Piloting an innovation for teachers’ capacity development in STEM subjects in Nigerian secondary schools
Bibliographic record
Abstract
Connecting Learning for secondary school Teachers Capacity Development in STEM (CL4STEM) is a project that aims to pilot innovation and research its effectiveness and potential scaling for building capacity of teachers in secondary school in science and mathematics to foster higher-order teaching with inclusion and equity (HOTIE). It is a South-South collaboration among higher education institutions to adapt and pilot the Connected Learning Initiative (CLix) (http://clix.tiss.edu), which was developed and scaled in India, to new contexts in Bhutan, Nigeria and Tanzania with support from the IDRC. CL4STEM engaged in teacher professional development for newly recruited teachers in Nigeria by implanting and using highly localized and contextualized open education resources (OER), strengthening the use of technology and local resources in teaching enhancing contents knowledge specifically in the sciences and mathematics Data were collected in three phases, baseline, midline, and endline. The findings indicate that teachers' understanding of CL4STEM innovation improved from baseline to endline. At the baseline 2 teachers were still learning how to effectively navigate CL4STEM modules and Telegram group (CoPs) while none was at the endline. There is an increase in the number of teachers exploring ways of improving CL4STEM teaching strategies through further refinement of the modules and CoP participation and/or alternative ways of achieving better results from 1 at midline to 5 at endline. There is a decrease in the number of teachers that are exploring ways of collaboration with other teachers and educators to help impact student learning using CL4STEM teaching strategies from 11 at the midline to 3 at the endline.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".