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Ontology Engineering Approach to
Support Computer Supported
Collaborative Learning (CSCL)
University of Sao Paulo
sisotani@icmc.usp.br
Seiji Isotani
Takeaway Message
Designing Intelligent Learning Technologies:
1. Take a real world problem that is hard to
solve
2. Organize the pedagogical knowledge
from different sources
3. Build an ontology
4. Hide the ontology behind a tool that people
can understand and use
5. Apply the tool and the ontology to in real
educational scenarios
2
The field of Computer-Supported
Collaborative Learning - CSCL dedicates to
study about how technology can be used to
support collaborative learning and its
processes (Stahl et al., 2006)
3
Context
The field of Computer-Supported
Collaborative Learning - CSCL dedicates to
study about how technology can be used to
support collaborative learning and its
processes (Stahl et al., 2006)
Despite of the potential benefits of
Collaborative Learning, this approach is
only beneficial when there is an
adequate design and orchestration of
its scenarios (Hernández-Leo et al., 2006, 2011;
Dillenbourg, 2013)
4
Context
Sequence of activities
Group
Formation
CL
Design
Interaction
Support and
Analysis
...
Learners
Groups
Teacher
Meaningful
Results
5
Context
The Problem
• These activities are too complex and time consuming
• They also require specific knowledge and skills
How to increase the
chances of successful
collaborative learning (CL)?
How to provide intelligent
support to design and
carry out collaboration ?
Challenges !
Knowledge to design
effective collaboration
is distributed across
several learning
theories and best
practices
Isotani, S; Mizoguchi, et al. (2009) An ontology engineering approach to the realization of theory-driven
group formation. International Journal of Computer-Supported Collaborative Learning, v. 4, p. 445-478.
They do not share the
same terminology,
assumptions and
expectations and can
be even contradictory!
Isotani et al. (2013). A Semantic Web-based authoring tool to facilitate the planning of collaborative
learning scenarios compliant with learning theories. Computers & Education, 63, 267-284.
In fact, Only 35% of
the the current CL
technology rely on
pedagogical
knowledge
Borges et al (2018) Group Formation in CSCL: A Review of the State of the Art.
Communications in Computer and Information Science, vol 832. Springer, Cham
12
Can we organize this
pedagogical knowledge and
build an infrastructure to use
it adequately?
So, the question is ...
13
I-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goal
Behavior
W(A)-goal
Role
YI-goal
Role
YI-goal
W(L)-goal
Common goal
Primary focus (P)
Secondary focus (S)
S<=P-goal
P<=S-goal
I-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goal
Behavior
k./cog. state
Goal state
I-goal
W(L)-goal
k./cog. state (Group)
Goal state
How does the learner
change his/her state?
What activity does the
group want to do?
How does the group
change its state?
G
G
G
G
Why does the learner want to
interact with other learners?
S
S
G
I-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goal
Behavior
I-goalI-goalI-goalI-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goalY<=I-goal
Behavior
W(A)-goal
Role
YI-goal
Role
YI-goal
W(L)-goal
Common goal
Primary focus (P)
Secondary focus (S)
S<=P-goal
P<=S-goal
W(A)-goalW(A)-goal
RoleRoleRole
YI-goalYI-goalYI-goalYI-goal
RoleRoleRole
YI-goalYI-goalYI-goalYI-goal
W(L)-goal
Common goal
Primary focus (P)
Secondary focus (S)
S<=P-goal
P<=S-goal
I-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goal
Behavior
I-goalI-goalI-goalI-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goalY<=I-goal
Behavior
k./cog. statek./cog. state
Goal state
I-goalI-goalI-goal
W(L)-goal
k./cog. state (Group)
Goal state
How does the learner
change his/her state?
What activity does the
group want to do?
How does the group
change its state?
G
G
G
G
Why does the learner want to
interact with other learners?
S
S
G
Pedagogical knowledge
Use ontological engineering
to describe formally meaningful
information contained in theories
Ontological structure
Use ontologies to
support the
development of
ontology-aware systems
users
Teachers and students
Run experimental studies to:
➢propose group formation;
➢design group activities;
➢ estimate benefits, etc..
Our Approach
Theory aware
intelligent systems
14
20+ Year History on the Systematization of CSCL
Ikeda, M., Go, S. & Mizoguchi, R. (1997) Opportunistic Group Formation.
A Theory for Intelligent Support in Collaborative Learning. Proc. of International
Conference on Artificial Intelligence in Education (AIED), pp.167-174
Group
Formation
Inaba, A., Supnithi,T., Ikeda, M., Mizoguchi, R. & Toyoda, J. (2000) How Can We
Form Effective CL Groups: Theoretical justification of Opportunistic Group
Formation. Proc. of International Conf. on Intelligent Tutoring Systems, pp.282-291
Inaba, A., Ikeda, M. & Mizoguchi, R. (2003) What Learning Patterns are Effective
for a Learner's Growth?An ontological support for designing CL. Proc. of
International Conference on Artificial Intelligence in Education (AIED), pp.219-226
CL Design
Isotani, S. & Mizoguchi, R. (2007) Deployment of Ontologies for an Effective Design
of Collaborative Learning Scenarios. Proc. of International Conference on
Collaboration and Technology (CRIWG), pp.223-238
Inaba, A., Ohkubo, R., Ikeda, M. & Mizoguchi, R. (2002) An Interaction Analysis
Support System for CSCL . Proc. of International Conference on Computers in
Education (ICCE), pp.358-362
Interaction
Analysis
Isotani, S. & Mizoguchi, R. (2006) An Integrated Framework for Fine-Grained
Analysis and Design of Group Learning Activities. Proc. of International Conference
on Computers in Education (ICCE), pp.193-200
15
Formalizing CL
LA
LC
LB
Whole groupsmaller group
part of the whole
interaction
16
LA
LC
LB
Role Role
Role
Individual goal
Individual goalIndividual goal
Strategy A
Whole group goal
Sub-group goal
Strategy B
Formalizing CL
17
Knowledge Formalization
I-goal(LC)
I-goal(LB)I-goal(LA)
W(L)-goal({LA,LB})
W(L)-goal({LA,LB,LC})
Y<=I -goal(LA<=LB)
Y<=I-goal (LB<=LA)
✓Learning Strategies
✓Learning Goals
Knowledge Acquisition:
(accretion, tuning, …)
Learning by
Guiding
Learning by
Apprenticeship
Cognitive Skill Development
(cognitive, associative, …)
Formalizing CL
✓Group Goals
LA
LC
LB
Role Role
Role
Spread of
a skill
Knowledge
sharing
✓Roles
Tutor Tutee
18
p/o
CL Ontology
Graphical representation of the collaborative learning ontology.
19
CL Ontology: Cognitive Apprenticeship
…
20
Collaborative Learning Ontology
This ontology solves several
problems to model and apply
pedagogical knowledge in CSCL
Isotani, S.; Inaba, A. ; Ikeda, M. ; Mizoguchi, R. (2009) An ontology engineering approach to the
realization of theory-driven group formation. International Journal of Computer-Supported
Collaborative Learning, v. 4, p. 445-478.
Challco G.C., Moreira D.A., Mizoguchi R., Isotani S. (2014) An Ontology Engineering Approach
to Gamify Collaborative Learning Scenarios. Lecture Notes in Computer Science, vol 8658.
Springer, p. 185-198.
21
Collaborative Learning Ontology
OK. But let’s be realistic …
Almost nobody can understand
or use this ontology
Takeaway Message
Designing Intelligent Learning Technologies:
1. Take a real world problem that is hard to
solve
2. Organize the pedagogical knowledge
from different sources
3. Build an ontology
4. Hide the ontology behind a tool that people
can understand and use
5. Apply the tool and the ontology to in real
educational scenarios
22
23
Sequence of activities
CL
Design
...
Ontologies
CHOCOLATO: Concrete and Helpful Ontology-aware
Collaborative Learning Authoring Tool
Interaction
Analysis
Meaningful
results
Learners
Theories
CHOCOLATO
I-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goal
Behavior
W(A)-goal
Role
YI-goal
Role
YI-goal
W(L)-goal
Common goal
Primary focus (P)
Secondary focus (S)
S<=P-goal
P<=S-goal
I-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goal
Behavior
k./cog. state
Goal state
I-goal
W(L)-goal
k./cog. state (Group)
Goal state
How does the learner
change his/her state?
What activity does the
group want to do?
How does the group
change its state?
G
G
G
G
Why does the learner want to
interact with other learners?
S
S
G
I-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goal
Behavior
I-goalI-goalI-goalI-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goalY<=I-goal
Behavior
W(A)-goal
Role
YI-goal
Role
YI-goal
W(L)-goal
Common goal
Primary focus (P)
Secondary focus (S)
S<=P-goal
P<=S-goal
W(A)-goalW(A)-goal
RoleRoleRole
YI-goalYI-goalYI-goalYI-goal
RoleRoleRole
YI-goalYI-goalYI-goalYI-goal
W(L)-goal
Common goal
Primary focus (P)
Secondary focus (S)
S<=P-goal
P<=S-goal
I-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goal
Behavior
I-goalI-goalI-goalI-goal
Behavior
I-role
You-role
I-goal (I)
Y<=I-goalY<=I-goal
Behavior
k./cog. statek./cog. state
Goal state
I-goalI-goalI-goal
W(L)-goal
k./cog. state (Group)
Goal state
How does the learner
change his/her state?
What activity does the
group want to do?
How does the group
change its state?
G
G
G
G
Why does the learner want to
interact with other learners?
S
S
G
Group
Formation
Effective Groups
Isotani et al. (2013). A Semantic Web-based authoring tool to facilitate the planning of collaborative
learning scenarios compliant with learning theories. Computers & Education, 63, 267-284.
24
)
Student 1
How to group students?
Student 2
)Student 3
)
25
)
Student 1 Student 2
)Student 3
)
How to group students?
26
)
Student 1 Student 2
)Student 3
)
How to group students?
27
)
Student 1 Student 2
)Student 3
)
How to group students?
28
Theory-Driven Group Formation
Identify which theories can help learners to achieve their goals
learning goals
Y<=I-goal
CL scenario
Learning Strategy IT<=LR
I-goal
I-role
I-goal
Learner
Learner
You-role
G
*
participant
Behavioral role
participant
Behavioral role
Satisfies
Teacher’s
intention
GnG1 …
learning goals
Teacher’s
intention
Y<=I-goal
Learning Strategy LR<=IT
I-goal
I-role
I-goal
Learner
G
participant
Behavioral role
…
GnG1 …
Satisfies
Can play
Can play
LA LB
29
CHOCOLATO
CL Design
Support System
Knowledge Base
Domain Mapping
Support System
Group Formation
Support System
Learning
Objects
Ontologies
Learner
Model
Learning Material
Support System
30
CHOCOLATO
Development
◼ RDF/OWL Parser (ARC2), PHP, Claroline (LMS).
31
CHOCOLATO
32
(a) Created groups
(b) Users’ details
CHOCOLATO
Takeaway Message
Designing Intelligent Learning Technologies:
1. Take a real world problem that is hard to
solve
2. Organize the pedagogical knowledge
from different sources
3. Build an ontology
4. Hide the ontology behind a tool that people
can understand and use
5. Apply the tool and the ontology to in real
educational scenarios
33
34
Collaborative Learning Ontology
Does it really work in practice
and at scale?
A successful case of applying
Semantic Web Technology to build a company
Startup
20 10000
+50.000
STUDENTS
+1000
TEACHERS
+300
SCHOOLS
RESULTS
SUPPORT
PEDAGOGICAL
DECISIONS
RESULTS
INCREASE
LEARNING
EFFECTIVENESS
Paiva, R. ; Bittencourt, I. I. ; Jaques, P. ; ISOTANI, S. . What do students do on-line? Modeling
students' interactions to improve their learning experience. Computers in Human Behavior , v.
64, p. 769-781, 2016.
Tenório, T. ; Bittencourt, I. I. ; Silva, A. P. ; Ospina, P. ; ISOTANI, S. . A gamified peer assessment
model for on-line learning environments in a competitive context. Computers in Human
Behavior, v. 64, p. 247-263, 2016.
Geiser, C. C.; Bittencourt, I. I. ; ISOTANI, S. The Effects of Ontology-Based Gamification in Scripted
Collaborative Learning. IEEE Int. Conference on Advanced Learning Technologies, p.1-5,
2019.
AWARDS
ALAGOAS
GOVERNO DO ESTADO
Future Directions !
Understand the role of affective
states in group formation (and
collaborative learning processes)
Reis, R., Isotani, S. et al (2018). Affective states in computer-supported collaborative
learning: Studying the past to drive the future. Computers & Education, 120, 29-50.
Using Gamification and
ontologies to deal with
demotivation in CSCL
Challco G.C., Mizoguchi R., Isotani S. (2018) Using Ontology and Gamification to
Improve Students’ Participation and Motivation in CSCL. Communications in Computer
and Information Science, vol 832. Springer, Cham
1) Opening
educational
data ...
2) Mining
CSCL data...
http://learnsphere.org/
Takeaway Message
Designing Intelligent Learning Technologies:
1. Take a real world problem that is hard
to solve
2. Organize the pedagogical knowledge
from different sources
3. Build an ontology
4. Hide the ontology behind a tool that
people can understand and use
5. Apply the tool and the ontology to in real
educational scenarios
44
Many
thanks
Ontology Engineering Approach to
Support Computer Supported
Collaborative Learning (CSCL)
University of Sao Paulo
sisotani@icmc.usp.br
Seiji Isotani

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An Ontology Engineering Approach to Support Personalized Gamification of CSCL

  • 1. Ontology Engineering Approach to Support Computer Supported Collaborative Learning (CSCL) University of Sao Paulo sisotani@icmc.usp.br Seiji Isotani
  • 2. Takeaway Message Designing Intelligent Learning Technologies: 1. Take a real world problem that is hard to solve 2. Organize the pedagogical knowledge from different sources 3. Build an ontology 4. Hide the ontology behind a tool that people can understand and use 5. Apply the tool and the ontology to in real educational scenarios 2
  • 3. The field of Computer-Supported Collaborative Learning - CSCL dedicates to study about how technology can be used to support collaborative learning and its processes (Stahl et al., 2006) 3 Context
  • 4. The field of Computer-Supported Collaborative Learning - CSCL dedicates to study about how technology can be used to support collaborative learning and its processes (Stahl et al., 2006) Despite of the potential benefits of Collaborative Learning, this approach is only beneficial when there is an adequate design and orchestration of its scenarios (Hernández-Leo et al., 2006, 2011; Dillenbourg, 2013) 4 Context
  • 5. Sequence of activities Group Formation CL Design Interaction Support and Analysis ... Learners Groups Teacher Meaningful Results 5 Context The Problem • These activities are too complex and time consuming • They also require specific knowledge and skills
  • 6. How to increase the chances of successful collaborative learning (CL)?
  • 7. How to provide intelligent support to design and carry out collaboration ?
  • 9. Knowledge to design effective collaboration is distributed across several learning theories and best practices Isotani, S; Mizoguchi, et al. (2009) An ontology engineering approach to the realization of theory-driven group formation. International Journal of Computer-Supported Collaborative Learning, v. 4, p. 445-478.
  • 10. They do not share the same terminology, assumptions and expectations and can be even contradictory! Isotani et al. (2013). A Semantic Web-based authoring tool to facilitate the planning of collaborative learning scenarios compliant with learning theories. Computers & Education, 63, 267-284.
  • 11. In fact, Only 35% of the the current CL technology rely on pedagogical knowledge Borges et al (2018) Group Formation in CSCL: A Review of the State of the Art. Communications in Computer and Information Science, vol 832. Springer, Cham
  • 12. 12 Can we organize this pedagogical knowledge and build an infrastructure to use it adequately? So, the question is ...
  • 13. 13 I-goal Behavior I-role You-role I-goal (I) Y<=I-goal Behavior W(A)-goal Role YI-goal Role YI-goal W(L)-goal Common goal Primary focus (P) Secondary focus (S) S<=P-goal P<=S-goal I-goal Behavior I-role You-role I-goal (I) Y<=I-goal Behavior k./cog. state Goal state I-goal W(L)-goal k./cog. state (Group) Goal state How does the learner change his/her state? What activity does the group want to do? How does the group change its state? G G G G Why does the learner want to interact with other learners? S S G I-goal Behavior I-role You-role I-goal (I) Y<=I-goal Behavior I-goalI-goalI-goalI-goal Behavior I-role You-role I-goal (I) Y<=I-goalY<=I-goal Behavior W(A)-goal Role YI-goal Role YI-goal W(L)-goal Common goal Primary focus (P) Secondary focus (S) S<=P-goal P<=S-goal W(A)-goalW(A)-goal RoleRoleRole YI-goalYI-goalYI-goalYI-goal RoleRoleRole YI-goalYI-goalYI-goalYI-goal W(L)-goal Common goal Primary focus (P) Secondary focus (S) S<=P-goal P<=S-goal I-goal Behavior I-role You-role I-goal (I) Y<=I-goal Behavior I-goalI-goalI-goalI-goal Behavior I-role You-role I-goal (I) Y<=I-goalY<=I-goal Behavior k./cog. statek./cog. state Goal state I-goalI-goalI-goal W(L)-goal k./cog. state (Group) Goal state How does the learner change his/her state? What activity does the group want to do? How does the group change its state? G G G G Why does the learner want to interact with other learners? S S G Pedagogical knowledge Use ontological engineering to describe formally meaningful information contained in theories Ontological structure Use ontologies to support the development of ontology-aware systems users Teachers and students Run experimental studies to: ➢propose group formation; ➢design group activities; ➢ estimate benefits, etc.. Our Approach Theory aware intelligent systems
  • 14. 14 20+ Year History on the Systematization of CSCL Ikeda, M., Go, S. & Mizoguchi, R. (1997) Opportunistic Group Formation. A Theory for Intelligent Support in Collaborative Learning. Proc. of International Conference on Artificial Intelligence in Education (AIED), pp.167-174 Group Formation Inaba, A., Supnithi,T., Ikeda, M., Mizoguchi, R. & Toyoda, J. (2000) How Can We Form Effective CL Groups: Theoretical justification of Opportunistic Group Formation. Proc. of International Conf. on Intelligent Tutoring Systems, pp.282-291 Inaba, A., Ikeda, M. & Mizoguchi, R. (2003) What Learning Patterns are Effective for a Learner's Growth?An ontological support for designing CL. Proc. of International Conference on Artificial Intelligence in Education (AIED), pp.219-226 CL Design Isotani, S. & Mizoguchi, R. (2007) Deployment of Ontologies for an Effective Design of Collaborative Learning Scenarios. Proc. of International Conference on Collaboration and Technology (CRIWG), pp.223-238 Inaba, A., Ohkubo, R., Ikeda, M. & Mizoguchi, R. (2002) An Interaction Analysis Support System for CSCL . Proc. of International Conference on Computers in Education (ICCE), pp.358-362 Interaction Analysis Isotani, S. & Mizoguchi, R. (2006) An Integrated Framework for Fine-Grained Analysis and Design of Group Learning Activities. Proc. of International Conference on Computers in Education (ICCE), pp.193-200
  • 15. 15 Formalizing CL LA LC LB Whole groupsmaller group part of the whole interaction
  • 16. 16 LA LC LB Role Role Role Individual goal Individual goalIndividual goal Strategy A Whole group goal Sub-group goal Strategy B Formalizing CL
  • 17. 17 Knowledge Formalization I-goal(LC) I-goal(LB)I-goal(LA) W(L)-goal({LA,LB}) W(L)-goal({LA,LB,LC}) Y<=I -goal(LA<=LB) Y<=I-goal (LB<=LA) ✓Learning Strategies ✓Learning Goals Knowledge Acquisition: (accretion, tuning, …) Learning by Guiding Learning by Apprenticeship Cognitive Skill Development (cognitive, associative, …) Formalizing CL ✓Group Goals LA LC LB Role Role Role Spread of a skill Knowledge sharing ✓Roles Tutor Tutee
  • 18. 18 p/o CL Ontology Graphical representation of the collaborative learning ontology.
  • 19. 19 CL Ontology: Cognitive Apprenticeship …
  • 20. 20 Collaborative Learning Ontology This ontology solves several problems to model and apply pedagogical knowledge in CSCL Isotani, S.; Inaba, A. ; Ikeda, M. ; Mizoguchi, R. (2009) An ontology engineering approach to the realization of theory-driven group formation. International Journal of Computer-Supported Collaborative Learning, v. 4, p. 445-478. Challco G.C., Moreira D.A., Mizoguchi R., Isotani S. (2014) An Ontology Engineering Approach to Gamify Collaborative Learning Scenarios. Lecture Notes in Computer Science, vol 8658. Springer, p. 185-198.
  • 21. 21 Collaborative Learning Ontology OK. But let’s be realistic … Almost nobody can understand or use this ontology
  • 22. Takeaway Message Designing Intelligent Learning Technologies: 1. Take a real world problem that is hard to solve 2. Organize the pedagogical knowledge from different sources 3. Build an ontology 4. Hide the ontology behind a tool that people can understand and use 5. Apply the tool and the ontology to in real educational scenarios 22
  • 23. 23 Sequence of activities CL Design ... Ontologies CHOCOLATO: Concrete and Helpful Ontology-aware Collaborative Learning Authoring Tool Interaction Analysis Meaningful results Learners Theories CHOCOLATO I-goal Behavior I-role You-role I-goal (I) Y<=I-goal Behavior W(A)-goal Role YI-goal Role YI-goal W(L)-goal Common goal Primary focus (P) Secondary focus (S) S<=P-goal P<=S-goal I-goal Behavior I-role You-role I-goal (I) Y<=I-goal Behavior k./cog. state Goal state I-goal W(L)-goal k./cog. state (Group) Goal state How does the learner change his/her state? What activity does the group want to do? How does the group change its state? G G G G Why does the learner want to interact with other learners? S S G I-goal Behavior I-role You-role I-goal (I) Y<=I-goal Behavior I-goalI-goalI-goalI-goal Behavior I-role You-role I-goal (I) Y<=I-goalY<=I-goal Behavior W(A)-goal Role YI-goal Role YI-goal W(L)-goal Common goal Primary focus (P) Secondary focus (S) S<=P-goal P<=S-goal W(A)-goalW(A)-goal RoleRoleRole YI-goalYI-goalYI-goalYI-goal RoleRoleRole YI-goalYI-goalYI-goalYI-goal W(L)-goal Common goal Primary focus (P) Secondary focus (S) S<=P-goal P<=S-goal I-goal Behavior I-role You-role I-goal (I) Y<=I-goal Behavior I-goalI-goalI-goalI-goal Behavior I-role You-role I-goal (I) Y<=I-goalY<=I-goal Behavior k./cog. statek./cog. state Goal state I-goalI-goalI-goal W(L)-goal k./cog. state (Group) Goal state How does the learner change his/her state? What activity does the group want to do? How does the group change its state? G G G G Why does the learner want to interact with other learners? S S G Group Formation Effective Groups Isotani et al. (2013). A Semantic Web-based authoring tool to facilitate the planning of collaborative learning scenarios compliant with learning theories. Computers & Education, 63, 267-284.
  • 24. 24 ) Student 1 How to group students? Student 2 )Student 3 )
  • 25. 25 ) Student 1 Student 2 )Student 3 ) How to group students?
  • 26. 26 ) Student 1 Student 2 )Student 3 ) How to group students?
  • 27. 27 ) Student 1 Student 2 )Student 3 ) How to group students?
  • 28. 28 Theory-Driven Group Formation Identify which theories can help learners to achieve their goals learning goals Y<=I-goal CL scenario Learning Strategy IT<=LR I-goal I-role I-goal Learner Learner You-role G * participant Behavioral role participant Behavioral role Satisfies Teacher’s intention GnG1 … learning goals Teacher’s intention Y<=I-goal Learning Strategy LR<=IT I-goal I-role I-goal Learner G participant Behavioral role … GnG1 … Satisfies Can play Can play LA LB
  • 29. 29 CHOCOLATO CL Design Support System Knowledge Base Domain Mapping Support System Group Formation Support System Learning Objects Ontologies Learner Model Learning Material Support System
  • 30. 30 CHOCOLATO Development ◼ RDF/OWL Parser (ARC2), PHP, Claroline (LMS).
  • 32. 32 (a) Created groups (b) Users’ details CHOCOLATO
  • 33. Takeaway Message Designing Intelligent Learning Technologies: 1. Take a real world problem that is hard to solve 2. Organize the pedagogical knowledge from different sources 3. Build an ontology 4. Hide the ontology behind a tool that people can understand and use 5. Apply the tool and the ontology to in real educational scenarios 33
  • 34. 34 Collaborative Learning Ontology Does it really work in practice and at scale? A successful case of applying Semantic Web Technology to build a company
  • 36.
  • 38. SUPPORT PEDAGOGICAL DECISIONS RESULTS INCREASE LEARNING EFFECTIVENESS Paiva, R. ; Bittencourt, I. I. ; Jaques, P. ; ISOTANI, S. . What do students do on-line? Modeling students' interactions to improve their learning experience. Computers in Human Behavior , v. 64, p. 769-781, 2016. Tenório, T. ; Bittencourt, I. I. ; Silva, A. P. ; Ospina, P. ; ISOTANI, S. . A gamified peer assessment model for on-line learning environments in a competitive context. Computers in Human Behavior, v. 64, p. 247-263, 2016. Geiser, C. C.; Bittencourt, I. I. ; ISOTANI, S. The Effects of Ontology-Based Gamification in Scripted Collaborative Learning. IEEE Int. Conference on Advanced Learning Technologies, p.1-5, 2019.
  • 41. Understand the role of affective states in group formation (and collaborative learning processes) Reis, R., Isotani, S. et al (2018). Affective states in computer-supported collaborative learning: Studying the past to drive the future. Computers & Education, 120, 29-50.
  • 42. Using Gamification and ontologies to deal with demotivation in CSCL Challco G.C., Mizoguchi R., Isotani S. (2018) Using Ontology and Gamification to Improve Students’ Participation and Motivation in CSCL. Communications in Computer and Information Science, vol 832. Springer, Cham
  • 43. 1) Opening educational data ... 2) Mining CSCL data... http://learnsphere.org/
  • 44. Takeaway Message Designing Intelligent Learning Technologies: 1. Take a real world problem that is hard to solve 2. Organize the pedagogical knowledge from different sources 3. Build an ontology 4. Hide the ontology behind a tool that people can understand and use 5. Apply the tool and the ontology to in real educational scenarios 44
  • 46. Ontology Engineering Approach to Support Computer Supported Collaborative Learning (CSCL) University of Sao Paulo sisotani@icmc.usp.br Seiji Isotani