The Effects of Context-Rich Problems on Motivation and Learning in Mechanical Physics
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
This study examines the effects of the pedagogical use of context-rich problems on motivation and learning, as compared to traditional problems, in mechanical physics courses at the college level. The results indicate that the treatment has appreciable outcomes on conceptual learning gain, on the perception of task value and on a perceived sense of competence. Moreover, the affected motivational variables exhibit a considerable positive correlation with learning gain. A linear regression analysis shows that the best predictors of learning gain are perceived sense of competence and interest. The former acts as the main learning gain predictor when the theme of the problem is imposed by the teacher, whereas the latter becomes the best predictor when students can choose the theme among different possibilities, situations that seem to be more conducive to learning gain. Therefore, offering a choice of themes, while using context-rich problems, appears to increase the students’ emotional reactions, making this pedagogical device a promising tool for achieving learning gains.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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