Goals and Learning in Microworlds
Open accessAbstract
We explored the consequences for learning through interaction with an educational microworld called Electric Field Hockey (EFH). Like many microworlds, EFH is intended to help students develop a qualitative understanding of the target domain, in this case, the physics of electrical interactions. Through the development and use of a computer model that learns to play EFH, we analyzed the knowledge the model acquired as it applied the game‐oriented strategies we observed physics students using. Through learning‐by‐doing on the standard sequence of tasks, the model substantially improved its EFH playing ability; however, it did so without acquiring any new qualitative physics knowledge. This surprising result led to an experiment that compared students' use of EFH with standard‐goal tasks against two alternative instructional conditions, specific‐path and no‐goal, each justified from a different learning theory. Students in the standard‐goal condition learned less qualitative physics than did those in the two alternative conditions, which was consistent with the model. The implication for instructional practice is that careful selection and analysis of the tasks that frame microworld use is essential if these programs are to lead to the learning outcomes imagined for them. Theoretically, these results suggest a new interpretation for numerous empirical findings on the effectiveness of no‐goal instructional tasks. The standing “reduced cognitive load” interpretation is contradicted by the success of the specific‐path condition, and we offer an alternative knowledge‐dependent interpretation.
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The benefits of prior knowledge activation are contingent on the relevance of the activation.
“Research on goal specificity (e.g., Miller etal., 1999; Vollmeyer etal., 1996), mechanisms of errorful generation (Cyr & Anderson, 2015), as well as prior PS-I work (e.g., Kapur, 2015; Schwartz etal., 2011) has suggested that the benefits of prior knowledge activation such as noticing inconsistencies across multiple problem instances, encoding critical features from instruction, and so on, are”
From When Problem Solving Followed by Instruction Works: Evidence for Productive Failure (2021) · cites this paper as Miller et al., 1999
In “Implications for Learning Theory: Knowledge Dependencies” (page unavailable), the paper says that learning transfers when goal-dependent relationships coincide with pedagogically relevant relationships. This supports the importance of relevance, but the passage concerns knowledge acquired through microworld interaction, not the benefits of activating prior knowledge or the stated examples of noticing inconsistencies and encoding features from instruction. (LLM, conf=0.82)
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Hu, Y., Wouters, P., van der Schaaf, M. F., & Kester, L. (2024) · Learning and Instruction · doi:10.1016/j.learninstruc.2024.101943 · Cited by 16 · see citing works
Khamparia, A., Pandey, B., & Mishra, B. P. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10142-2 · Cited by 11 · see citing works
Enhancing Learning through an Online Secondary School Educational Game
Kaufman, D. M., Sauvé, L., & Rénaud, L. (2011) · Journal of Educational Computing Research · doi:10.2190/ec.44.4.c · Cited by 11 · see citing works
Malkiewich, L. J., & Chase, C. C. (2019) · Instructional Science · doi:10.1007/s11251-019-09493-2 · Cited by 11 · see citing works
Aquamoose 3d: a constructionist approach to math learning motivated by artistic expression
Elliott, J., & Bruckman, A. S. (2005) · UPT. Syiah Kuala University Library (Syiah Kuala University) · Cited by 10 · see citing works
Ritzhaupt, A. D., Higgins, H. J., & Oyarzun, B. A. (2010) · Journal of Computers in Mathematics and Science Teaching · Cited by 9 · see citing works
Smith, C. (2005) · Nature · doi:10.1038/435991a · Cited by 9 · see citing works
Discovery of Ideas and Ideas about discovery
Hulshof, C. (2001) · University of Twente Research Information · Cited by 9 · see citing works
Anderson, D. I., & Steel, K. A. (2022) · Brazilian Journal of Motor Behavior · doi:10.20338/bjmb.v16i2.278 · Cited by 8 · see citing works
Motivational Factors for Learning by Teaching
Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Stylianides, G. J., & Koedinger, K. R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30950-2_14 · Cited by 8 · see citing works
Self-citationWhen Practice Doesn’t Make Perfect
Miller, C. S., & Settle, A. (2011) · ACM Transactions on Computing Education · doi:10.1145/2048931.2048933 · Cited by 8 · see citing works
Self-citationSurvey of commercial off-the-shelf games, benefits and barriers in formal educational settings
Ritzhaupt, A. D., & Gunter, E. (2010) · Cited by 8 · see citing works
- Authors
- Miller, C. S., Lehman, J. F., & Koedinger, K. R.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Miller, Craig S.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 1999-07
- Volume
- 23
- Issue
- 3
- Pages
- 305-336
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2058522826
- Cited by
- 160
- References
- 37
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 1 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 4 · 2018: 7 · 2017: 7 · 2016: 6 · 2015: 7 · 2014: 7 · 2013: 9 · 2012: 7
- FWCI
- 5.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Science (publishedVersion)
- Funders
- National Science Foundation (9022511)
- Carnegie Mellon University
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Education › Science Education and Pedagogy
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- qualitative physics, physics learning, hands-on learning, goal-directed tasks
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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