A learning analytic approach to unveiling self-regulatory processes in learning tactics

Abstract

Investigation of learning tactics and strategies has received increasing attention by the Learning Analytics (LA) community. While previous research efforts have made notable contributions towards identifying and understanding learning tactics from trace data in various blended and online learning settings, there is still a need to deepen our understanding about learning processes that are activated during the enactment of distinct learning tactics. In order to fill this gap, we propose a learning analytic approach to unveiling and comparing self-regulatory processes in learning tactics detected from trace data. Following this approach, we detected four learning tactics (Reading with Quiz Tactic, Assessment and Interaction Tactic, Short Login and Interact Tactic and Focus on Quiz Tactic) as used by 728 learners in an undergrad course. We then theorised and detected five micro-level processes of self-regulated learning (SRL) through an analysis of trace data. We analysed how these micro-level SRL processes were activated during enactment of the four learning tactics in terms of their frequency of occurrence and temporal sequencing. We found significant differences across the four tactics regarding the five micro-level SRL processes based on multivariate analysis of variance and comparison of process models. In summary, the proposed LA approach allows for meaningful interpretation and distinction of learning tactics in terms of the underlying SRL processes. More importantly, this approach shows the potential to overcome the limitations in the interpretation of LA results which stem from the context-specific nature of learning. Specifically, the study has demonstrated how the interpretation of LA results and recommendation of pedagogical interventions can also be provided at the level of learning processes rather than only in terms of a specific course design.

Dispositional Learning Analytics for Supporting Individualized Learning Feedback

“…The statistical analysis of this study is based on the creation of disposition profiles by cluster-analytic methods (Fan et al, 2021;Matz et al, 2021). These profiles represent relatively homogeneous subsamples of students created from the very heterogeneous total sample.…”

“…Even if we can successfully reconstruct behavioral proxies of learning dispositions, such as Salehian Kia et al ( 2021), these come with a substantial delay. It takes time for trace data to settle down in stable patterns that are sufficiently informative to create trace-based dispositions, as illustrated in Fan et al (2021).…”

“…In a study of 38 students completing 430 sessions, Salehian Kia et al (2021) were able to model respective SRL phases in two assignments based upon trace data. In an undergraduate module with 728 learners, Fan et al (2021) were able to identify four distinct SRL processes based upon students' trace data. Similar developments can be observed in the user modeling, adaptation, and personalization (UMAP) research community, where the tradeoff between prior information on learners and information generated by mining behavioral data is a subject of investigation (e.g., Akhuseyinoglu and Brusilovsky, 2021).…”

A Multi-Level Trace Clustering Analysis Scheme for Measuring Students' Self-Regulated Learning Behavior in a Master-Based Online Learning Environment

“…In m-clusters 5 (AYSn) and 6 (SYSn), a study event followed either a normal or a short first attempt. In those three clusters, it is likely that students are guessing on the assessments after either having taken the first attempt (3) or after studying the instructional materials (5,6). Since the theory-driven distance metric produced m-clusters with both better structure and better interpretability, we chose to use those results in Level III clustering analysis.…”

“…SRL identifies students as playing active roles in their learning by engaging in planning, monitoring, and reflection strategies during learning [24,27], which is imperative for online learning as students are less reliant on instructors for in-the-moment assistance and are required to engage in more independent, structured, and regimented learning to keep up with class activities and content mastery. A number of recent studies have employed multiple data mining techniques such as sequence pattern analysis, process mining, and hierarchical clustering to identify, visualize and compare students' SRL strategies from trace data [6,16], with the latest example being the Trace-SRL framework developed by Saint et. al.…”