An Evaluation of Math Applications in the App Store: Do they Contain Benchmarks of Educational Quality?
Bibliographic record
Abstract
Well-designed math apps can improve students’ engagement and achievement (Fabian et al, 2016). Previous studies have suggested five educational benchmarks that can be used to identify well-designed math apps (Dubé et al., 2020). However, there is limited research evaluating the educational quality of children’s math apps. To address this problem 33 top math apps in the Apple App store were analyzed. Specifically, a coding scheme was developed and applied for the evaluation of the apps’ in-game content. The coding scheme covered five educational benchmarks (scaffolding, feedback, learning theory, math subjects covered, and content integration). The evaluation of these top math apps showed that, in general, most of the math apps contained more than two educational benchmarks in their game. Although all the apps applied a learning theory and contained feedback, there was a lack of variety in these two benchmarks as the apps tended to primarily use direct instruction and corrective feedback. 93% and 72% of apps included math subjects and scaffolding benchmarks, respectively. Among them, various types of scaffolding and math subjects were prevalent. The least common benchmark was intrinsic content integration, which implied that developers failed at linking the math learning content with the game context. Overall, the majority of math apps contain some amounts of educational benchmarks, but the dominant presence of corrective feedback, direct instruction, and only on- demand scaffolding do not suggest a high-level of educational quality. These preliminary findings highlight the need for improved design of educational math 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 | 0.036 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".