How do Canadian parents evaluate numeracy content in math apps for young children?
Bibliographic record
Abstract
Well-designed math apps can foster children’s numeracy development. Although parents are interested in using math apps for their children, the quantity and variability in quality of apps can make app selection challenging. As such, it is important to know how parents evaluate numeracy content in math apps for their children. This study investigated parents’ evaluations of four numeracy apps of varying quality. Forty-five parents of 3- to 6-year-old children explored each app for three minutes on their own device and rated each on 17 early numeracy skills. Parents were conservative in their ratings of numeracy skills. Likelihood of downloading apps ranged from 24.4% to 73%. If the first app viewed was of the highest quality, it influenced their subsequent ratings suggesting that parents could be using it as a benchmark. Individual differences affected numeracy ratings, such that parents high in both math teaching confidence and/or anxiety rated the apps more favourably than parents low in those traits. Parents experienced some challenges in identifying better content and design features and individual differences contributed to this. Outcomes also suggest practical supports such as providing parents with a “good” referent may aid app selection.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".