Enhancing Chemistry Education Through Professional Development of Secondary School Chemistry Teachers Using Open Educational Resources
Bibliographic record
Abstract
Chemistry, a core element of STEM education. It helps understand matter and its interactions while bridging sciences and fostering critical thinking. It also offers careers in medicine, biotechnology, and sustainability, driving innovations in health and technology However, its teaching is challenging due to complex concepts, calculations, and laboratory safety concerns. Bhutanese students often struggle to comprehend chemistry and its practical application, relying heavily on rote memorisation. Research suggests that professional development can improve teaching and learning outcomes. This study assessed the impact of Open Educational Resources (OERs) on the professional development of Bhutanese secondary chemistry teachers, aiming to enhance their content knowledge, pedagogical skills, and inclusive practices. A mixed methods approach was used to evaluate the effectiveness of the OERs, using pre-test and post-test, lesson plans, reflections, classroom observations, and interviews. Additionally, a Community of Practice (CoP) network on Telegram was analysed to understand the dynamics of knowledge-sharing and learning within the community. The results indicated that the OERs and the CoP network supported the enhancement of teachers' knowledge, pedagogical skills, and inclusive practices. This suggests that educational institutions should leverage OERs and CoP platforms to promote equitable, high-quality professional development for teachers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".