Temporal Assessment of Self-Regulated Learning by Mining Students’ Think-Aloud Protocols

Lyn Lim,Joep Van Der Graaf,Inge Molenaar,Maria Bannert,Johanna Moore,Dragan Gašević,Yizhou Fan,Jonathan Kilgour

doi:10.3389/fpsyg.2021.749749

Abstract

It has been widely theorized and empirically proven that self-regulated learning (SRL) is related to more desired learning outcomes, e.g., higher performance in transfer tests. Research has shifted to understanding the role of SRL during learning, such as the strategies and learning activities, learners employ and engage in the different SRL phases, which contribute to learning achievement. From a methodological perspective, measuring SRL using think-aloud data has been shown to be more insightful than self-report surveys as it helps better in determining the link between SRL activities and learning achievements. Educational process mining on the basis of think-aloud data enables a deeper understanding and more fine-grained analyses of SRL processes. Although students’ SRL is highly contextualized, there are consistent findings of the link between SRL activities and learning outcomes pointing to some consistency of the processes that support learning. However, past studies have utilized differing approaches which make generalization of findings between studies investigating the unfolding of SRL processes during learning a challenge. In the present study with 29 university students, we measured SRL via concurrent think-aloud protocols in a pre-post design using a similar approach from a previous study in an online learning environment during a 45-min learning session, where students learned about three topics and wrote an essay. Results revealed significant learning gain and replication of links between SRL activities and transfer performance, similar to past research. Additionally, temporal structures of successful and less successful students indicated meaningful differences associated with both theoretical assumptions and past research findings. In conclusion, extending prior research by exploring SRL patterns in an online learning setting provides insights to the replicability of previous findings from online learning settings and new findings show that it is important not only to focus on the repertoire of SRL strategies but also on how and when they are used.

Highlights

A key competence for lifelong learning is self-regulated learning (SRL), otherwise known as “learning to learn,” and it refers to the ability to monitor and adapt one’s learning (European Union, 2019)
The findings of our study extended previous SRL research in digital and online learning settings and provided insights to whether previous findings were replicable, and whether our findings were still valid and coherent to older studies when applied to a different learning task and context
We examined the differences in temporal structures of learning activities between successful and less successful students (RQ3) by using process mining

Summary

Introduction

A key competence for lifelong learning is self-regulated learning (SRL), otherwise known as “learning to learn,” and it refers to the ability to monitor and adapt one’s learning (European Union, 2019). There needs to be more focus on SRL in digital and online learning, owing to the sharp increase in learning taking place in these settings, largely driven by the ongoing pandemic (EDUCAUSE, 2021). It has been widely theorized and empirically supported that SRL is related to more desired learning outcomes (e.g., performance in transfer tests; Panadero, 2017; Schunk and Greene, 2017). SRL consists of complex and dynamic activities and processes which are adapted as students regulate their learning and, need further investigation in order to support students’ learning (Azevedo et al, 2010; Winne, 2010).

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Conclusion