Science on the Move: How Mobile Pedagogy Shapes Human Capital
Bibliographic record
Abstract
While many developing economies have made progress in providing access to education, the provision of quality education that delivers life-long learning, learning-howto-learn, and developing the ability to apply knowledge to unfamiliar circumstances is essentially absent.In collaboration with the Agastya Foundation, we conducted a randomized controlled trial in public schools in Uttar Pradesh (India) to evaluate an intervention that provides -discovery-based pedagogy in science topics -in 68 "treatment" schools, which are then compared to 64 "control" schools.We find that treated students show remarkable improvement relative to control students: intrinsic factors (curiosity, self-confidence, aspirations, self-efficacy) improved in the range of 0.12 -0.18 sd, and simultaneously, the perception of barriers reduced by 0.22 sd.Student engagement in science increased in the range of 0.17 -0.20 sd, and their general engagement in school increased by 0.22.Finally, we find that test scores improved by 0.22-0.31sd.Our results highlight the importance of adopting child-centric pedagogical practices as an important tool to improve educational quality.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".