Learning analytics on student engagement to enhance students' learning performance: a systematic review

• Main Authors: Johar, Nurul Atiqah, Kew, Si Na, Tasir, Zaidatun, Koh, Elizabeth
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: L Education (General)
• Online Access: http://eprints.utm.my/107320/1/NurulAtiqahJohar2023_LearningAnalyticsonStudentEngagementtoEnhance.pdf; http://eprints.utm.my/107320/; http://dx.doi.org/10.3390/su15107849

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.