“Everything’s technology now”: the role of technology in home- and school-based summer learning activities in Canada
Bibliographic record
Abstract
Though summer learning loss has been widely documented across both the United States and Canada, there is little knowledge on how parents and teachers view the use of technology in the context of summer vacation, and what the role of digital tools are in potentially alleviating achievement gaps due to summer learning loss. Drawing on 71 parent and 37 teacher interviews from a large-scale Canadian study examining summer learning loss in Ontario through summer literacy and numeracy programs for students (grades 1–3), this study highlights the complexities associated with using digital tool in both home and school life in the summer. Through extensions of Bourdieu’s theory of cultural capital, we suggest that digital tools are becoming a new type of valued skillset that parents and educators are acknowledging. In specific, our main findings center around three interrelated themes: i) comfort with technology; ii) home-school connections; and iii) perception of children as digital natives. Results may be fruitful for parents, educators, and policymakers to understand the larger role that digital technology plays amongst Canadian families and teachers during the summer months and school year. Capturing these discussions can maximize both school and home use of digital tools.
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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 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".