The Mediation Effect of Online Self-Regulated Learning Between Engagement and Cognitive Load: A Case of an Online Course With Smart Instant Feedback

The Mediation Effect of Online Self-Regulated Learning Between Engagement and Cognitive Load: A Case of an Online Course With Smart Instant Feedback

Jerry Chih-Yuan Sun, Yiming Liu
Copyright: © 2022 |Pages: 17
DOI: 10.4018/IJOPCD.295953
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Abstract

This study aims to apply PLS-SEM to investigate the mediation roles of goal setting and task strategies, two constructs of online self-regulated learning, between behavioral/cognitive engagement and germane/extraneous cognitive load in an online course with smart instant feedback. Participants of this study consisted of 35 graduate students who were asked to complete four units of digital learning materials and questionnaires as part of the experiment. Results show that goal setting has a significant mediation effect between behavioral engagement and germane cognitive load; task strategies have a significant mediation effect between behavioral engagement and extraneous cognitive load; and task strategies also have a significant mediation effect between cognitive engagement and extraneous cognitive load. Finally, recommendations are provided to instructors and researchers based on these results as a reference for future studies.
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Introduction

In response to rapid developments in technology, changing lifestyles, and the increase in enrolments, online learning has been embraced by institutions of higher education (Sun et al., 2018) and is increasingly becoming a “new normal” of learning because of the COVID-19 outbreak (Hew et al., 2020). In the United States, although higher education enrolment has been declining overall, distance education enrolment continues to grow, and online courses have become a part of education in many traditional universities to meet students’ demands (Allen & Seaman, 2016). This shift has led to dramatic changes in students’ learning environments (Clayton et al., 2018). Therefore, many studies have compared online courses with conventional courses, but their findings are inconsistent (Bazelais & Doleck, 2018; Lo & Hew, 2020; Thai et al., 2020).

Jaggars and Xu (2016) argued that differences between online and traditional courses are due to the design of the former. Yang et al. (2020) found that the design of online courses played a vital role in students’ success in online learning environments. Chickering and Gamson (1999) developed the Seven Principles for Good Practice targeted at the design of traditional face-to-face courses. Al-Furaih (2017) proposed that the seven principles for good practice can guide instructors to create engaging and effective online courses and serve as criteria to evaluate the effectiveness of digital learning environments. Although the seven principles for good practice have emphasized the importance of delivering timely feedback, there is a notable lack of studies to explore how to develop instant and meaningful feedback in online learning environments (Sun & Hsu, 2019; Uribe & Vaughan, 2017). Additionally, recent studies (e.g., Kim et al., 2018; Schumacher & Ifenthaler, 2021) have pointed out that online learners are required to apply self-regulated learning strategies to achieve intended learning goals because the lack of interaction with instructors and peers leads students to struggle with independently managing their own learning. Despite this, few studies have combined self-regulated learning with instant feedback to activate online learners to perform self-regulated learning strategies such as goal setting, time management, and self-evaluation. In this study, we developed a smart instant feedback system based on online self-regulated learning, and integrated it into an online course on research ethics.

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