Towards a Methodological Framework for Sequence Analysis in the Field of Self-Regulated Learning

Abstract

In recent decades, conceptualizations and operationalizations of self-regulated learning (SRL) have shifted from SRL as an aptitude to SRL as an event. Alongside this shift, increased technological capability has introduced computer log files to the investigation of SRL, uncovering new research avenues. One such avenue investigates the time-related characteristics of SRL through learners' behavioural sequences. Although sequence analysis is still relatively new in SRL research, other fields have fruitful traditions in its application and may serve as a basis for applications in the field of SRL. Ten years of investigating SRL through sequence analysis have produced a wide range of methodological approaches. While this variety of methods illustrates the diversity of opportunities, it also indicates the lack of consensus regarding the most appropriate approaches often resulting in difficult to understand methods and nontransparent ways of reporting. Since the introduction of sequence analysis in the field of SRL, researchers have been emphasizing the need for a methodological framework to guide its application. Yet, to date, no such framework has been proposed, hindering our progress through (1) transparent methods and (2) comparative studies to (3) empirical and ecological applications. To help overcome this issue, this manuscript discusses the basis of a methodological framework for the use of sequence analysis in SRL research. We first make a case for why such a framework is necessary; secondly, we propose a set of guidelines which could serve as a starting point for the construction of a framework.

A mixed method approach to studying self-regulated learning in MOOCs

“…Learners whose trace data included sequences indicative of all defined SRL activities had greater persistence in the course and achieved higher course grades. These exemplary studies show trace data may be beneficial for studying SRL in online education: trace data are gathered unobtrusively, can be analysed automatically for large groups of learners, and may in the future even be used for real time support of learners (Van Laer & Elen, 2018).…”

“…Trace data provide a reliable measure of when students engaged in the online learning environment and with what materials they engaged, and offers information about temporal and sequential characteristics of activities, making it a readily available and valuable data source to study learners' SRL (Cicchinelli et al, 2018;Fincham, Gasevic, Jovanovic, & Pardo, 2018;Hadwin et al, 2007;Kizilcec, Pérez-Sanagustín, & Maldonado, 2017;Maldonado-Mahauad, Pérez-Sanagustín, Kizilcec, Morales, & Munoz-Gama, 2018;Winne, 2014). Several authors have used trace data to attempt to locate learner behaviour that is representative of SRL activities (Van Laer & Elen, 2018). For example, in the study of Kizilcec et al (2017), SRL questionnaire data were coupled to learner behaviour information stored in trace data to identify learner activities related to SRL.…”

“…In order to examine students' SRL in online education, researchers have increasingly focused their attention on trace data (Van Laer & Elen, 2018;Winne, 2010). In online education, all learners' interactions with the online course materials (e.g., videos, assignments, forum discussions) are stored.…”

“…These so-called traces of learner behaviour are stored as time stamped events, providing an overview of all learners activities within the online learning environment. Using trace data as an indicator for learners' SRL is considered a promising approach because the data are gathered automatically, unobtrusively, and over longer durations of time (Rovers, Clarebout, Savelberg, De Bruin, & Van Merriënboer, 2019;Van Laer & Elen, 2018). Over the past years, the call for fine-grained process measures of SRL has strengthened (e.g., Winne, 2010) and researchers have therefore started using trace data to measure SRL (e.g., Cicchinelli et al, 2018;Van Laer & Elen, 2018).…”

“…Using trace data as an indicator for learners' SRL is considered a promising approach because the data are gathered automatically, unobtrusively, and over longer durations of time (Rovers, Clarebout, Savelberg, De Bruin, & Van Merriënboer, 2019;Van Laer & Elen, 2018). Over the past years, the call for fine-grained process measures of SRL has strengthened (e.g., Winne, 2010) and researchers have therefore started using trace data to measure SRL (e.g., Cicchinelli et al, 2018;Van Laer & Elen, 2018). Using data mining techniques, trace data could be used to automatically measure and support SRL, for example by automatically detecting when students show a lack of self-regulation and subsequently offering feedback, suggestions, or other forms of interventions.…”