A Study on Grouping Strategy of Collaborative Learning Based on Clustering Algorithm

  • Qingtang Liu
  • Shen BaEmail author
  • Jingxiu HuangEmail author
  • Linjing Wu
  • Chuanyuan Lao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10309)


As educational informatization develops continuously, blended learning has become the focus of increasing amount of research works. Among these studies, researchers indicate that collaborative learning, as an important teaching strategy, is of great effectiveness in promoting students’ learning performance either in E-learning or classroom teaching. However, due to the fact that different students may have different learning styles, it is crucial for teachers to take this factor into consideration. Therefore, to enhance the validity of grouping in collaborative learning, this paper proposes a grouping strategy based on the analysis results of students’ learning styles using K-Means and hierarchical clustering. Results of clustering analysis can provide a valuable reference no matter for homogeneous grouping or heterogeneous grouping.


Collaborative learning Learning style analysis Clustering analysis Education data mining Grouping strategy 



This research is supported by the National High-tech Research and Development Program (No. 2015AA015408) and the Fundamental Research Funds for the Central Universities (No. CCNU16A05023).


  1. 1.
    Piaget, J., Mays, W.: The principles of genetic epistemology. Philos. Q. 24(94), 87 (1997)Google Scholar
  2. 2.
    He, K.K.: The teaching mode, teaching method and teaching design of Constructivism. J. Peking Univ. (Soc. Sci. Ed.) 5, 74–81 (1997)Google Scholar
  3. 3.
    Chiu, M.M.: Adapting teacher interventions to student needs during cooperative learning. Am. Educ. Res. J. 41(2), 365–399 (2004)CrossRefGoogle Scholar
  4. 4.
    Prince, M.: Does active learning work? A review of research. J. Eng. Educ. 93(3), 223–231 (2004)CrossRefGoogle Scholar
  5. 5.
    Zhao, J.H., Li, K.D.: Collaborative learning and collaborative learning mode. Chin. Educ. Technol. 10, 5–6 (2000)Google Scholar
  6. 6.
    Dunn, R.: Learning style: state of the science. Theory Pract. 23(1), 10–19 (1984)CrossRefGoogle Scholar
  7. 7.
    Vygotsky, L.: Interaction between learning and development. Mind Soc. 79–91 (1978)Google Scholar
  8. 8.
    Chang, Y.C., Kao, W.Y., Chu, C.P., Chiu, C.H.: A learning style classification mechanism for e-learning. Comput. Educ. 53, 273–285 (2009)CrossRefGoogle Scholar
  9. 9.
    Tang, J., Li, H.J., Qiu, F.Y.: A research on collaborative grouping peer-model in mCSCL. Distant Educ. China 2, 48–51 (2012)Google Scholar
  10. 10.
    Hu, H., He, J.H.: Research of composing cooperative learning group based on enhanced ant colony optimization algorithm. Comput. Eng. Appl. 50(13), 137–141 (2014)Google Scholar
  11. 11.
    Ma, Y.Y., Yuan, J.: Based on GSDBK – means grouping algorithm research for networked collaborative learning. Electron. Sci. Technol. 29(12), 89–92 (2016)Google Scholar
  12. 12.
    Han, J.W., Kamber, M., Pei, J.: Data Mining, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2004)zbMATHGoogle Scholar
  13. 13.
    Bewley, A., Upcroft, B.: Advantages of exploiting projection structure for segmenting dense 3D points clouds. In: Proceedings of Australasian Conference on Robotics and Automation. University of New South Wales (2013)Google Scholar
  14. 14.
    Lin, C.F., Yeh, Y.C., Hung, Y.H., Chang, R.I.: Data mining for providing a personalized learning path in creativity: an application of decision trees. Comput. Educ. 68, 199–210 (2013)CrossRefGoogle Scholar
  15. 15.
    Andrew, M.: K-means and hierarchical clustering – tutorial slides.
  16. 16.
    Osmar, R.Z.: Data clustering. In: Principles of Knowledge Discovery in Databases.
  17. 17.
    Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3, 32–57Google Scholar
  18. 18.
    Kolb, D.A.: The Learning Style Inventory: Technical Manual. McBer and Company, Boston (1999)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Educational Information TechnologyCentral China Normal UniversityWuhanChina

Personalised recommendations