Comparison of the Effectiveness and Performance of Student Workgroups in Online Wiki Activities with and without AI

Abstract

Collaborative learning has been widely acknowledged as a successful teaching method within the education field, with research indicating its positive impact on student outcomes. During the COVID-19 pandemic, when all courses transitioned online due to lockdown measures, many universities employed learning management systems to facilitate continued group work among students. However, forming effective student groups remained challenging, particularly given the large number of enrolled students. To address this issue, this study proposes the application of an artificial intelligence (machine learning) solution to automatically group students based on their behaviours and interactions within an e-learning environment. This paper explores the potential of machine learning (ML) algorithms in assisting educators to create heterogeneous groups, considering various student attributes, such as behaviour and performance, to optimise collaborative learning outcomes. Students’ performance within a module was compared using a wiki activity that employed group work over the course of two academic years. In the first experiment, groups were formed randomly, while in the second experiment, students with similar behaviours were firstly identified using a clustering algorithm and then organised by an additional algorithm into heterogeneous groups. The results demonstrate the efficacy of the machine learning solution compared to the random approach in assisting educators with group formation for a collaborative activity such as the wiki, confirmed by a comparative analysis showing an improvement in student performance and satisfaction. This research contributes to the advancement of online education through the creation of more effective group dynamics using machine learning algorithms, thereby improving overall student learning.

Keywords

• machine learning
• clustering
• learning management systems