An investigation of the mathematics applications in the Apple App Store: Do they contain benchmarks of educational quality?
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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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 | 0.011 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".