Early childhood learning analytics: A case study of Learning Jungle
Bibliographic record
Abstract
The benefits of participating in high-quality Early Childhood Education (ECE) have been recognized by people for many years; and the need for high-quality ECE has never been greater. In this case study, we focus on whether ECE can improve learning speed in five domains: social, emotional, communication, cognition, and physical development. The initial ages for each of these five domains, in months since birth, are collected and compared with that of common children as described in Nipissing District Developmental Screen (NDDS). We find that children in the ECE program learned faster with a p-value no >0.0078. In addition, students in an ECE program are labeled by their ages at enrollment as Cohort 1 (infant) and Cohort 2 (toddler), and we conduct the following statistical tests on their difference: Welch's t-test, Hoteling's T2-test, and survival analysis. We find that the average initial observation age of Cohort 1 is 4.82 months earlier than that of Cohort 2 with a p-value no >0.009. We are convinced that ECE programs could advance students' learning in all five domains.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".