Supporting self‐regulated learning in computer‐based learning environments: systematic review of effects of scaffolding in the domain of science education
Abstract
Despite the widespread assumption that students require scaffolding support for self‐regulated learning (SRL) processes in computer‐based learning environments (CBLEs), there is little clarity as to which types of scaffolds are most effective. This study offers a literature review covering the various scaffolds that support SRL processes in the domain of science education. Effective scaffolds are categorized and discussed according to the different areas and phases of SRL. The results reveal that most studies on scaffolding processes focus on cognition, whereas few focus on the non‐cognitive areas of SRL. In the field of cognition, prompts appear to be the most effective scaffolds, especially for processes during the control phase. This review also shows that studies have paid little attention to scaffold designs, learner characteristics, or various task characteristics, despite the fact that these variables have been found to have a significant influence. We conclude with the implications of our results on future design and research in the field of SRL using CBLEs.
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Interventions that cue users to reflect on their goals and strategies via question prompts or conversational interfaces have been shown to improve outcomes in the workplace, education, and other domains.
“Interventions that cue users to reflect on their goals and strategies via question prompts or conversational interfaces have been shown to improve outcomes in the workplace [92,122,209], education [51], and other domains [17,147].”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [51]
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How can primary school students learn self-regulated learning strategies most effectively?
Dignath, C., Buettner, G., & Langfeldt, H.-P. (2008) · Educational Research Review · doi:10.1016/j.edurev.2008.02.003 · Cited by 903 · cited 1× · see citing works
“Unfortunately, in practice, few students attain this level of competence (Dignath et al. 2008), and it has been stated that many students fail to exploit the opportunities afforded by CBLEs (Clarebout & Elen 2006).”
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The Metacognitive Demands and Opportunities of Generative AI
Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642902 · Cited by 280 · see citing works
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Baars, M., Khare, S., & Ridderstap, L. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.793002 · Cited by 36 · see citing works
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- Authors
- Devolder, A., van Braak, J., & Tondeur, J.
- Affiliations
- Ghent University (BE)
- Research Foundation - Flanders (BE)
- Corresponding author
- Devolder, A.
- ORCID
- Tondeur, J.
- Venue
- Journal of Computer Assisted Learning
- Publisher
- Wiley
- Published
- 2012-02-22
- Volume
- 28
- Issue
- 6
- Pages
- 557-573
- ISSN
- 0266-4909 (ISSN-L)
- 1365-2729
- OpenAlex
- W1933421241
- Cited by
- 258
- References
- 2
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 24 · 2023: 24 · 2022: 30 · 2021: 20 · 2020: 23 · 2019: 22 · 2018: 26 · 2017: 14 · 2016: 19 · 2015: 17 · 2014: 9 · 2013: 2
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computer Assisted Learning (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- student self-regulation, scaffolding, computer-based learning environments, science education, prompting, scaffold design, learner characteristics, task characteristics
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