Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning
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Channeling novices into worked examples and advanced students directly into problem solving yielded more efficient learning outcomes.
“Kalyuga and Sweller (2005) addressed this variability by developing pretests that categorize students as novices or advanced students and showed that channeling the novices into worked examples and the advanced students directly into problem solving yielded more efficient learning outcomes.”
From The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning (2012) · cites this paper as Kalyuga and Sweller (2005)
For more experienced students, straight problem solving yielded better learning than interleaved worked examples and problem solving.
“In contrast to the positive results of worked examples for novice students, a number of studies (e.g., Kalyuga et al., 2003Kalyuga et al., , 2005) ) have demonstrated that for more experienced students, straight problem solving yielded better learning than interleaved worked examples and problem solving.”
From The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning (2012) · cites this paper as Kalyuga et al., 2005
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“This definition was different from that first suggested by Paas and van Merriënboer (1993) and used in our previous studies (e.g., Kalyuga et al., 2001).”
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Wang, T., Zheng, J., Tan, C., & Lajoie, S. P. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12824 · Cited by 35 · see citing works
Testing Is More Desirable When It Is Adaptive and Still Desirable When Compared to Note-Taking
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- Authors
- Kalyuga, S., & Sweller, J.
- Affiliations
- UNSW Sydney (AU)
- Corresponding author
- Kalyuga, Slava
- Venue
- Educational Technology Research and Development
- Published
- 2005-09
- Volume
- 53
- Issue
- 3
- Pages
- 83-93
- ISSN
- 1042-1629 (ISSN-L)
- 1556-6501
- OpenAlex
- W2166550729
- Cited by
- 265
- References
- 30
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 9 · 2023: 3 · 2022: 6 · 2021: 19 · 2020: 10 · 2019: 18 · 2018: 12 · 2017: 11 · 2016: 6 · 2015: 15 · 2014: 17 · 2013: 13 · 2012: 20
- FWCI
- 13.04
- Type
- journal article
- Language
- en
- Open access
- green
- handle.unsw.edu.au
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Educational Technology Research and Development (publishedVersion)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational and Psychological Assessments
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- adaptive e-learning, working memory, cognitive load, long-term memory retrieval, cognitive efficiency
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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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