneous according toprior domain knowledge. Aristotle University of Thessaloniki, Greece 2011 10. Work up to now 7/8IMS-LD is interconnected with GF component and 1 MoodlePre-task adaptation (AP) 32Aristotle University of Thessaloniki, Greece 2011 11. Work up to now 8/8IMS-LD is interconnected with Moodle forum 1In-task adaptation (AP)2 3Mirroringvs Meta-cognitive Aristotle University of Thessaloniki, Greece 2011 12. Research Directions 1/3Implement courses in real environments Educational WorkplaceInvestigate 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 architectureplayers suggestions Aristotle University of Thessaloniki, Greece 2011 13. Research Directions 2/3Re-Course screenshotAdaptation Pattern: Advance the Advanced. Monitored Parameter(s) How is the AdvancedAdd parameterLearner Defined? SCRIPT PHASE:MaxIndividualofnumber Study 3learners in Group Resourcescandidate tools to work upon are. What is AdaptedActivityRe-Course Editor andWebcollage Otherintroduce adaptation patterns as A possible interface of a s/w componentcomponents/ services/tools infacilitating AP applicationthe form of a toolbox Aristotle University of Thessaloniki, Greece 2011 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 activitiesIn-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 OntologiesAristotle University of Thessaloniki, Greece 2011 15. Publications S. Demetriadis, I. Magnisalis and A. Karakostas, Adaptation Patterns in Systems forCollaborative Learning and the Role of the Learning Design Specification, Scripted vs.Free CS collaboration: Alternatives and paths for adaptable and flexible CS scriptedcollaboration Workshop in CSCL2009, Rhodes, 2009, pp. 43-47. I. Magnisalis, S. Demetriadis, Modeling adaptation patterns with IMS-LD specification: acase study as a proof of concept implementation", International Conferenceon Intelligent Networking and Collaborative Systems (INCoS 2009), Barcelona, 2009. Ioannis D. Magnisalis, Stavros N. Demetriadis, Andreas S. Pomportsis, ImplementingAdaptive Techniques in Systems for Collaborative Learning by extending IMS-LDcapabilities", International Conference on Intelligent Networking and CollaborativeSystems (INCoS 2010), Thessaloniki, 2010 (accepted). I. Magnisalis, S. Demetriadis, Modeling adaptation patterns in the context ofcollaborative learning: case studies of IMS-LD based implementation", Technology-Enhanced Systems and Adaptation Methods for Collaborative Learning Support, (underrevision). Magnisalis, Ioannis; Demetriadis, Stavros; Karakostas, Anastasios; , "Adaptive andIntelligent Systems for Collaborative Learning Support: A Review of the Field," LearningTechnologies, IEEE Transactions on , vol.4, no.1, pp.5-20, Jan. 2011doi: 10.1109/TLT.2011.2 D. Meimaridou, I. Magnisalis, S. Demetriadis, A. Pomportsis, Web conferencing tosupport 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. 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. Thank you for your attention!Questions?Contact:E-mail: imagnisa@csd.auth.grDepartment of Informatics, AUTH: http://www.csd.auth.gr Multimedia lab: http://mlab.csd.auth.gr/Aristotle University of Thessaloniki, Greece 2011 18. Common SOFCLES project LEADFLOW4LD GSI CLFPs and APs 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 anactivity (e.g. drawing & forum & chat) Linked data org 20. Terms Ubiquitous (Pros Cons) Collective Intelligence (Educational aspect) (folksonomy vs ontology, modest computing) Pedagogy Adaptation - Meta-adaptation Orchestration (choreography) Design vs Run-timehttp://www.kinecteducation.com/ Kinect http://projects.ict.usc.edu/mxr/faast/ http://www.rit.edu/innovationcenter/kinectatrit/ag gregator/categories/1 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, WSDLhttp://code.google.com/apis/explorer/#_s=translate&_v=v2&_m=translations.list&q=good%20morning&target=ELhttp://code.google.com/apis/ajax/playground/?exp=libraries#translatehttp://code.google.com/intl/el-GR/apis/discovery/ Aristotle University of Thessaloniki, Greece 2011 22. Annotation example Aristotle University of Thessaloniki, Greece 2011 23. Scenarios Orchestration Design: Use in Dicsuss2 (small groups) in pyramidscript a tool with affordances(IA indicators, SNA, text based withupload capability etc.) Orchestration conducting: A forum selected at design time is notavailable. 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 extramaterial in next activity of Second Life (SL) and given tools inanother activity of Augmented Reality/Virtuality (in class/SL he ispermitted to use specific material that others in group cannot) -Kinect use in SL. CompleX WSs (=BPEL) and annotated with semantics: Based onIRMO, input of WS2(IA indicators) is output of WS1, and output ofBPEL complex process is an overall assessment of a learner invarious group activities.Aristotle University of Thessaloniki, Greece 2011 24. Orchestration Layered modelSMAPIS (LinkedDataparadigm)Learning Flow (OWL) Meta-AdaptationData flow (BPEL, OWL-S) AdaptationIRMO, MAPISPedagogy (CLFP, AP)Design ScriptTechnology CommunicationAristotle University of Thessaloniki, Greece 2011 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, useyour phone to take a photo, use Lights to attractattention Aristotle University of Thessaloniki, Greece 2011 26. So Whats the Problem? BPEL: Description ofhow Web Services arecomposed. Limitations:No IOPEs, Allowsexecution of a manuallyconstructedcomposition UDDI: Directory Servicefor Web Services.Limitations: keywordsearches, limitedcapability searchAristotle University of Thessaloniki, Greece 2011 27. Process Model in OWL-S Process Potentially interpretable description of service providers behavior Specifies service interaction protocol Tells service user how and when to interact (read/write messages) Specifies abstract messages: ontological type of informationtransmitted Used for: Service invocation, planning/composition,interoperation, monitoring All processes have: Inputs, outputs, preconditions and effects Composite processes deal with: Control flow Data flowAristotle University of Thessaloniki, Greece 2011 28. SWS tasksAristotle University of Thessaloniki, Greece 2011 29. Tackling Semantic InteroperabilityLack 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