Socioemotional Learning in Early Childhood Education: Experimental Evidence from the Think Equal Program’s Implementation in Colombia
Bibliographic record
Abstract
In this article we experimentally evaluate Colombia’s Think Equal program, which teaches socioemotional skills to children ages 3 to 6. Given the context of COVID-19, the original design was adapted as a hybrid model, alternating in-person and remote instruction and engaging families in the implementation of the curriculum. We found that the program had positive effects on children’s prosocial behavior, self-awareness, and cognitive learning. The intervention also had an impact on education centers personnel (community mothers) and caregivers implementing the activities. Treated community mothers had higher levels of empathy, lower negative health symptoms, better pedagogical practices, and a closer relationship with the children’s caregivers compared with those in the control group. Treated caregivers had better stimulation practices and lower negative health symptoms compared with those in the control group. These findings suggest that a well-designed intervention has the potential to develop socioemotional skills in children at an early age and, at the same time, to develop capacities in those who implement the activities. Our results have important implications for the design, implementation, and evaluation of early childhood socioemotional learning programs and provide novel evidence about the challenges faced by interventions combining face-to-face and remote learning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".