A meta-analysis of the impact of the implementation of thinking skills approaches on pupils.
Bibliographic record
Abstract
s and titles screened N = 127 Papers excluded N = 96 Potential includes N = 31 Papers not obtained N = 8 Full document screened N = 23 Meta-analysis Studies included N = 3 Papers excluded N = 20 Full document screened N = 191 Meta-analysis Studies included N = 29 A meta-analysis of the impact of the implementation of thinking skills approaches on pupils 19 3. Identifying and describing studies: results 3.2 Characteristics of the included studies 3.2.1 Country of included studies The studies in the meta-analysis come from a range of countries, with half from the US and UK and a range of other countries represented. Table 3.1: Countries of studies in the in-depth review (N = 29) Country Number of studies 5 Australia 3 Canada 2 Hungary 1 Israel 2 The Netherlands 1 Nigeria 1 Pakistan 1 The Philippines 1 Taiwan 1 South Africa 1 UK 7 5 USA 8 Total (mutually exclusive) 29 3.2.2 Educational setting The studies in the meta-analysis represent a range of educational settings. Table 3.2: Educational setting of studies in the in-depth review (N = 29) Educational setting Number of studies Primary school 9 Secondary school 20 6 Special needs school 2 Total (not mutually exclusive) 31 3.2.3 Curriculum focus of the studies in the in-depth review The majority of the studies in the meta-analysis focus on science, mathematics and aspects of literacy (mainly reading comprehension). 5 Inference for one paper from affiliation of the author of the study. 6 Both Special Needs schools were also secondary schools A meta-analysis of the impact of the implementation of thinking skills approaches on pupils 20 3. Identifying and describing studies: results Table 3.3: Curriculum focus of the in-depth studies (N = 29) Curriculum focus Number of studies Citizenship 1 General/Cross-curricular/Other 7 ICT 1 Literacy – first languages 7 Mathematics 9 Science 9 7 Total (not mutually exclusive) 34
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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 | 0.030 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.073 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".