Abstract
In recent years, unobtrusive measures of self-regulated learning (SRL) processes based on log data recorded by digital learning environments have attracted increasing attention. However, researchers have also recognised that simple navigational log data or time spent on pages are often not fine-grained enough to study complex SRL processes. Recent advances in data-capturing technologies enabled researchers to go beyond simple navigational logs to measure SRL processes with multi-channel data. What multi-channel data can reveal about SRL processes, and to what extent can the addition of peripheral and eye-tracking data with navigational log data change and improve the measurement of SRL are key questions that require further investigation. Hence, we conducted a study and collected learning trace data generated by 25 university students in a laboratory setting, that aimed to address this problem by enhancing navigational log data with peripheral and eye-tracking data. We developed a trace-based measurement protocol of SRL, which interpreted raw trace data from multi-channel data into SRL processes. Specifically, the study compared the frequency and duration of SRL processes detected, how much duration and times of occurrences of the detected SRL processes were affected or refined. We also used a process mining technique to analyses how temporal sequencing of the detected SRL processes changed by enriching navigational log data with peripheral and eye-tracking data. The results revealed that by adding new data channels, we improved the capture of learning actions and detected SRL processes while enhancing the granularity of the measurement. In comparison to the use of navigational logs only, the completeness of temporal sequencing relationships between SRL processes with multi-channel data improved. In addition, we concluded that eye-tracking data is valuable for measuring and extracting SRL processes, and it should receive more attention in the future.
