Developing and Evaluating an Educational Game to Support Early Reading Skills in Kindergarten Students
Bibliographic record
Abstract
Reading is a crucial skill that should be learned from an early age, as it plays a vital role in daily life. While most children can easily learn to read, some struggle with the process. Therefore, effective learning media are needed to support children's reading development. This study develops and evaluates an educational game to assist young children in learning to read. Unlike previous studies, the game integrates multiple interactive modes that reinforce word recognition, spelling, and sentence construction through auditory and visual cues. The game features a simple user interface, a collection of 100 open vocabulary words, and three gameplay modes designed to support different aspects of early reading skills. The game's effectiveness was assessed through an experiment involving 12 kindergarten students, divided into a game-playing and a non-game-playing group. The findings indicate that the educational game can enhance kindergarten students' reading skills. Moreover, the game increases students' enthusiasm and motivation for learning to read. However, further improvements, such as additional features and platform expansion, are necessary to make the game more accessible to a broader audience.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".