<b>Effectiveness of PowerPoint Media in Improving Letter Recognition Skills among Indonesian Preschoolers </b>
Bibliographic record
Abstract
Purpose – This study investigates whether PowerPoint-based media can improve early literacy skills, focusing on letter recognition among preschool children. Traditional methods often fail to engage young learners effectively, and this research aims to explore the potential of multimedia tools to enhance literacy outcomes.Design/methods/approach – A quantitative, pre-experimental one-group pre-test-post-test design was used, involving 18 children aged 5–6 years from TK Tunas Mandiri Sungai Raya, Indonesia. Data were collected through structured observation using a checklist assessing letter identification, sound pronunciation, and letter-image association. Statistical analysis was conducted via paired sample t-tests in SPSS.Findings – Results showed a significant increase in letter recognition scores, from a mean of 4.67 in the pre-test to 8.61 in the post-test. These findings suggest that PowerPoint can be a practical and effective tool to support early literacy learning, engaging children through visual and auditory stimuli.Research implications/limitations – The study’s limitations include the absence of a control group and a small sample size, which limit generalizability. Nonetheless, this research contributes valuable insights into how simple digital tools can enhance literacy education in early childhood settings, particularly where resources are limited
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".