An ontology engineering approach to the realization of theory-driven group formation

  • Seiji IsotaniEmail author
  • Akiko Inaba
  • Mitsuru Ikeda
  • Riichiro Mizoguchi


One of the main difficulties during the design of collaborative learning activities is adequate group formation. In any type of collaboration, group formation plays a critical role in the learners’ acceptance of group activities, as well as the success of the collaborative learning process. Nevertheless, to propose both an effective and pedagogically sound group formation is a complex issue due to multiple factors that influence group arrangement. The current (and previous) learner’s knowledge and skills, the roles and strategies used by learners to interact among themselves, and the teacher’s preferences are some examples of factors to be considered while forming groups. To identify which factors are essential (or desired) in effective group formation, a well-structured and formalized representation of collaborative learning processes, supported by a strong pedagogical basis, is desirable. Thus, the main goal of this paper is to present an ontology that works as a framework based on learning theories that facilitate group formation and collaborative learning design. The ontology provides the necessary formalization to represent collaborative learning and its processes, while learning theories provide support in making pedagogical decisions such as gathering learners in groups and planning the scenario where the collaboration will take place. Although the use of learning theories to support collaborative learning is open for criticism, we identify that they provide important information which can be useful in allowing for more effective learning. To validate the usefulness and effectiveness of this approach, we use this ontology to form and run group activities carried out by four instructors and 20 participants. The experiment was utilized as a proof-of-concept and the results suggest that our ontological framework facilitates the effective design of group activities, and can positively affect the performance of individuals during group learning.


Group formation Ontological engineering Collaborative learning design 



We would like to thank the reviewers and editors of the ijCSCL for their helpful comments and suggestions. We also would like to thank the Nippon Foundation, the Association of Nikkei & Japanese Abroad, JICA (Japan International Cooperation Agency), IBM Research and the Department of Knowledge Systems (MizLab) for their financial and technical support.


  1. Alfonseca, E., Carro, R. M., Martín, E., Ortigosa, A., & Paredes, P. (2006). The impact of learning styles on student grouping for collaborative learning: a case study. User Modeling and User-Adapted Interaction, 16(3–4), 377–401.CrossRefGoogle Scholar
  2. Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89(4), 369–406.CrossRefGoogle Scholar
  3. Aronson, E., & Patnoe, S. (1997). The jigsaw classroom: Building cooperation in the classroom (2nd ed.). New York: Addison Wesley Longman.Google Scholar
  4. Bandura, A. (1971). Social learning theory. New York: General Learning.Google Scholar
  5. Barkley, E., Cross, K. P., & Major, C. H. (2005). Collaborative learning techniques: A practical guide to promoting learning in groups. San Francisco: Jossey Bass.Google Scholar
  6. Barros, B., Verdejo, M. F., Read, T., & Mizoguchi, R. (2002). Applications of a collaborative learning ontology. In Proceedings of the Mexican International Conference on Artificial Intelligence, LNCS 2313, 103–118.Google Scholar
  7. Cognition and Technology Group at Vanderbilt. (1992). Anchored instruction in science education. In R. Duschl & R. Hamilton (Eds.), Philosophy of science, cognitive psychology, and educational theory and practice (pp. 244–273). Albany: SUNY.Google Scholar
  8. Collins, A. (1991). Cognitive apprenticeship and instructional technology. In L. Idol & B. F. Jones (Eds.), Educational values and cognitive instruction: Implications for reform (pp. 121–138). Hillsdale: Erlbaum.Google Scholar
  9. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). Introduction to algorithms. Cambridge: The MIT.Google Scholar
  10. Deibel, K. (2005). Team formation methods for increasing interaction during in-class group work. ACM SIGCSE Bulletin, 37(3), 291–295.CrossRefGoogle Scholar
  11. Dillenbourg, P. (2002). Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In P. A. Kirschner (Ed.), Three worlds of CSCL. Can we support CSCL? (pp. 61–91). Heerlen: Open University Nederland.Google Scholar
  12. Devedzic, V. (2006). Semantic web and education. New York: Springer.Google Scholar
  13. Endlsey, W. R. (1980). Peer tutorial instruction. Englewood Cliffs: Educational Technology.Google Scholar
  14. Ertmer, P. A., & Newby, T. J. (1993). Behaviorism, cognitivism, constructivism: comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 6(4), 50–70.Google Scholar
  15. Faria, E. S. J., Adán-Coello, J. M., Yamanaka, K. (2006). Forming groups for collaborative learning in introductory computer programming courses based on students’ programming styles: An empirical study. In Procceedings of the ASEE/IEEE Frontiers in Education Conference, S4E-6–S4E-11.Google Scholar
  16. Fiechtner, S. B., & Davis, E. A. (1985). Why some groups fail: a survey of students’ experiences with learning groups. Organizational Behavior Teaching Review, 9(4), 75–88.Google Scholar
  17. Graf, S., & Bekele, R. (2006). Forming heterogeneous groups for intelligent collaborative learning systems with ant colony optimization. In Proceedings of Intelligent Tutoring Systems, LNCS 4053, 217–226.Google Scholar
  18. Greer, J., McCalla, G., Cooke, J., Collins, J., Kumar, V., Bishop, A., & Vassileva, J. (1998). The Intelligent Helpdesk: Supporting Peer-Help in a University Course. International Conference on Intelligent Tutoring Systems, LNCS 1452, 494–503.Google Scholar
  19. Harrer, A., McLaren, B. M., Walker, E., Bollen, L., & Sewall, J. (2006). Creating cognitive tutors for collaborative learning: steps toward realization. User Modeling and User-Adapted Interaction, 16(3–4), 175–209.CrossRefGoogle Scholar
  20. Hayashi, Y., Bourdeau, J., & Mizoguchi, R. (2006). Ontological support for a theory-eclectic approach to instructional and learning design. In Proceedings of the European Conference on Technology Enhanced Learning, LNCS 4227, 155–169.Google Scholar
  21. Hayashi, Y., Bourdeau, J., & Mizoguchi, R. (2008). Structurization of learning/instructional design knowledge for theory-aware authoring systems. In Proceedings of the International Conference on Intelligent Tutoring Systems, LNCS 5091, 573–582.Google Scholar
  22. Inaba, A., & Mizoguchi, R. (2004). Learners’ roles and predictable educational benefits in collaborative learning. In Proceedings of the International Conference on Intelligent Tutoring Systems, LNCS 3220, 285–294.Google Scholar
  23. Inaba, A., Supnithi, T., Ikeda, M., & Mizoguchi, R. (2000). How can we form effective collaborative learning groups. In Proceedings of Intelligent Tutoring Systems, LNCS 1839, 282–291.Google Scholar
  24. Inaba, A., Ohkubo, R., Ikeda, M., & Mizoguchi, R. (2002). An interaction analysis support system for CSCL. In Proceedings of the International Conference on Computers in Education. IEEE Press. 358–362.Google Scholar
  25. Inaba, A., Ikeda, M., & Mizoguchi, R. (2003). What learning patterns are effective for a learner’s growth? In Proceedings of the International Conference on Artificial Intelligence in Education, 219–226.Google Scholar
  26. Isotani, S., & Mizoguchi, R. (2006). An integrated framework for fine-grained analysis and design of group learning activities. In Proceedings of the International Conference on Computers in Education, IOS Press, v. 151, 193–200.Google Scholar
  27. Isotani, S., & Mizoguchi, R. (2007). Deployment of ontologies for an effective design of collaborative learning scenarios. In Proceedings of the International Workshop on Groupware, LNCS 4715, 223–238.Google Scholar
  28. Isotani, S., & Mizoguchi, R. (2008). Adventures in the boundary between domain-independent ontologies and domain content for CSCL. In Proceedings of the International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, LNAI 5179, 523–532.Google Scholar
  29. Kobbe, L., Weinberger, A., Dillenbourg, P., Harrer, A., Hämäläinen, R., & Fischer, F. (2007). Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2(2–3), 211–224.CrossRefGoogle Scholar
  30. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. New York: Cambridge University Press.Google Scholar
  31. Miao, Y., Hoeksema, K., Hoppe, H. U., & Harrer, A. (2005). CSCL scripts: Modelling features and potential use. In Proceedings of the International Conference on Computer Support for Collaborative Learning, 423–432.Google Scholar
  32. Mizoguchi, R., Sunagawa, R., Kozaki, K., & Kitamura, Y. (2007). The model of roles within an ontology development tool: Hozo. Applied Ontology, 2(2), 159–179.Google Scholar
  33. Muhlenbrock, M. (2005). Formation of learning groups by using learner profiles and context information. In Proceedings of the International Conference on Artificial Intelligence in Education, 507–514.Google Scholar
  34. Ounnas, A., Davis, H., & Millard, D. (2008). A framework for semantic group formation. In Proceedings of the IEEE International Conference on Advanced Learning Technologies, 34–38.Google Scholar
  35. Psyche, V., Bourdeau, J., Nkambou, R., & Mizoguchi, R. (2005). Making learning Design standards works with an ontology of educational theories. In Proceedings of the International Conference on Artificial Intelligence in Education, 539–546.Google Scholar
  36. Resta, P., & Laferrière, T. (2007). Technology in support of collaborative learning. Educational Psychology Review, 19(1), 65–83.CrossRefGoogle Scholar
  37. Rumelhart, D. E., & Norman, D. A. (1978). Accretion, tuning, and restructuring: Three modes of learning. In J. W. Cotton & R. Klatzky (Eds.), Semantic factors in cognition (pp. 37–53). Hillsdale: Erlbaum.Google Scholar
  38. Romiszowski, A. J. (1981). Designing instructional systems. New York: Nichols.Google Scholar
  39. Salomon, G. (1993). Distributed cognitions. Cambridge: Cambridge University Press.Google Scholar
  40. Sassenberg, K., & Karl-Andrew, W. (2008). Group-based self-regulation: the effects of regulatory focus. European Review of Social Psychology, 19, 126–164.CrossRefGoogle Scholar
  41. Soh, L., Khandaker, N., & Jiang, H. (2008). I-MINDS: a multiagent system for intelligent computer-supported collaborative learning and classroom management. Journal of Artificial Intelligence in Education, 18(2), 119–151.Google Scholar
  42. Soller, A. (2001). Supporting social interaction in an intelligent collaborative learning system. International Journal of Artificial Intelligence in Education, 12(1), 40–62.Google Scholar
  43. Soller, A., Martínez-Monés, A., Jermann, P., & Muehlenbrock, M. (2005). From mirroring to guiding: a review of state of the art technology for supporting collaborative learning. Journal of Artificial Intelligence in Education, 15(4), 261–290.Google Scholar
  44. Spiro, R. J., Coulson, R. L., Feltovich, P. J., & Anderson, D. K. (1988). Cognitive flexibility theory: Advanced knowledge acquisition in ill-structured domains. In Proceedings of the Annual Conference of the Cognitive Science Society, 375–383.Google Scholar
  45. Stahl, G., Koschmann, T., & Suthers, D. (2006). CSCL: An historical perspective. Cambridge handbook of the learning sciences (pp. 409–426). Cambridge: Cambridge University Press.Google Scholar
  46. Strijbos, J. W., & Fischer, F. (2007). Methodological challenges for collaborative learning research. Learning & Instruction, 17(4), 389–393.CrossRefGoogle Scholar
  47. Strijbos, J. W., Martens, R. L., & Jochems, W. M. G. (2004). Designing for interaction: six steps to designing computer-supported group-based learning. Computers and Education, 42(4), 403–424.CrossRefGoogle Scholar
  48. Suthers, D. D., Dwyer, N., Medina, R., & Vatrapu, R. (2007). A framework for eclectic analysis of collaborative interaction. In Proceedings of the International Conference on Computer Supported Collaborative Learning (CSCL), 694–703.Google Scholar
  49. Vygotsky, L. S. (1978). Mind in society: The development of the higher psychological processes. Cambridge: Harvard University Press. (re-publication).Google Scholar
  50. Wessner, M., & Pfister, H. (2001). Group formation in computer-supported collaborative learning. In Proceedings of ACM CSCW, 24–31.Google Scholar

Copyright information

© International Society of the Learning Sciences, Inc.; Springer Science + Business Media, LLC 2009

Authors and Affiliations

  • Seiji Isotani
    • 1
    Email author
  • Akiko Inaba
    • 1
  • Mitsuru Ikeda
    • 2
  • Riichiro Mizoguchi
    • 1
  1. 1.Department of Knowledge Systems, The Institute of Scientific and Industrial ResearchOsaka UniversityIbarakiJapan
  2. 2.Department of Knowledge ScienceJapan Advanced Institute of Science and TechnologyNomiJapan

Personalised recommendations