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An Ontology Engineering Approach to Support Personalized Gamification of CSCL

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LSWT2019 Talk by Seiji Isotani, Professor @ University of São Paulo

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An Ontology Engineering Approach to Support Personalized Gamification of CSCL

  1. 1. Ontology Engineering Approach to Support Computer Supported Collaborative Learning (CSCL) University of Sao Paulo sisotani@icmc.usp.br Seiji Isotani
  2. 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. 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. 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. 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. 6. How to increase the chances of successful collaborative learning (CL)?
  7. 7. How to provide intelligent support to design and carry out collaboration ?
  8. 8. Challenges !
  9. 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. 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. 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. 12 Can we organize this pedagogical knowledge and build an infrastructure to use it adequately? So, the question is ...
  13. 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. 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. 15 Formalizing CL LA LC LB Whole groupsmaller group part of the whole interaction
  16. 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. 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. 18 p/o CL Ontology Graphical representation of the collaborative learning ontology.
  19. 19. 19 CL Ontology: Cognitive Apprenticeship …
  20. 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. 21 Collaborative Learning Ontology OK. But let’s be realistic … Almost nobody can understand or use this ontology
  22. 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. 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. 24 ) Student 1 How to group students? Student 2 )Student 3 )
  25. 25. 25 ) Student 1 Student 2 )Student 3 ) How to group students?
  26. 26. 26 ) Student 1 Student 2 )Student 3 ) How to group students?
  27. 27. 27 ) Student 1 Student 2 )Student 3 ) How to group students?
  28. 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. 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. 30 CHOCOLATO Development ◼ RDF/OWL Parser (ARC2), PHP, Claroline (LMS).
  31. 31. 31 CHOCOLATO
  32. 32. 32 (a) Created groups (b) Users’ details CHOCOLATO
  33. 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. 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
  35. 35. Startup
  36. 36. 20 10000 +50.000 STUDENTS +1000 TEACHERS +300 SCHOOLS RESULTS
  37. 37. 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.
  38. 38. AWARDS ALAGOAS GOVERNO DO ESTADO
  39. 39. Future Directions !
  40. 40. 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.
  41. 41. 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
  42. 42. 1) Opening educational data ... 2) Mining CSCL data... http://learnsphere.org/
  43. 43. 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
  44. 44. Many thanks
  45. 45. Ontology Engineering Approach to Support Computer Supported Collaborative Learning (CSCL) University of Sao Paulo sisotani@icmc.usp.br Seiji Isotani

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