Collaborative Learning Management System with Analytical Insights: A Preliminary Study DOI: https://doi.org/10.33093/ijomfa.2024.5.1.6
Main Article Content
Abstract
The mode of teaching and learning had been drastically changed over the decades. Therefore, one approach might not fit into all scenarios. Collaborative learning promotes collaboration between the students in completing given tasks with common goals. In this paper, problem statements were formed: (i) the collaboration between students and their teachers in the virtual learning environment has been at the bare minimum, (ii) the learning management system implemented has not been fully utilised with the data and information collected academically. Moreover, systematic literature review (SLR) is practised to investigate insights about collaborative learning, learning management system (LMS) and analytical approaches for student profiling. The aim of this paper is to address three research questions formed in the SLR: (i) What is the most commonly practised methodology for collaborative learning? (ii) What are the typical practised analytical methods and models for student profiling? and (iii) What factors influence students to use the learning management system? Besides, collaborative learning enables the students to conduct group discussions and assignments, promoting mutual interactions and creating knowledge amongst them. Additionally, the third-party LMS lacks synchronous chat feature. A student's profile grants educators valuable insights into the student's academic performance and learning progress. This information contributes to predicting the student’s performance with the assistance of analytical approaches applied. The applied analytical approaches provide useful information about the student’s learning behaviour, allowing the teachers to take adequate action. As a result, a conceptual framework is constructed with hypotheses formulated, reflecting the relations between each construct. Besides, a dedicated collaborative learning management system with machine learning capabilities is an ideal solution, tackling students’ collaboration among peers and between teachers with their academic performance and behaviour taken into account.
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References
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