Helping Students Understand Challenging Topics in Science Through Ontology Training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chi (2005) proposed that students experience difficulty in learning about physics concepts such as light, heat, or electric current because they attribute to these con-cepts an inappropriate ontological status of material substances rather than the more veridical status of emergent processes. Conceptual change could thus be facilitated by training students in the appropriate ontology prior to physics instruction. We tested this prediction by developing a computer-based module whereby participants learned about emergent processes. Control participants completed a computer-based task that was uninformative with respect to ontology. Both groups then studied a physics text concerned with electricity, including explanations and a posttest. Verbal explanations and qualitative problem solutions revealed that experimental students gained a deeper understanding of electric current. Students ’ understandings of concepts like force, light, heat, or electricity are well-established and quite distinct from the conventional scientific views offered by instructors. For decades, cognitive and science education research has exam-ined the science knowledge of novices and experts in a widespread effort to iden-tify and characterize preconceptions of various science concepts. Many of the ear-
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it