Self-Regulated Learning and Online Learning: A Systematic Review

Adam, Noor Latiffah; Alzahri, Fatin Balkis; Soh, Shaharuddin Cik; Bakar, Nordin Abu; Kamal, Nor Ashikin Mohamad

doi:10.1007/978-3-319-70010-6_14

Cited by 59 publications

(57 citation statements)

References 31 publications

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“…The online learning conditions were less likely to be instructor-directed than they were to be learner-directed. Independent learning and collaborative learning are significant features of online learning (Adam et al, 2017). It is surmised that SRL is more important for online learning than traditional learning, which may yield different effect size.…”

Section: Discussionmentioning

confidence: 99%

What Are the Effects of Self-Regulation Phases and Strategies for Chinese Students? A Meta-Analysis of Two Decades Research of the Association Between Self-Regulation and Academic Performance

Tang

et al. 2018

Front. Psychol.

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Background: Self-regulated learning refers to the monitoring and controlling of one's own cognitive performance before, during, and after a learning episode. Previous literature suggested that self-regulated learning had a significant relationship with academic achievement, but not all self-regulated learning strategies exerted the same influences. Using an invalid strategy may waste the limited psychological resources, which will cause the ego depletion effect. The present meta-analysis study intended to search for the best self-regulated learning strategies and inefficient strategies for Chinese students in elementary and secondary school, and analyzed the critical phases of self-regulated learning according to Zimmerman's theory. The moderating effects of gender, grade, and publication year were also analyzed.Methods: Empirical studies which conducted in real teaching situations of elementary and secondary education were systematically searched using Chinese academic databases. Studies focused on undergraduate students, students of special education, or online learning environments were excluded. Fifty-five cross-sectional studies and four intervention studies (which generated 264 independent samples) were included with a total sample size of 23,497 participants. Random effects model was chosen in the current meta-analysis, and publication bias was also examined.Results: The results indicated that the overall effect size of self-regulated learning on academic achievement was small for primary and secondary school students in China. The effect sizes of self-efficacy, task strategies, and self-evaluation were relatively higher than other strategies. Self-regulated learning strategies have the largest effect size on science disciplines (including mathematics and physics). Performance phase and self-reflection phase are key phases of self-regulated learning. From 1998 to 2016, the effect size between self-regulated learning and academic achievement was gradually decreasing.Conclusions: The main findings of the current study showed that self-efficacy, task strategies, and self-evaluation were key self-regulated learning strategies for Chinese students. Performance phase and self-reflection phase played significant roles in the process of self-regulated learning. Future studies need to include more intervention studies with rigorous treatment fidelity control and provide more empirical evidence from online learning, so as to compare the different effects of self-regulated learning between traditional education and online education.

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Section: Discussionmentioning

confidence: 99%

What Are the Effects of Self-Regulation Phases and Strategies for Chinese Students? A Meta-Analysis of Two Decades Research of the Association Between Self-Regulation and Academic Performance

Tang

et al. 2018

Front. Psychol.

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“…These studies have greatly contributed to our understanding that use of SRL skills positively enhances academic performance for learners (Adam, Alzahri, Cik Soh, Abu Bakar, & Mohamad Kamal, 2017;Garcia et al, 2018;Lee et al, 2019;Roth et al, 2016;Wong et al, 2018). Despite these developments in SRL, our understanding on the measurement tools and instruments to measure SRL for online learning environments is limited as evidenced on the use of traditional tools that were developed for face-toface still being used for measuring SRL online.…”

Section: Previous Review Studiesmentioning

confidence: 99%

Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018)

Araka

Maina

Gitonga

et al. 2020

RPTEL

121

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For the last one decade, research in self-regulated learning (SRL) and educational psychology has proliferated. Researchers and educators have focused on how to support leaners grow their SRL skills on both face-to-face and e-learning environments. In addition, recent studies and meta-analysis have greatly contributed to the domain knowledge on the use of SRL strategies and how they contribute and boost academic performance for learners. However, there is little systematic review on the literature on the techniques and tools used to measure SRL on e-learning platforms. This review sought to outline recent advances and the trends in this area to make it more efficient for researchers to establish the empirical studies and research patterns among different studies in the field of SRL. The findings from this study are concurrent with existing empirical evidence that traditional methods designed for classroom supports are being used for measuring SRL on e-learning environments. Few studies have used learner analytics and educational data mining (EDM) techniques to measure and promote SRL strategies for learners. The paper finally points out the existing gaps with the tools presently used to measure and support SRL on learning management systems and recommends further studies on the areas of EDM which can support SRL.

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“…One reason that explains the interest in the SRL lies in the fact that we live in a society where lifelong learning process is increasingly important and informal learning environments require self-regulation skills. Moreover, SRL can support students self-awareness and selfregulation level so as to enhance their cognitive, metacognitive, motivation and engagement in an online learning environment [18,19]. The three phases of SRL, namely: forethought phase (goal setting and planning), performance phase (task strategy, help-seeking and intrinsic interest), and the last phase, self-reflection (self-evaluation and self-monitoring); and their sub-processes are derived from commonly used models such as the Zimmerman, Pintrich, Boekaert, Corno and Mandinach, and Winne and Hadwin models.…”

Section: B Learning Theorymentioning

confidence: 99%

New Development and Evaluation Model for Self-Regulated Smart Learning Environment in Higher Education

Gambo

Shakir

2019

2019 IEEE Global Engineering Education Conference (EDUCON)

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The smart learning environment is a technologysupported learning environment that is enriched with digital resources, context-aware and adaptive devices, and can provide appropriate support to meet the learning style and abilities of diverse students to promote better and enhances learning process in higher education. It is a student-centered learning environment that has the capacity to engage students and offers an effective learning process. It is characterized by the ability to provide interactions between students and facilitators, and personalized and inclusive learning experiences to anyone, anytime and anywhere using smart mobile devices. However, despite the increasing use of smart learning environment in higher education, there is no well-defined model with a set of educational requirements for developing and evaluating selfregulated smart learning environment which considers both instructional and evaluation design. Therefore, this article explores instructional design and learning evaluation process to propose a new set of educational requirements referred to as smart learning environment pedagogical and educational requirements model (SLE-PERM) for developing and evaluating self-regulated smart learning environment. Next, the article presents the application of the educational requirements on three common learning management system (LMS) namely: Moodle, Blackboard and Schoology which can be used to evaluate the suitability of LMS to guide stakeholders on the critical issues that require pedagogical and educational requirements for developing a self-regulated smart learning environment in higher education.

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Self-Regulated Learning and Online Learning: A Systematic Review

Cited by 59 publications

References 31 publications

What Are the Effects of Self-Regulation Phases and Strategies for Chinese Students? A Meta-Analysis of Two Decades Research of the Association Between Self-Regulation and Academic Performance

What Are the Effects of Self-Regulation Phases and Strategies for Chinese Students? A Meta-Analysis of Two Decades Research of the Association Between Self-Regulation and Academic Performance

Research trends in measurement and intervention tools for self-regulated learning for e-learning environments—systematic review (2008–2018)

New Development and Evaluation Model for Self-Regulated Smart Learning Environment in Higher Education

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