Exploring performance engagement in online postgraduate learning : utilisation of digital activities

Abstract

BACKGROUND TO THE STUDY : As fully online postgraduate programmes expand, questions remain regarding whether sufficient student engagement is achieved and how such sufficiency can be measured. This study examined the types and levels of engagement within a fully online postgraduate module and explored how engagement can be operationalised using learning management system (LMS) analytics. OBJECTIVE : To explore whether there is sufficient student engagement in an online module, and the types and levels of online engagement. METHODS : A quantitative single-case study analysed LMS trace data from 773 students. Data were analysed using the Online Engagement Framework and Moore's interaction typology. Engagement was operationalised using four behavioural indicators: submissions, interactions, time-on-platform and Grade Center access. Cluster analysis was applied to identify engagement profiles. RESULTS : Findings indicate high levels of social, cognitive, behavioural and collaborative engagement, with participation substantially exceeding minimum requirements. In contrast, structured opportunities for emotional engagement were absent. Frequent Grade Centre access (mean = 68 views per student) suggests a digitally observable form of performance engagement characterised by academic self-monitoring behaviour Cluster analysis revealed four distinct engagement profiles, highlighting heterogeneity in student interaction patterns. CONCLUSION : The findings suggest that high-density programmatic assessment is associated with sustained engagement behaviours in online contexts. This study contributes to the literature by proposing a trace-based operationalisation of performance engagement and offering a practical framework for examining engagement sufficiency in fully online programmes.

KEY POINTS • What is already known about this topic ○ Student engagement predicts success in online learning. ○ Engagement is multidimensional (behavioural, cognitive, social, emotional). ○ LMS analytics are increasingly used to measure engagement. • What this paper adds ○ Demonstrates how engagement sufficiency can be operationalised using LMS trace data. ○ Introduces performance engagement as digitally observable academic self-monitoring behaviour ○ Identifies four distinct engagement profiles using clustering. • Implications for practice and/or policy ○ Assessment design strongly shapes engagement behaviour. ○ Time-on-platform alone is insufficient as an engagement indicator. ○ Emotional engagement requires intentional design in online programmes. ○ Multidimensional analytics dashboards may better support early identification of diverse engagement patterns.