Interrelated analysis of interaction, sequential patterns and academic achievement in online learning
Keywords:online learning, learner profile, age and programme features, course design, cluster analysis, sequential patterns
This study aimed to examine the behaviour of learners across a whole system and in various courses to reveal the interrelation between learners' system interaction, age, programme features and course design. We obtained data from the system logs of 1,634 learners enrolled in distance learning programmes. We performed hierarchical clustering analysis to describe system interactions; then, we carried out a sequential pattern analysis to examine navigational behaviours by clusters. The results showed that the system interactions (e.g., content, live lesson, assignment, exam, discussion) across the whole system differ by age and programme. The behaviour profiles of the learners changed when different course designs were presented. Learners who interacted more with any component (e.g., live lesson or content) according to their needs were more successful than those with limited interaction and assessment-oriented (those with limited interactions outside of the assignment). In an information and communication technology course, learners whose system interactions were sufficient to receive rewards were more likely to succeed. The sequential pattern analysis showed that the assessment-oriented cluster interacted with the assignment in the midterm weeks; the award-oriented cluster interacted with the content or completed their assignment and received an award. Consequently, it is difficult to determine or generalise the intervention unless the system, programme and course design features are standard.
Implications for practice or policy:
- Course designers can use the assessment activities or motivation factors such as awards to increase students' system interactions.
- Course designers should not determine or generalise interventions unless the system, programme and course design are standard.
- Researchers should not only focus on data but also consider the contextual characteristics of data.
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Copyright (c) 2022 Denizer YILDIRIM, Yasemin USLUEL
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