Learning Analytics on Student Engagement to Enhance Students’ Learning Performance: A Systematic Review

Nurul Atiqah Johar, Si Na Kew (), Zaidatun Tasir and Elizabeth Koh
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Nurul Atiqah Johar: School of Education, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
Si Na Kew: Language Academy, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
Zaidatun Tasir: School of Education, Universiti Teknologi Malaysia, Skudai 81310, Malaysia
Elizabeth Koh: National Institute of Education, Nanyang Technological University, Singapore 639798, Singapore

Sustainability, 2023, vol. 15, issue 10, 1-25

Abstract: The study of learning analytics provides statistical analysis and extract insights from data, particularly in education. Various studies regarding student engagement in online learning have been conducted at tertiary institutions to verify its effects on students’ learning performance. However, there exists a knowledge gap whereby the types of student-engagement issues derived from learning analytics have not been collectively studied thus far. In order to bridge the knowledge gap, this paper engages a new systematic literature review (SLR) that analysed 42 articles using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The existing research on student engagement in online learning does not extensively integrate the five types of online engagement proposed by Redmond et al., and the use of learning analytics on the subject matter is also limited. Thus, this review sheds light on the types of student engagement indicated by using learning analytics, hoping to enhance students’ learning performance in online learning. As revealed in the findings, some studies measured multifaceted engagement to enhance students’ learning performance, but they are limited in number. Thus, it is recommended that future research incorporate multifaceted engagement such as social, cognitive, collaborative, behavioural, and emotional engagement in online learning and utilise learning analytics to improve students’ learning performance. This review could serve as the basis for future research in online higher education.

Keywords: student engagement; learning analytics; online learning; PRISMA; systematic review (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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