The Role of Learning Analytics in Evaluating Course Effectiveness

Billy T. M. Wong (), Kam Cheong Li and Mengjin Liu
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Billy T. M. Wong: Institute for Research in Open and Innovative Education, Hong Kong Metropolitan University, Homantin, Kowloon, Hong Kong, China
Kam Cheong Li: Institute for Research in Open and Innovative Education, Hong Kong Metropolitan University, Homantin, Kowloon, Hong Kong, China
Mengjin Liu: Institute for Research in Open and Innovative Education, Hong Kong Metropolitan University, Homantin, Kowloon, Hong Kong, China

Sustainability, 2025, vol. 17, issue 2, 1-22

Abstract: This study aims to examine the use of learning analytics in course evaluation within higher education institutions, in order to identify effective methodologies and best practices for leveraging data to improve educational effectiveness. Following the PRISMA guidelines, a systematic literature search was conducted in Scopus, yielding 34 relevant studies published between 2015 and 2024 for analysis. The results reveal six key categories of learning analytics applications: sentiment analysis, questionnaire analysis, engagement analysis, topic classification, predictive modelling, and performance analysis. The data sources for learning analytics applications primarily include questionnaires and learning management systems. While descriptive analysis was found to be the most commonly employed analytical technique, advanced techniques such as machine learning, artificial intelligence, and social network analysis are becoming more prominent. The studies addressed a wide range of elements associated with course evaluation, including course design, content quality, assignments, instructional strategies, workload, feedback mechanisms, and the integration of technology. These findings highlight the importance of adopting holistic approaches to capture the multifaceted nature of student experiences. This study also uncovers major limitations in the existing research, such as small sample sizes, potential biases due to the use of survey-based methods, and challenges in generalising findings across disciplines. These insights underscore the need for further research to enhance the methodologies used in course evaluations. This study contributes to advancing learning analytics practices and emphasises the importance of innovative approaches for evaluating and improving course effectiveness.

Keywords: learning analytics; course evaluation; machine learning; educational data mining; educational quality (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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