The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Why is science difficult to learn? Things are seldom what they seem
Abstract
The difficulties of learning science are related to the nature of science itself and to the methods by which science is customarily taught without regard to what is known about children's learning. An information processing model is proposed to guide thinking and research in this area.
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“In 1991, Johnstone arranged the three levels as the vertices of a triangle ('triangle of levels of thought') and renamed them the macro, submicro, and symbolic levels (Johnstone, 1991).”
From An analysis of the visual representation of redox reactions and related content in Brazilian secondary school chemistry textbooks (2020) · cites this paper as Johnstone, 1991
Citations
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Metadata
- Authors
- Johnstone, A. H.
- Affiliations
- University of Glasgow (GB)
- Corresponding author
- Johnstone, A. H.
- Venue
- Journal of Computer Assisted Learning
- Publisher
- Wiley
- Published
- 1991-06
- Volume
- 7
- Issue
- 2
- Pages
- 75-83
- ISSN
- 0266-4909 (ISSN-L)
- 1365-2729
- OpenAlex
- W2166624758
- Semantic Scholar
- e5120cb07075e3a2e46612bfad670ce951b27178
- Cited by
- 1,321
- References
- 6
- Citations by year
- 2026: 103 · 2025: 77 · 2024: 78 · 2023: 79 · 2022: 96 · 2021: 109 · 2020: 80 · 2019: 109 · 2018: 78 · 2017: 48 · 2016: 55 · 2015: 57 · 2014: 56 · 2013: 50 · 2012: 44
- FWCI
- 2.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computer Assisted Learning (publishedVersion)
- Topics
- Social Sciences › Education › Science Education and Pedagogy
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- science learning difficulties, nature of science, science teaching methods, children's learning, information processing model, cognitive models of learning
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