Learning with eMates in Etobicoke Final Project Report
Bibliographic record
Abstract
This study examines the assumption that optimal learning occurs in classrooms where every child has access to their own eMate laptop computer. Grades one to four classrooms in six schools of an urban school district were provided with laptop computers in three different student-to-computer ratios (1:1, 2:1, 4:1). Throughout the school year three samples of student writing were taken at equal intervals and classrooms were regularly observed. Writing samples were also collected from control classrooms in the same schools that did not have access to computers. A MANCOVA analysis of holistic ratings of writing samples revealed that by the end of the school year there were significant differences between the four groups in their development of writing proficiency (p<.05), with students in the 2:1 ratio classes showing the greatest average gains, followed closely by the 1:1 and then the 4:1 classes. The control group students demonstrated the least improvement. A logistic regression analysis of the observational data indicated that in classes with eMates, teachers were less likely to be engaging in direct instruction and more likely to using a resource-based, project-oriented pedagogical strategy. They also spent more time managing student activities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".