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Learning Management Systems, Online Group Task, Collaboration Competence Level, Intelligent grouping Algorithm, Machine learning
The current Learning Management Systems used in e-learning lack intelligent mechanisms which can be used by an instructor to group learners during an online group task based on the learners’ collaboration competence level. In this paper, we discuss a novel approach for grouping students in an online learning group task based on individual learners’ collaboration competence level. We demonstrate how it can be applied in a Learning Management System such as Moodle using forum data. To create the collaboration competence levels, two machine learning algorithms for clustering namely Skmeans and Expectation Maximization (EM) were applied to cluster data and generate clusters based on learner’s collaboration competence. We develop an intelligent grouping algorithm which utilizes these machine learning generated clusters to form heterogeneous groups. These groups are automatically made available to the instructor who can proceed to assign them to group tasks. This approach has the advantage of dynamically changing the group membership based on learners’ collaboration competence level.
Elizaphan M. Maina, Robert O. Oboko, Peter W. Waiganjo,"Using Machine Learning Techniques to Support Group Formation in an Online Collaborative Learning Environment", International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.3, pp.26-33, 2017. DOI:10.5815/ijisa.2017.03.04
A. R. Anaya and J. G. Boticario, “Clustering Learners according to their Collaboration.,” Proc. 13th Int. Conf. Comput. Support. Coop. Work Des., 2009.
A. R. Anaya and J. G. Boticario, “Ranking Learner Collaboration according to their Interactions.,” 1st Annu. Eng. Educ. Conf., 2010.
A. R. Anaya and J. G. Boticario, “Content-free Collaborative Learning Modeling Using Data Mining. Special issue of User Modeling and User-Adapted Interaction on Data Mining in Education.,” 2011.
B. M. McLaren, O. Scheuer, and J. Miksatko, “Supporting collaborative learning and e-Discussions using artificial intelligence techniques,” Int. J. Artif. Intell. Educ., 2010.
R. Messeguer, E. Medina, D. Royo, L. Navarro, and J. P. Juarez, “Group Prediction in Collaborative Learning,” in In Intelligent Environments (IE), Sixth International Conference, 2010, pp. 350–355.
Y. Awuor and R. Oboko, “Automatic assessment of online discussions using text mining,” Int. J. Mach. Learn. Appl., vol. 1, no. 1, p. 7–pages, 2012.
B. N. L. Valetts and R. Gesa, “Modelling Collaborative Competence Level Using Machine Learning Techniques,” In e-Learning, pp. 56–60, 2008.
M. Wessner and H. R. Pfister, “Group formation in computer-supported collaborative learning,” in In Proceedings of the 2001 international ACM SIGGROUP conference on supporting group work, ACM, 2001, pp. 24–31.
J. Scott, Social Network Analysis : a handbook, 2nd ed. London: Sage, 2001.
D. R. Bacon, K. A. Stewart, and E. S. Anderson, “Methods of assigning players to teams: A review and novel approach,” Simul. Gaming, vol. 32, no. 1, pp. 6–17, 2001.
K. J. Chapman, M. Meuter, D. Toy, and L. Wright, “Can’t we pick our own groups? The influence of group selection method on group dynamics and outcomes,” J. Manag. Educ., vol. 30, no. 4, pp. 557–569, 2006.
S. Liu, M. Joy, and N. Griffiths, “iGLS: intelligent grouping for online collaborative learning,” in In Advanced Learning Technologies, 2009. ICALT 2009, Ninth IEEE International Conference, 2009, pp. 364–368.
M. Muehlenbrock, “Learning group formation based on learner profile and context,” Int. J. E-learning, vol. 5, no. 1, pp. 19–24, 2006.
J. Laffey, T. Tupper, D. Musser, and J. Wedman, “A computer-mediated support system for project-based learning,” Educ. Technol. Res. Dev., 1998.
D. W. Johnson, R. T. Johnson, and K. A. Smith, “Cooperative learning returns to college,” Change, vol. 30, no. 4, pp. 26–35, 1998.
A. Kaye, “Learning together apart. In A. R. Kaye,” Collab. Learn. Through Comput. Conf., 1992.
E. Muuro, P. W. Wagacha, and R. Oboko, “Models for Improving and Optimizing Online and Blended Learning in Higher Education,” Jared Keengwe and J. J. Agamba, Eds. IGI Global. Pennsylvania, USA, 2014, pp. 204–219.
C. Romero, S. Ventura, and E. Garcia, “Data mining in course management systems: Moodle case study and tutorial,” Comput. Educ., vol. 51, no. 1, pp. 368–384, 2008.
A. K. Jain, M. N. Murty, and P. J. Flynn, “Data Clustering: A Review,” ACM Comput. Surv., vol. 31, no. 3, pp. 264–323, 1999.
N. Sharma, A. Bajpai, and M. R. Litoriya, “Comparison the various clustering algorithms of Weka tools,” Int. J. Emerg. Technol. Adv. Eng., vol. 2, no. 5, May 2012.
R. Asif, A. Merceron, and M. K. Pathan, “Predicting student academic performance at degree level: a case study,” Int. J. Intell. Syst. Appl., vol. 7, no. 1, p. 49, 2014.
W. Hamalainen, J. Suhonen, E. Sutinen, and H. Toivonen, “Data mining in personalizing distance education courses,” in In World Conference on Open Learning and Distance Education, 2004, pp. 1–11.
J. F. Nunamaker, J. R. M. Chen, and T. D. M. Purdin, “Systems Development in Information Systems Research,” J. Manag. Inf. Syst., vol. 7, no. 3, pp. 89–106, 1991.
R. T. Aldahdooh and W. Ashour, “DIMK-means‘ Distance-based Initialization Method for K-means Clustering Algorithm,’” Int. J. Intell. Syst. Appl., vol. 5, no. 2, p. 41, 2013.
S. P. Algur and P. Bhat, “Web Video Object Mining: A Novel Approach for Knowledge Discovery,” Int. J. Intell. Syst. Appl., vol. 8, no. 4, 2016.
E. Muuro, P. Wagacha, R.Oboko, and J. M. Kihoro, “Students’ Perceived Challenges in an Online Collaborative Learning Environment: A Case of Higher Learning Institutions in Nairobi, Kenya,” Int. Rev. Res. Open Distance Learn., vol. 15, 2014.