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Synthesizing Cognitive Load and Self-regulation Theory: a Theoretical Framework and Research Agenda
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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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