Nurhidayat Nurhidayat
universitas Muhammadiyah Kolaka Utara

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Deep Learning-Based Daily Activity Storybook for Early Childhood Literacy Skills Nur Alim Amri; Nurhidayat Nurhidayat; Abdul Muhsin; Abdul Hakim; Farida Febrianti
Ihya Ulum: Early Childhood Education Journal Vol 4 No 2 (2026): ihya Ulum: Early Childhood Education Journal
Publisher : PG PAUD Universitas Islam Makassar | LP2M-UIM | PPJ PAUD (Perhimpunan Pengelola Jurnal PAUD) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59638/ihyaulum.v4i2.817

Abstract

This study aims to examine the effectiveness of a deep learning–based daily activity storybook in enhancing early literacy skills among children aged 5–6 years. Employing a quantitative approach with a pre-experimental one-group pretest–posttest design, the research involved 22 children selected through purposive sampling. Data were collected using a validated and reliable literacy observation sheet (Aiken’s V = 0.84–0.95; a = 0.91; ICC = 0.84) measuring children’s ability to comprehend symbols, use vocabulary, and express ideas meaningfully. Statistical analysis using the Paired Sample t-test revealed a significant difference between pretest (M = 52.14, SD = 4.38) and posttest (M = 74.27, SD = 5.16) scores, t (21) = 13.42, p < 0.001, with a very large effect size (Cohen’s d = 2.86). The findings demonstrate that integrating deep learning principles into contextual storybooks substantially improves children’s functional and expressive literacy. The study’s novelty lies in combining literacy, communication, and experiential learning through a child-centered storytelling model. Practically, it offers teachers an innovative pedagogical tool aligned with the Merdeka Curriculum, fostering joyful and meaningful learning. This research contributes to strengthening the theoretical and practical foundation of deep learning–based literacy education in early childhood contexts.
A Theory-Informed Framework for Interactive Digital Learning in Physics Education: A Systematic Literature Review Dewi Hikmah Marisda; Ana Dhiqfaini Sultan; Muhammad Wajdi; Ramli Rasyid; Asrinan Asrinan; Nurhidayat Nurhidayat
Jurnal Pendidikan Terapan Vol 4, No 4 September (2026)
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/jupiter.v4i4.1675

Abstract

Purpose: This study synthesizes evidence on interactive digital learning in physics education and addresses fragmented technology-specific reviews by proposing a theory-informed conceptual framework linking digital affordances, theoretical foundations, pedagogical design, learner processes and outcomes, and implementation conditions. Methods: Following PRISMA 2020 and PRISMA-S, literature published from January 2016 to June 2026 was retrieved from nine information sources and supplemented by backward and forward citation chasing. of 3,142 records identified, 66 publications met the eligibility criteria. Empirical studies were appraised using MMAT, with the JBI checklist additionally applied to quasi-experimental studies. Evidence was synthesized through thematic and abductive analysis. Findings: Five interconnected themes emerged: digital learning affordances, theoretical foundations, pedagogical design features, learner processes and outcomes, and implementation conditions. Learning benefits were associated with representational coherence, guided inquiry, scaffolding, feedback, reflection, and context-sensitive implementation. The synthesis does not establish causal mediation among framework components. Conclusion: The review proposes a theory-informed conceptual framework organizing recurring relationships across heterogeneous studies. Research Implications: The framework supports the design, implementation, evaluation, and empirical testing of coherent digital physics learning environments. Originality: The review integrates technological, theoretical, pedagogical, learner, outcome, and contextual dimensions across multiple digital modalities within a unified conceptual structure for future research.