The Impact of the OER Module ‘Force and Motion’ on Physics Teachers’ Knowledge and Practices
Bibliographic record
Abstract
Students' performance in STEM subjects is a growing concern globally, with coinciding factors cited, as ineffective teaching methods, students' lack of interest, and the abstractness of STEM content. This study in particular investigated the impact of an Open Educational Resource module on teachers' understanding of Force and Motion. The goal was to enhance the professional efficacy of Secondary School Science and Mathematics teachers, promoting an inclusive and equitable higher-order learning in their classrooms with the help of OER. A mixed-methods approach was employed, involving 36 teachers from 32 different schools. Data collection included pre-tests and post-tests, evaluations of lesson plans and reflections, assessments of teacher participation in a community of practice and the Moodle platform, and interviews. Findings indicated a modest yet positive shift in teachers' content knowledge, with an increase in the number of participants classified as 'accomplished' in post-test assessments. While teachers demonstrated the improved awareness of their students' needs and engagement strategies, challenges still remain in effectively integrating Universal Design for Learning principles and diverse assessment methods. The study highlights the importance of ongoing professional development and clearer guidelines to support teachers in implementing these practices. Recommendations for future research include longitudinal studies to assess the long-term effects of OER modules on teaching practices and student outcomes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".