Replication Data for: Understanding Self-Regulation Strategies in Problem-Based Learning through Dispositional Learning Analytics

In the ongoing discussion about how learning analytics can effectively support self-regulated student learning and which types of data are most suitable for this purpose, this empirical study aligns with the framework proposed by Buckingham Shum and Deakin Crick (2012) who advocated the inclusion of both behavioural trace data and survey data in learning analytics studies. By incorporating learning dispositions in our learning analytics modelling, this research aims to investigate and understand how students engage with learning tasks, tools, and materials in their academic endeavours.

This is achieved by analysing trace data, which captures digital footprints of students' interactions with digital tools, along with survey responses from the Study of Learning Questionnaire (SLQ), to comprehensively examine their preferred learning strategies. Additionally, the study explores the relationship between these strategies and students' learning dispositions measured at the start of the course. An innovative aspect of this investigation lies in its emphasis on understanding how learning dispositions act as antecedents and potentially predict the utilization of specific learning strategies.