The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Synthesizing Cognitive Load and Self-regulation Theory: a Theoretical Framework and Research Agenda
Open accessAbstract
An exponential increase in the availability of information over the last two decades has asked for novel theoretical frameworks to examine how students optimally learn under these new learning conditions, given the limitations of human processing ability. In this special issue and in the current editorial introduction, we argue that such a novel theoretical framework should integrate (aspects of) cognitive load theory and self-regulated learning theory. We describe the effort monitoring and regulation (EMR) framework, which outlines how monitoring and regulation of effort are neglected but essential aspects of self-regulated learning. Moreover, the EMR framework emphasizes the importance of optimizing cognitive load during self-regulated learning by reducing the unnecessary load on the primary task or distributing load optimally between the primary learning task and metacognitive aspects of the learning task. Three directions for future research that derive from the EMR framework and that are discussed in this editorial introduction are: (1) How do students monitor effort? (2) How do students regulate effort? and (3) How do we optimize cognitive load during self-regulated learning tasks (during and after the primary task)? Finally, the contributions to the current special issue are introduced.
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“Relating these insights to the EMR framework (de Bruin etal., 2020), we posit that perceived effort is continuously experienced and invested effort is dynamically regulated by the learner, where task difficulty and perceived learning are important but potentially misleading cues learners use to regulate their effort.”
From Worth the Effort: the Start and Stick to Desirable Difficulties (S2D2) Framework (2023) · cites this paper as de Bruin et al., 2020
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“There is a risk that strategies to address metacognitive demands may also increase cognitive load, due to the additional information that users have to process, such as self-reflective prompts, a set of sub-goals resulting from task decomposition, or model explanations [46].”
From The Metacognitive Demands and Opportunities of Generative AI (2024) · cites this paper as [46]
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Cites
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Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998) · Educational Psychology Review · doi:10.1023/a:1022193728205 · Cited by 6,023
Test-Enhanced Learning: Taking Memory Tests Improves Long-Term Retention
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Metamemory: A Theoretical Framework and New Findings
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Allen, B. (1995) · Information Processing & Management · doi:10.1016/0306-4573(95)80050-4 · Cited by 877
Accuracy of metacognitive monitoring affects learning of texts.
Thiede, K. W., Anderson, M. C. M., & Therriault, D. J. (2003) · Journal of Educational Psychology · doi:10.1037/0022-0663.95.1.66 · Cited by 780
Learning Versus Performance: An Integrative Review
Soderstrom, N. C., & Bjork, R. A. (2015) · Perspectives on Psychological Science · doi:10.1177/1745691615569000 · Cited by 651
Supporting Self-Regulated Learning in Online Learning Environments and MOOCs: A Systematic Review
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2018.1543084 · Cited by 631
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Research works printed but not linked: 2
- Baars, M., Wijnia, L., de Bruin, A. B. H., & Paas, F. (this issue). The relation between student’s effort and monitoring judgments during learning: A Metaanalysis. Educational Psychology Review.
- Scheiter, K., Ackerman, & Hoogerheide, V. (this issue). Looking at mental effort appraisals through a metacognitive lens: Are they biased? Educational Psychology Review.
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Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025) · Brain Sciences · doi:10.3390/brainsci15020203 · Cited by 309
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 493
Nückles, M., Roelle, J., Glogger-Frey, I., Waldeyer, J., & Renkl, A. (2020) · Educational Psychology Review · doi:10.1007/s10648-020-09541-1 · Cited by 213
Self-citationCastro-Alonso, J. C., de Koning, B. B., Fiorella, L., & Paas, F. (2021) · Educational Psychology Review · doi:10.1007/s10648-021-09606-9 · Cited by 278
Chauncey, S. A., & McKenna, H. P. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100182 · Cited by 91
Metadata
- Authors
- de Bruin, A. B. H., Roelle, J., Carpenter, S. K., Baars, M., & EFG-MRE
- Affiliations
- Maastricht University (NL)
- Ruhr University Bochum (DE)
- Iowa State University (US)
- Erasmus University Rotterdam (NL)
- Corresponding author
- de Bruin, Anique B. H.
- Venue
- Educational Psychology Review
- Published
- 2020-10-12
- Volume
- 32
- Issue
- 4
- Pages
- 903-915
- ISSN
- 1040-726X (ISSN-L)
- 1573-336X
- OpenAlex
- W3092407193
- Cited by
- 196
- References
- 42
- Citations by year
- 2026: 49 · 2025: 40 · 2024: 32 · 2023: 30 · 2022: 20 · 2021: 16 · 2020: 9
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Educational Psychology Review (publishedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- RUNE (Research UNE) (submittedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion, cc-by)
- RePub (Erasmus University Rotterdam) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VI.Vidi.195.135)
- 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
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Keywords
- cognitive load theory, student self-regulation, effort monitoring, effort regulation, metacognition, load distribution, information overload, theoretical integration
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