Why this app: How user ratings and app store rankings impact parents’ choice of educational apps
Bibliographic record
Abstract
Parents should look for benchmarks of educational quality (curriculum, feedback, scaffolding, learning theory, and development team) to distinguish good apps from the abundance of poor-quality apps available in mobile app stores. If parents instead base their choices on user ratings or the app's ranking in the top charts of the education category, they risk selecting apps that do not offer quality educational experiences for their children. Thus, the present study investigates how ratings, rankings, and educational benchmarks impact parents' choices of educational apps. One-hundred and forty-nine parents of children in kindergarten to grade 6 viewed and evaluated 18 researcher-created educational math app pages. Results from a repeated-measures MANOVA and non-parametric tests revealed that parents were more likely to download, pay more for, and rate apps higher when they had positive user ratings, with a large effect, and parents generally preferred apps with bottom rankings, with a medium effect. Yet, the effect of educational benchmarks on parents' decisions was unclear. This study demonstrates an important problem in parents' app selection: when user ratings are available in app stores, parents rely heavily on this poor source of evidence of educational quality to choose apps for their kids. To address this, researchers should develop trainings and guidelines to help parents evaluate educational quality, and app stores should improve their rating and ranking systems to facilitate the selection of high-quality educational apps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".