Prior knowledge activation: how different concept mapping tasks lead to substantial differences in cognitive processes, learning outcomes, and perceived self-efficacy
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Pre-constructed concept maps are more effective as pre-training for learners with lower prior knowledge than having learners create their own.
“research suggests that pre-constructed concept maps are more effective as pre-training for learners with lower prior knowledge than having them create their own (Gurlitt and Renkl, 2008; Gurlitt and Renkl, 2010).”
From Rethinking pre-training: cognitive load implications for learners with varying prior knowledge (2025) · cites this paper as Gurlitt and Renkl, 2010
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- Authors
- Gurlitt, J., & Renkl, A.
- Affiliations
- University of Freiburg (DE)
- Corresponding author
- Gurlitt, Johannes
- ORCID
- Renkl, Alexander
- Venue
- Instructional Science
- Published
- 2009-01-08
- Volume
- 38
- Issue
- 4
- Pages
- 417-433
- ISSN
- 0020-4277 (ISSN-L)
- 1573-1952
- OpenAlex
- W2022556519
- Cited by
- 103
- References
- 60
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 11 · 2023: 7 · 2022: 5 · 2021: 5 · 2020: 11 · 2019: 6 · 2018: 3 · 2017: 6 · 2016: 11 · 2015: 8 · 2014: 6 · 2013: 5 · 2012: 8
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Instructional Science (publishedVersion)
- 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
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Keywords
- concept mapping, prior knowledge activation, cognitive processes, elaboration, model development, think-aloud, learning outcomes, self-efficacy, coherence effects, hypertext learning, economics education, meteorology education
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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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