Using schema training to facilitate students' understanding of challenging engineering concepts in heat transfer and thermodynamics
Bibliographic record
Abstract
Abstract Background Chi and colleagues have argued that some of the most challenging engineering concepts exhibit properties of emergent systems. However, students often lack a mental framework, or schema, for understanding emergence. Slotta and Chi posited that helping students develop a schema for emergent systems, referred to as schema training, would increase the understanding of challenging concepts exhibiting emergent properties. Purpose We tested the effectiveness of schema training and explored the nature of challenging concepts from thermodynamics and heat transfer. We investigated if schema training could (a) repair misconceptions in advanced engineering students and (b) prevent them in beginning engineering students. Method We adapted Slotta and Chi's schema training modules and tested their impact in two studies that employed an experimental design. Items from the Thermal and Transport Concept Inventory and expert‐developed multiple‐choice questions were used to evaluate conceptual understanding of the participants. The language used by students in their open‐ended explanations of multiple‐choice questions was also coded. Results In both studies, students in the experimental groups showed larger gains in their understanding of some concepts—specifically in dye diffusion and microfluidics in Study One, and in the final test for thermodynamics in Study Two. But in neither study did students exhibit any gain in conceptual questions about heat transfer. Conclusion Our studies suggest the importance of examining the nature of the phenomena underlying the concepts being taught because the language used in instruction has implications for how students understand them. Therefore, we suggest that instructors reflect on their own understanding of the concepts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".