Transforming Pedagogical Practices: The Impact of a Connected Learning for STEM Intervention on Science and Mathematics Teachers in Northern Nigeria
Bibliographic record
Abstract
The purpose of this study is to explore the impact of the Connected Learning for Capacity Building initiative on the pedagogical practices of secondary school science and mathematics teachers in Northern Nigeria. It specifically examines how participation in the programme impacted teachers’ understanding and implementation of equitable, inclusive, and learner-centered instructional strategies. The study adopted a qualitative descriptive research design. Data were collected through semi-structured interviews with a purposive sample of 20 novice teachers across three phases, namely baseline, midline, and endline. The data were analyzed thematically to trace the evolution of teachers’ pedagogical conceptions and practices, particularly in relation to equity and inclusion. The findings indicate a substantial transformation in teachers’ pedagogical orientations and classroom practices. Participants demonstrated an expanded understanding of equity and inclusion, moving beyond academic ability to consider gender, socioeconomic background, and geographical disparities. Furthermore, teachers increasingly applied principles of Universal Design for Learning (UDL) by integrating diverse instructional strategies such as the use of local materials, multimedia resources, and collaborative learning activities to accommodate varied learner needs and contexts. This study provides empirical insights into how a connected learning framework that incorporates Open Educational Resources (OER) and a mobile-based Community of Practice (CoP) can foster pedagogical innovation in resource-limited settings. It highlights the potential of connected learning as a sustainable model for teacher professional development and as a means to promote equitable quality education in alignment with Sustainable Development Goal 4.
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How this classification was reachedexpand
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".