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Record W4409887110 · doi:10.31004/obsesi.v9i4.6925

Developing and Evaluating an Educational Game to Support Early Reading Skills in Kindergarten Students

2025· article· en· W4409887110 on OpenAlexaff
Hans Juwiantho, Liliana Liliana, Lily Eka Sari

Bibliographic record

VenueJurnal Obsesi Jurnal Pendidikan Anak Usia Dini · 2025
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsEducational gameReading (process)Mathematics educationPsychologyGame based learningMedical educationComputer sciencePedagogyMedicinePolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.054
GPT teacher head0.440
Teacher spread0.386 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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