TY  - JOUR
T1  - A New Approach to Grouping Learners Based on Behavioral Engagement in CSCL Environments
AU  - Lafifi, Yacine
AU  - Zerdoudi, Souhila
AU  - Tadjer, Houda
AU  - Mehenaoui, Zohra
PY  - 2024
DA  - December
Y2  - 2024
DO  - 10.26650/acin.1567382
JF  - Acta Infologica
JO  - ACIN
PB  - Istanbul University
WT  - DergiPark
SN  - 2602-3563
SP  - 260
EP  - 273
VL  - 8
IS  - 2
LA  - en
AB  - In Computer-Supported Collaborative Learning (CSCL) environments, forming a group is essential for the success of the learning process. Furthermore, several studies on forming groups in CSCL environments have been conducted recently to form ones that promote learners’ engagement and collaboration among their members. Forming group-based approaches requires data on learners’ actions (or traces) during the learning process. In this study, behavioral traces of learners are used to form groups. In other words, we used a clustering algorithm based on learners’ behavioral engagement to form homogeneous groups of learners. The learners must have different levels of engagement within each group to enhance their engagement and cognitive levels. The basis of the proposed grouping algorithm is a set of indicators of learners’ engagement. Furthermore, the proposed approach is based on an artificial intelligence algorithm, the k-means clustering method, which is used to find the maximum possibilities for the best clusters. Then, another algorithm is applied to obtain groups of learners with different levels of behavioral engagement. The validation of the proposed approach on a dataset containing behavioral traces from 100 learners was encouraging and promoting.
KW  - CSCL
KW  - Group formation
KW  - K-means
KW  - Learning analytics
KW  - Learner engagement
CR  - Abnar, S., Orooji, F., & Taghiyareh, F. (2012, January). An evolutionary algorithm for forming mixed groups of learners in web based collaborative learning environments. In the IEEE international conference on technology enhanced education (ICTEE) (pp. 1-6). IEEE. google scholar
CR  - Amara, S., Macedo, J., Bendella, F., & Santos, A. (2016). Group formation in mobile computer-supported collaborative learning contexts: A systematic literature review. Journal of Educational Technology & Society, 19(2), 258-273. google scholar
CR  - Anzieu, D., & Martin, J. Y. (1971). La dynamique des groupes restreints. Presses univ. de France. google scholar
CR  - Atherton, M., Shah, M., Vazquez, J., Griffiths, Z., Jackson, B., & Burgess, C. (2017). Using learning analytics to assess student engagement and academic outcomes in open access enabling programmes. Open Learning: The Journal of Open, Distance and e-Learning, 32(2), 119-136. google scholar
CR  - Bekele, R. (2005). Computer-assisted learner group formation based on personality traits (Doctoral dissertation, Staats-und Universitatsbiblio-thek Hamburg Carl von Ossietzky). google scholar
CR  - Bouyzem, M., AL Meriouh, Y., & Moustakim, O. (2021). Le e-learning a l’universite Abdelmalek Essaâdi: Une analyse descriptive du point de vue des enseignants. Revue Economie, Gestion et Societe, 1 (32). google scholar
CR  - Chen, B., Chang, Y. H., Ouyang, F., & Zhou, W. (2018). Fostering student engagement in online discussion through social learning analytics. The Internet and Higher Education, 37, 21-30. google scholar
CR  - Christodoulopoulos, C. E., & Papanikolaou, K. A. (2007, October). A group formation tool in an e-learning context. In 19th IEEE international conference on tools with artificial intelligence (ICTAI 2007) (Vol. 2, pp. 117-123). IEEE google scholar
CR  - Cole, E., Mac Aodha, O., Lorieul, T., Perona, P., Morris, D., & Jojic, N. (2021). Multi-label learning from single positive labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 933-942). google scholar
CR  - Combaudon, S. (2018). MySQL 5.7: administracion y optimizacion. Ediciones Eni. google scholar
CR  - Da Rocha, H. (2019). Learn Chart. js: Create interactive visualizations for the web with chart. js 2. Packt Publishing, Ltd. google scholar
CR  - Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109. google scholar
CR  - Indira, B., Valarmathi, K., & Devaraj, D. (2019). An approach to enhance packet classification performance of software-defined network using deep learning. Soft Computing, 23, 8609-8619. google scholar
CR  - Isotani, S., Mizoguchi, R., Isotani, S., Capeli, O. M., Isotani, N., De Albuquerque, A. R., ... & Jaques, P. (2013). A Semantic Web-based authoring tool to facilitate the planning of collaborative learning scenarios compliant with learning theories. Computers & Education, 63, 267-284.b google scholar
CR  - Jozan, M. M. B., & Taghiyareh, F. (2013). An evolutionary algorithm for homogeneous grouping to enhance web-based collaborative learning. International Journal of Computer Science Research and Application, 3(1), 74-85. google scholar
CR  - Karaoglan Yilmaz, F. G., & Yilmaz, R. (2022). Learning analytics intervention improves students’ engagement in online learning. Technology, Knowledge and Learning, 27(2), 449-460. google scholar
CR  - Kirschner, P. A., Jochems, W., Dillenbourg, P., & Kanselaar, G. (2002). Three worlds of CSCL: Can we support CSCL. Heerlen: Open University of the Netherlands. google scholar
CR  - Maqtary, N., Mohsen, A., & Bechkoum, K. (2019). Group formation techniques in computer-supported collaborative learning: A systematic literature review. Technology, Knowledge and Learning, 24, 169-190. google scholar
CR  - Matazi, I., Messoussi, R., & Bennane, A. (2014, May). The design of an intelligent multi-agent system for supporting collaborative learning. In the 9th International conference on intelligent systems: Theories and applications (SITA-14) (pp. 1-8). IEEE. google scholar
CR  - Mujkanovic, A., Lowe, D., Willey, K., & Guetl, C. (2012, June). Unsupervised learning algorithm for adaptive group formation: Collaborative learning support in remotely accessible laboratories. In International Conference on Information Society (i-Society 2012) (pp. 50-57). IEEE. google scholar
CR  - O’Donnell, K., & Reschly, A. L. (2020). Assessment of student engagement. In A. L. Reschly, A. J. Pohl, & S. L. Christenson (Eds.), Student engagement: Effective academic, behavioral, cognitive, and affective interventions at school (pp. 55-76). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-37285-9_3. google scholar
CR  - Ounnas, A., Davis, H. C., & Millard, D. E. (2007). Semantic modeling for group formation. Workshop on Personalisation in E-Learning Environments at Individual and Group Level (PING) at the 11th International Conference on User Modeling UM2007, Corfu, Greece. google scholar
CR  - Ouyang, F., Chen, S., & Li, X. (2021). Effect of three network visualizations on students’ social-cognitive engagement in online discussions. British Journal of Educational Technology, 52(6), 2242-2262. google scholar
CR  - Rajabalee, Y. B., & Santally, M. I. (2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26(3), 2623-2656. google scholar
CR  - Reschly, A. L., Pohl, A., Christenson, S. L., & Appleton, J. J. (2017). Engaging adolescents in secondary schools. School Mental Health Services for Adolescents, 45-77. google scholar
CR  - Resta, P., & Laferriere, T. (2007). Technology in support of collaborative learning. Educational Psychology Review, 19, 65-83. google scholar
CR  - Scheuer, O., Loll, F., Pinkwart, N., & McLaren, B. M. (2010). Computer-supported argumentation: A review of the state of the art. International Journal of Computer-Supported Collaborative Learning, 5, 43-102. google scholar
CR  - Srba, I., & Bielikova, M. (2014). Dynamic group formation as an approach to collaborative learning support. IEEE Transactions on Learning Technologies, 8(2), 173-186. google scholar
CR  - Stahl, G., Koschmann, T., & Suthers, D. (2006). Aprendizaje Colaborativo apoyado por computador: Una perspectiva historica. G. Stahl, T. Koschmann, & D. Suthers, Aprendizaje Colaborativo apoyado por computador: Una perspectiva historica.(pag. 426). Cambridge: RK Sawyer. google scholar
CR  - Zheng, Y., Li, C., Liu, S., &Lu, W. (2018). An improved genetic approach for composing optimal collaborative learning groups. Knowledge-Based Systems, 139, 214-225. google scholar
CR  - Zhou, L., Zheng, X., Yang, D., Wang, Y., Bai, X., & Ye, X. (2021). Application of multi-label classification models for the diagnosis of diabetic complications. BMC Med Inform Decis., 21(1), 182. google scholar
UR  - https://doi.org/10.26650/acin.1567382
L1  - https://dergipark.org.tr/en/download/article-file/4288626
ER  -