Choosing and disusing educational technology: Examining parents’ decision making about math and literacy apps for their young children
Bibliographic record
Abstract
Many parents are interested in using educational apps for their young children. Evidence indicates that well designed apps can promote children’s literacy and math skills. However, many commercially available apps are poorly designed. This highlights the importance of understanding how parents decide which educational apps they make available for their child and also why they may disuse them. Sixty-five Canadian parents (58 mothers) completed a survey assessing literacy and math knowledge, and decisions about literacy and math apps. Parents’ naturally self-generated features for app selection yielded similarities e.g., (ease of use, age appropriateness) and differences (e.g., advertisements, games) to rubrics typically generated by researchers. Highly endorsed features were similar across app types. App quality and potential for independent use were key reasons for disuse. Parental knowledge of foundational literacy and math concepts such as phonological awareness and cardinality was low, which could pose a challenge for their assessments of apps.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".