Which log variables significantly predict academic achievement? A systematic review and meta‐analysis. (24th October 2022)

• Record Type: Journal Article
• Authors: Wang, Qin; Mousavi, Amin
• Is Part Of: British journal of educational technology. Volume 54:Number 1(2023)
• Page Start: 142
• Page End: 191
• Publication Date: 2022-10-24
• Subjects: learning analytics -- log analysis -- online learning -- academic achievement -- meta‐analysis; Audio-visual education -- Periodicals; 371.33
• DOI: 10.1111/bjet.13282
• Languages: English
• ISSNs: 0007-1013

Abstract

Abstract: Technologies and teaching practices can provide a rich log data, which enables learning analytics (LA) to bring new insights into the learning process for ultimately enhancing student success. This type of data has been used to discover student online learning patterns, relationships between online learning behaviors and assessment performance. Previous studies have provided empirical evidence that not all log variables were significantly associated with student academic achievement and the relationships varied across courses. Therefore, this study employs a systematic review with meta‐analysis method to provide a comprehensive review of the log variables that have an impact on student academic achievement. We searched six databases and reviewed 88 relevant empirical studies published from 2010 to 2021 for an in‐depth analysis. The results show different types of log variables and the learning contexts investigated in the reviewed studies. We also included four moderating factors to do moderator analyses. A further significance test was performed to test the difference of effect size among different types of log variables. Limitations and future research expectations are provided subsequently. Practitioner notes What is already known about this topic Significant relationship between active engagement in online courses and academic achievement was identified in a number of previous studies. Researchers have reviewed the literature to examine different aspects of applying LA to gain insights for monitoring student learning in digital environments (eg, data sources, data analysis techniques). What this paper adds Presents a new perspective of the log variables, which provides a reliable quantitative conclusion of log variables in predicting student academic achievement. Conducted subgroup analysis, examined four potential moderating variables and identified their moderating effect on several log variables such as regularity of study interval, number of online sessions, time‐on‐task, starting late and late submission. Compared the effect of generic and course‐specific, basic and elaborated log variables, and found significant difference between the basic and elaborated. Implications for practice and/or policy A depth of understanding of these log variables may enable researchers to build robust prediction models. It can guide the instructors to timely adjust teaching strategies according to their online learning behaviors.