Students Use of Self-testing Learning Strategies: What Dispositional Learning Analytics Can Tell

What insights can dispositional learning analytics provide about how first-year business and economics students approach their introductory math and statistics course? This study aims to understand student engagement with learning tasks, tools, and materials in their academic pursuits. It utilizes trace data, initial assessments of students’ learning dispositions, and responses from the Study of Learning Questionnaire (SLQ) to analyze their preferred learning strategies. An innovative aspect of this research is its focus on clarifying how learning dispositions influence and potentially predict the adoption of specific learning strategies. SLQ data is examined to identify profiles that represent typical patterns of self-testing and the use of worked examples as preferred strategies, and relate these profiles to students’ learning dispositions, learning activity in e-tutorials, and module performance. Data was collected from two cohorts of students, totaling 2,400 first-year students. An important conclusion drawn from our research is that adaptability, the ability to modify preferred learning strategies based on the learning context, is crucial for success across a broad range of learning dispositions. However, when focusing narrowly on module performance, the self-testing strategy using e-tutorials proves to be the most successful, in contrast to self-testing with self-made or peer-generated materials.