This document discusses adaptive and intelligent collaborative learning support systems (AICLS). It provides background on related fields and outlines a classification scheme for AICLS focusing on the target of intervention. It describes work done to model adaptation patterns, develop an AICLS architecture, and implement case studies. Future research directions are proposed, including implementing complete courses with adaptive support at different levels and evolving systems based on assessment. Key publications in the field are also listed.

1. Adaptive and Intelligent Collaborative
Learning Support systems (AICLS)
Magnisalis Ioannis
Aristotle University of Thessaloniki, Greece 2011

2. Presentation flow
– Background & Work up to now
– Research Directions & Future Roadmap
– Publications
Aristotle 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) - FUTURE
Aristotle University of Thessaloniki, Greece 2011

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. Work up to now 2/8
Adaptation 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. 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. Work up to now 4/8
Running 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. Work up to now 5/8
MAPIS Architecture
(i.e. Mediating Adaptation
Patterns & Intelligent
Services)
Problem: adaptive LDs
with external
tools & use IMS-
LD and EA with as
little as possible
intervention
Solution: SOA
architecture with
Web services as
main constituent
Requirements:
a) Interoperability,
b) extensibility
Aristotle University of Thessaloniki, Greece 2011

9. Work up to now 6/8
Proof of concept: ‘Group Heterogeneity’ adaptation
as an example case study/scenario
According 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. Work up to now 7/8
IMS-LD is interconnected with
GF component and 1
Moodle
Pre-task adaptation (AP)
3
2
Aristotle University of Thessaloniki, Greece 2011

11. Work up to now 8/8
IMS-LD is interconnected
with Moodle forum 1
In-task adaptation (AP)
2
3
Mirroring
vs Meta-cognitive
Aristotle University of Thessaloniki, Greece 2011

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. 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
Resources
candidate tools to work upon are ………………………….
What is Adapted Activity
Re-Course Editor and
Webcollage Other
introduce 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. Research Directions 3/3
As 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. 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. 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.gr
Department 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 an
activity (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-
time
http://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-21
Catalog of Tools/services
with semantics.
Wookie ++  Input, Output
of 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. Annotation example…
Aristotle University of Thessaloniki, Greece 2011

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. Orchestration Layered model
SMAPIS (LinkedData
paradigm)
Learning Flow (OWL) Meta-Adaptation
Data flow (BPEL, OWL-S)
Adaptation
IRMO, MAPIS
Pedagogy (CLFP, AP) Design – Script
Technology – Communication
Aristotle 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, use
your phone to take a photo, use Lights to attract
attention
Aristotle University of Thessaloniki, Greece 2011

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. 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. SWS tasks
Aristotle University of Thessaloniki, Greece 2011

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