An Investigation into the Application of Learning Analytics in Collaborative Learning

Collaborative learning has been recognised for its potential to enhance learning outcomes. With the advent of relevant data collection and analysis techniques, learning analytics has emerged as a powerful tool to support and enhance collaborative learning experiences. This paper offers a comprehensive review of the application of learning analytics in enhancing collaborative learning. A total of 89 research articles, published between 2014 and 2023 and related to the use of learning analytics in collaborative learning, were sourced from Scopus for analysis. The review focused on various aspects including the settings, objectives, data types, and techniques associated with the use of learning analytics in collaborative learning. The findings indicate that online learning environments are the most common setting for collaborative learning activities supported by learning analytics, followed by blended and face-to-face settings. The primary objectives of learning analytics include monitoring and understanding collaborative processes, assessing engagement and participation, and predicting and improving performance. The most frequently used data types include behavioural and interaction data, as well as communication data, primarily collected from learning management systems, collaborative learning platforms, cameras, and sensors. The most prevalent learning analytics techniques include machine learning, network analysis, and statistical analysis. The results of this study contribute to informing the design and implementation of effective collaborative learning activities. By leveraging data-driven insights, instructors can optimise engagement, participation, and learning outcomes in collaborative learning settings.