Impact Evaluation of Connecting Classrooms Programme. Nigeria
Bibliographic record
Abstract
This report presents the findings in Nigeria from the impact evaluation of the Connecting Classrooms programme, the British Council's international education programme. The programme aims to build the capacity of teachers and school leaders to integrate a range of core skills, including critical thinking and creativity, into the curriculum. This report was part of a 20-month evaluation carried out between October 2016 and May 2018 that included the UK, Ethiopia, Bangladesh and Lebanon, commissioned by British Council and conducted through a partnership between the consultancy Ecorys and the Robert Owen Centre at Glasgow. The first round of fieldwork in Nigeria took place in 2017 and comprised a counterfactual analysis of five Connecting Classrooms schools and five comparison schools. The second round of fieldwork occurred in the first quarter of 2018. Follow-up visits took place at Connecting Classrooms schools only, in and around Lagos, and focused on assessing retention of knowledge, how much further core skills had become embedded in the curriculum, and the sustainability of changes in teaching practices. Each visit comprised classroom observations of trained teachers, student focus groups, student assessments, a focus group with trained teachers and an interview with a school leader. Another element of the second round of fieldwork was a focus on the impact of the programme on policy and education stakeholders with additional interviews in the capital Abuja.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".