L’engagement des élèves en classe dans un programme «un portable, un élève» au secondaire: après un quart de siècle d’implantation
Bibliographic record
Abstract
This research examines the influence of “1:1 technology” programs on secondary school students’ engagement after over 25 years of implementation. These programs were initially seen as an innovative response to improve students’ academic success (Holcomb, 2009). While these initiatives were initially viewed as innovative, they are increasingly seen as a standard part of the educational landscape. “1:1 technology” programs are often linked to benefits in behavioural, emotional, and cognitive engagement, as well as social and agentic engagement, since digital tools facilitate better interaction and collaboration (Fredricks et al., 2016; Joshi et al., 2022). However, after a quarter-century of deployment, few studies have examined their impact (Norris et al., 2012). As part of a partnership established between a secondary education program team and a faculty of education at a university, students from an enriched program at a public school in Quebec answered a questionnaire containing open-ended questions about the factors of engagement or disengagement. Using a qualitative descriptive approach and an inductive general analysis of the responses to this questionnaire, the following question is addressed: After 25 years of implementation, what is the influence of “1:1 technology” programs on student engagement in secondary education?
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".