Evaluation of a personalized game-based learning app for developing young children’s reading skills
Bibliographic record
Abstract
Abstract This study examines whether [Reading App], an adaptive, personalized game-based program designed to help young learners build a foundation for reading comprehension and literacy, can improve early reading skills for pre-kindergarteners and kindergarteners (N = 402 treatment, 690 comparison). This study measured the feasibility of implementing [Reading App] in classrooms, teacher and student experiences of using the application, and growth in literacy outcomes between students in classrooms using [Reading App] and students in classrooms not using the application, controlling for baseline performance. Results of hierarchical linear models showed that: (a) kindergarteners who used [Reading App] made significantly greater gains on end-of-year literacy assessment than the comparison group, especially in alphabet knowledge and (b) pre-kindergarteners who mastered at least 16 alphabet skills in [Reading App] experienced greater gains in the skill than comparison group peers. Teacher surveys and interviews suggested that [Reading App] is an easy-to-use, effective, engaging learning resource that empowers them to provide personalized instruction and foster a more equitable classroom environment. The study provides initial evidence of [Reading App]’s effectiveness as a program that can enhance educators’ capacity to address learner variability and provide personalized instruction for all learners.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".