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Record W4406681873 · doi:10.17102/eip.10.2025.03

The Impact of the OER Module ‘Force and Motion’ on Physics Teachers’ Knowledge and Practices

2025· article· en· W4406681873 on OpenAlexfundno aff
Karma Utha, Ugyen Pem

Bibliographic record

VenueEducational innovation and practice · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsMotion (physics)PhysicsMathematics educationComputer scienceClassical mechanicsPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.527
Teacher spread0.424 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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