Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations
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Combining self-explanation with worked examples can be especially beneficial to students' learning.
“It has been shown that combining self-explanation with WEs can be especially beneficial to students' learning [1].”
From Exploring the Impact of Worked Examples in a Novice Programming Environment (2019) · cites this paper as [1]
Berthold found that scaffolded self-explanation prompts following worked examples were more beneficial to students in terms of conceptual and procedural understanding than open-ended self-explanation prompts in multimedia environments.
“Berthold found that scaffolded self-explanation prompts following WEs were more beneficial to students in terms of conceptual and procedural understanding than open-ended self-explanation prompts in multimedia environments [1].”
From Exploring the Impact of Worked Examples in a Novice Programming Environment (2019) · cites this paper as [1]
Cites
Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models
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Kirschner, P. A., Sweller, J., & Clark, R. E. (2006) · Educational Psychologist · doi:10.1207/s15326985ep4102_1 · Cited by 6,887 · see citing works
Self-explanations: How students study and use examples in learning to solve problems
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Cognitive Theory of Multimedia Learning
Mayer, R. E. (2014) · The Cambridge Handbook of Multimedia Learning · doi:10.1017/cbo9781139547369.005 · Cited by 1,976 · see citing works
Cognitive Theory of Multimedia Learning
Mayer, R. E. (2005) · The Cambridge Handbook of Multimedia Learning · doi:10.1017/cbo9780511816819.004 · Cited by 1,602 · see citing works
The Equivalence of Learning Paths in Early Science Instruction
Klahr, D., & Nigam, M. (2004) · Psychological Science · doi:10.1111/j.0956-7976.2004.00737.x · Cited by 914 · cited 1× · see citing works
“Kirschner et al. (2006) and Klahr and Nigam (2004) advocate to provide the learners with more assistance relative to more open approaches (see Anderson et al. 1998).”
BackgroundBielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995) · Cognition and Instruction · doi:10.1207/s1532690xci1302_3 · Cited by 422 · see citing works
The Role of Visual Indicators in Dual Sensory Mode Instruction
Jeung, H., Chandler, P., & Sweller, J. (1997) · Educational Psychology · doi:10.1080/0144341970170307 · Cited by 295 · cited 1× · see citing works
“This combined flashing and color-coding procedure (Jeung et al. 1997; Kalyuga et al. 1999) was intended to prevent distracting visual search processes (Ayres and Sweller 2005).”
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Klepsch, M., Schmitz, F., & Seufert, T. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01997 · Cited by 601 · see citing works
Eysink, T. H. S., de Jong, T., Berthold, K., Kolloffel, B., Opfermann, M., & Wouters, P. (2009) · American Educational Research Journal · doi:10.3102/0002831209340235 · Cited by 158 · see citing works
Self-citationContemporary cognitive load theory research: The good, the bad and the ugly
Kirschner, P. A., Ayres, P., & Chandler, P. (2011) · Computers in Human Behavior · doi:10.1016/j.chb.2010.06.025 · Cited by 144 · see citing works
Making Sense of Generative Learning
Fiorella, L. (2023) · Educational Psychology Review · doi:10.1007/s10648-023-09769-7 · Cited by 135 · see citing works
Hefter, M. H., Berthold, K., Renkl, A., Riess, W., Schmid, S., & Fries, S. (2014) · Instructional Science · doi:10.1007/s11251-014-9320-y · Cited by 63 · see citing works
Self-citationExploring the Impact of Worked Examples in a Novice Programming Environment
Zhi, R., Price, T. W., Marwan, S., Milliken, A., Barnes, T., & Chi, M. (2019) · Proceedings of the 50th ACM Technical Symposium on Computer Science Education · doi:10.1145/3287324.3287385 · Cited by 55 · see citing works
The expertise reversal effect in prompting focused processing of instructional explanations
Roelle, J., & Berthold, K. (2013) · Instructional Science · doi:10.1007/s11251-012-9247-0 · Cited by 40 · see citing works
Self-citationBeyond the “Wow” Factor: Using Generative AI for Increasing Generative Sense-Making
Makransky, G., Shiwalia, B. M., Herlau, T., & Blurton, S. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10039-x · Cited by 27 · see citing works
Learning by explaining after pauses in video lectures: Are provided visuals a scaffold or a crutch?
Fiorella, L. (2022) · Applied Cognitive Psychology · doi:10.1002/acp.3994 · Cited by 19 · see citing works
Assessing efficiency of prompts based on learner characteristics
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Why do cognitive prompts hurt learning in older adults?
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- Authors
- Berthold, K., Eysink, T. H. S., & Renkl, A.
- Affiliations
- University of Freiburg (DE)
- University of Twente (NL)
- Corresponding author
- Berthold, Kirsten
- Venue
- Instructional Science
- Published
- 2008-04-01
- Volume
- 37
- Issue
- 4
- Pages
- 345-363
- ISSN
- 0020-4277 (ISSN-L)
- 1573-1952
- OpenAlex
- W1994927678
- Cited by
- 238
- References
- 8
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 18 · 2023: 14 · 2022: 11 · 2021: 18 · 2020: 21 · 2019: 9 · 2018: 17 · 2017: 15 · 2016: 12 · 2015: 15 · 2014: 14 · 2013: 10 · 2012: 14
- FWCI
- 11.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Instructional Science (publishedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
- University of Twente Research Information (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- self-explanation prompts, multiple representations, worked examples, procedural knowledge, conceptual understanding, deep understanding
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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.
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