Collaborative Learning Team Formation: A Cognitive Modeling Perspective

  • Yuping Liu
  • Qi Liu
  • Runze Wu
  • Enhong ChenEmail author
  • Yu Su
  • Zhigang Chen
  • Guoping Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9643)


With a number of students, the purpose of collaborative learning is to assign these students to the right teams so that the promotion of skills of each team member can be facilitated. Although some team formation solutions have been proposed, the problem of extracting more effective features to describe the skill proficiency of students for better collaborative learning is still open. To that end, we provide a focused study on exploiting cognitive diagnosis to model students’ skill proficiency for team formation. Specifically, we design a two-stage framework. First, we propose a cognitive diagnosis model SDINA, which can automatically quantify students’ skill proficiency in continuous values. Then, given two different objectives, we propose corresponding algorithms to form collaborative learning teams based on the cognitive modeling results of SDINA. Finally, extensive experiments demonstrate that SDINA could model the students’ skill proficiency more precisely and the proposed algorithms can help generate collaborative learning teams more effectively.



This research was partially supported by grants from the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), the Natural Science Foundation of China (Grant No. 61403358) and the Science and Technology Program for Public Wellbeing (Grant No. 2013GS340302). Qi Liu gratefully acknowledges the support of the Youth Innovation Promotion Association of CAS and acknowledges the support of the CCF-Intel Young Faculty Researcher Program (YFRP).


  1. 1.
    Agrawal, R., Golshan, B., Terzi, E.: Grouping students in educational settings. In: SIGKDD, pp. 1017–1026. ACM (2014)Google Scholar
  2. 2.
    Christodoulopoulos, C.E., Papanikolaou, K.: Investigation of group formation using low complexity algorithms. In: Proceeding of PING, Workshop, pp. 57–60 (2007)Google Scholar
  3. 3.
    De La Torre, J.: Dina model and parameter estimation: a didactic. J. Educ. Behav. Stat. 34(1), 115–130 (2009)CrossRefGoogle Scholar
  4. 4.
    De La Torre, J.: The generalized dina model framework. Psychometrika 76(2), 179–199 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    DeCarlo, L.T.: On the analysis of fraction subtraction data: the dina model, classification, latent class sizes, and the q-matrix. APM (2010)Google Scholar
  6. 6.
    Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)CrossRefGoogle Scholar
  7. 7.
    Desmarais, M.C.: Mapping question items to skills with non-negative matrix factorization. ACM SIGKDD Explor. Newsl. 13(2), 30–36 (2012)CrossRefGoogle Scholar
  8. 8.
    DiBello, L.V., Roussos, L.A., Stout, W.: 31a review of cognitively diagnostic assessment and a summary of psychometric models. Handb. Stat. 26, 979–1030 (2006)CrossRefzbMATHGoogle Scholar
  9. 9.
    Embretson, S.E., Reise, S.P.: Item response theory for psychologists. Psychology Press, New York (2013)Google Scholar
  10. 10.
    Gall, M.D., Gall, J.P.: The discussion method. The psychology of teaching methods, (75 ppt 1), pp. 166–216 (1976)Google Scholar
  11. 11.
    Gibbs, G.: Learning in teams: a tutor guide. Oxford Centre for Staff and Learning Development (1995)Google Scholar
  12. 12.
    Gogoulou, A., Gouli, E., Boas, G., Liakou, E., Grigoriadou, M.: Forming homogeneous, heterogeneous and mixed groups of learners. In: Proceeding ICUM, pp. 33–40 (2007)Google Scholar
  13. 13.
    Graf, S., Bekele, R.: Forming heterogeneous groups for intelligent collaborative learning systems with ant colony optimization. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 217–226. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Hooper, S., Hannafin, M.J.: Cooperative cbi: The effects of heterogeneous versus homogeneous grouping on the learning of progressively complex concepts. J. Educ. Comput. Res. 4(4), 413–424 (1988)CrossRefGoogle Scholar
  15. 15.
    Hwang, G.-J., Yin, P.-Y., Hwang, C.-W., Tsai, C.-C., et al.: An enhanced genetic approach to composing cooperative learning groups for multiple grouping criteria. Educ. Technol. Soc. 11(1), 148–167 (2008)Google Scholar
  16. 16.
    Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of the 15th SIGKDD, pp. 467–476. ACM (2009)Google Scholar
  17. 17.
    Li, Q., Wang, P., Wang, W., Hu, H., Li, Z., Li, J.: An efficient K-means Clustering Algorithm on MapReduce. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014, Part I. LNCS, vol. 8421, pp. 357–371. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  18. 18.
    Mahdi, B., Fattaneh, T.: A semi-pareto optimal set based algorithm for grouping of students. In: ICELET, pp. 10–13. IEEE (2013)Google Scholar
  19. 19.
    Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: Advances in neural information processing systems, pp. 1257–1264 (2007)Google Scholar
  20. 20.
    Ounnas, A., Davis, H., Millard, D.: A framework for semantic group formation. In: ICALT, pp. 34–38. IEEE (2008)Google Scholar
  21. 21.
    Ozaki, K.: Dina models for multiple-choice items with few parameters considering incorrect answers. In: APM (2015)Google Scholar
  22. 22.
    Slavin, R.E.: Cooperative learning: theory, research, and practice, vol. 14. Allyn and Bacon, Boston (1990)Google Scholar
  23. 23.
    Smith, K.A., Sheppard, S.D., Johnson, D.W., Johnson, R.T.: Pedagogies of engagement: classroom-based practices. JEE 94(1), 87–101 (2005)Google Scholar
  24. 24.
    Štajner, T., Thomee, B., Popescu, A.-M., Pennacchiotti, M., Jaimes, A.: Automatic selection of social media responses to news. In: 19th ACM SIGKDD, pp. 50–58. ACM (2013)Google Scholar
  25. 25.
    Toscher, A., Jahrer, M.: Collaborative filtering applied to educational data mining. In: KDD Cupp (2010)Google Scholar
  26. 26.
    Wu, R., Liu, Q., Liu, Y., Chen, E., Su, Y., Chen, Z., Hu, G.: Cognitive modelling for predicting examinee performance. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 1017–1024. AAAI Press (2015)Google Scholar
  27. 27.
    Yannibelli, V., Amandi, A.: A deterministic crowding evolutionary algorithm to form learning teams in a collaborative learning context. Expert Syst. Appl. 39(10), 8584–8592 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yuping Liu
    • 1
  • Qi Liu
    • 1
  • Runze Wu
    • 1
  • Enhong Chen
    • 1
    Email author
  • Yu Su
    • 2
  • Zhigang Chen
    • 1
  • Guoping Hu
    • 3
  1. 1.University of Science and Technology of ChinaHefeiChina
  2. 2.Anhui UniversityHefeiChina
  3. 3.Anhui USTC IFLYTEK Co., Ltd.HefeiChina

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