An investigation of the mathematics applications in the Apple App Store: Do they contain benchmarks of educational quality?
Why this work is in the frame
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Bibliographic record
Abstract
Given the numerous mathematics applications marketed in the Apple App Store and the lack of quality control, it is critical to determine whether these digital learning tools are well-designed and if they are accurately marketed by developers. The present study evaluated the top math apps (n = 33) priced under $15, categorized into three age groups (i.e. <5, 6-8, and 9-11) in the App Store. It examined how well they incorporate five educational features or benchmarks in their apps, namely- scaffolding, feedback, learning theory, math subjects, and content integration (i.e. the connection between game and learning content). Furthermore, it assessed whether developers mentioned these benchmarks in their store descriptions and if the descriptions accurately reflected the app’s content. Most apps included more than three benchmarks. All apps contained feedback and learning theory and most provided some forms of scaffolding. The types and amount of math subjects, feedback, and scaffolding varied significantly across apps. Interestingly, these top apps contained more benchmarks and content than developers advertise in the App Store. The findings emphasize the importance of developers incorporating benchmarks into their apps and accurately communicating this to the public to help them navigate the sea of available apps.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it