Dysregulated Learning with Advanced Learning Technologies
Open accessAbstract
Successful learning with advanced learning technologies is based on the premise that learners adaptively regulate their cognitive and metacognitive behaviors during learning. However, there is abundant empirical evidence that suggests that learners typically do not adaptively modify their behavior, thus suggesting that they engage in what is called dysregulated behavior. Dysregulated learning is a new term that is used to describe a class of behaviors that learners use that lead to minimal learning. Examples of dysregulated learning include failures to: (1) encode contextual demands, (2) deploy effective learning strategies, (3) modify and update internal standards, (4) deal with the dynamic nature of the task, (5) metacognitively monitor the use of strategies and make accurate metacognitive judgments, and (6) intelligently adapt behavior during learning so as to maximize learning and understanding of the instructional material. Understanding behaviors associated with dysregulated learning is critical since it has implications for determining what they are, when they occur, how often they occur, and how they can be corrected during learning.
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Learners often struggle with effective learning regulation, particularly in digital settings, leading to missing learning outcome effects.
“learners have been found to often struggle with effective learning regulation, particularly in digital settings, leading to missing learning outcome effects (e.g., Azevedo & Feyzi-Behnagh, 2011;Lim et al., 2021).”
From How do students regulate their learning with a genAI chatbot? (2026) · cites this paper as Azevedo & Feyzi-Behnagh, 2011
Cites
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893 · see citing works
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Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning
Lim, L., Bannert, M., van der Graaf, J., Singh, S., Fan, Y., Surendrannair, S., Rakovic, M., Molenaar, I., Moore, J., & Gašević, D. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107547 · Cited by 198 · see citing works
Temporal Assessment of Self-Regulated Learning by Mining Students’ Think-Aloud Protocols
Lim, L., Bannert, M., van der Graaf, J., Molenaar, I., Fan, Y., Kilgour, J., Moore, J., & Gašević, D. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.749749 · Cited by 44 · see citing works
How do students regulate their learning with a genAI chatbot?
Lim, L., & Bannert, M. (2026) · Learning Letters · doi:10.20851/ll.v8.61
- Authors
- Azevedo, R., & Feyzi-Behnagh, R.
- Affiliations
- McGill University (CA)
- Venue
- Journal of e-Learning and Knowledge Society
- Publisher
- Journal of e-Learning and Knowledge Society
- Published
- 2011-05-31
- Volume
- 7
- Issue
- 2
- Pages
- 9-18
- OpenAlex
- W2133210753
- Cited by
- 43
- References
- 1
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 1 · 2022: 2 · 2021: 8 · 2020: 1 · 2019: 3 · 2018: 1 · 2017: 3 · 2016: 3 · 2015: 3 · 2014: 2 · 2013: 2 · 2012: 6
- FWCI
- 7.14
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Società Italiana di e-Learning
- JE-LKS. Journal of E-Learning and Knowledge Society (cc-by)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Keywords
- metacognition, metacognitive monitoring, metacognitive judgments, learning strategies, contextual demands, educational technology
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