Evaluation of Connecting Classrooms in Nigeria, Ethiopia, Bangladesh and Lebanon. Wave 2
Bibliographic record
Abstract
This report presents the overseas findings 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 (critical thinking and problem-solving, collaboration and communication, creativity and imagination, digital literacy, global citizenship and student leadership) into the curriculum. This report was part of a broader 20-month evaluation carried out between 2016-2018 that also included the UK. The evaluation was commissioned by The British Council principal office in London and conducted through a partnership between the research consultancy Ecorys and the Robert Owen Centre at the University of Glasgow. Findings in this overseas report are based on fieldwork in Nigeria, Lebanon, Bangladesh and Ethiopia. The first round of fieldwork took place in 2017 and comprised a counterfactual analysis of five Connecting Classrooms (CC) schools and five comparison schools per country. The second round of fieldwork occurred in the first quarter of 2018. These follow-up visits took place at CC schools only, 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. An additional element of the follow-up visits was the impact of the programme on policy and education stakeholders.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".