Impact of a Neuro-Cognitive Intervention on Students’ Cognitive Functions: Assessments in a Government School Setting in South India
Bibliographic record
Abstract
Evidence indicates that cognitive deficits can affect students’ performance in schools. An effort to enhance cognitive skills through integration of a neuro-cognitive training called ‘Brighter Minds’, was made in the government schools of South India during 2017-18. 110 students went through a pre and post training evaluation, using a mixed methods study design to assess changes in attention, observation and memory. Tools such as Make a Trail and Stroop test to assess attention, Word Recall and Visual stimulus tests for memory and acustomized tool to assess changes in observation were used. Focus group discussions were conducted to understand teachers’ experience and acceptance of the training. Paired t-tests demonstrated statistically significant gains in all the three cognitive traits. The average time taken to complete the trail tests significantly reduced after the training (Trail A: 59.3 s to 47.5 s; Trail B: 156 s to 120 s. p < 0.001). The average number of correct items read out in the Stroop test improved significantly. More students were able to observe deeper aspects of the test object (23 Vs 40, p < 0.001). On intuition tests, average score of correct observations went up significantly from 6.7 to 9.6 objects. Memory assessments revealed significant improvements in verbal recall (both immediate and delayed) and visual recognition, but marginal improvements in visual retention. The teachers reported changes in student’s motivation, discipline and participation. The training was seamlessly rolled out by the teachers during regular hours and led to improvements in students' cognitive abilities. The cognitive trainings offer promise to complement current efforts in enhance learning outcomes among students.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".