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All Journal Atfaluna: Journal of Islamic Early Childhood Education
Suharti Suharti
Department of Early Childhood Teacher Education, Universitas Negeri Surabaya, Indonesia
Education

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Integrating Local Wisdom and Scientific Inquiry in Early Childhood Education: A Contextualized Science Learning Approach Suharti Suharti; Hapidin Hapidin; Yuli Pujianti; Edi Suwandi; Muhammad Naufal Fairuzillah
Atfaluna: Journal of Islamic Early Childhood Education Vol. 8 No. 1 (2025): January-June 2025
Publisher : Atfaluna: Journal of Islamic Early Childhood Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32505/atfaluna.v8i1.11349

Abstract

This study explores the integration of local wisdom and scientific inquiry in early childhood education (ECE) on Untung Jawa Island, Administrative City of Kepulauan Seribu, DKI Jakarta. Employing a mixed-method approach, the quantitative phase utilized a one-shot posttest experimental design involving 21 children aged 5-6 years old, consisting of 11 boys and 10 girls. The children participated in culturally contextualized science activities that incorporated local wisdom. In addition to quantitative data collection, qualitative data were gathered through observations and interviews with five ECE teachers to gain deeper insights into the implementation process. The results indicate that integrating local wisdom enhances children's engagement and understanding of scientific concepts. This study recommends further research to explore long-term impacts and broader applications of culturally responsive teaching methods in early childhood settings.
Assessing Divergent and Convergent Creativity in Early Childhood Drawings: A Multi-Task Deep Learning Approach Anik Indarwati; Frangky Tupamahu; Suharti Suharti; Firsta Hannni Enggaring Galih; Satya Raj Joshi
Atfaluna: Journal of Islamic Early Childhood Education Vol. 8 No. 2 (2025): July-December 2025
Publisher : Atfaluna: Journal of Islamic Early Childhood Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32505/atfaluna.v8i2.12434

Abstract

This study aims to develop and evaluate a deep learning model for the comprehensive assessment of divergent and convergent creativity dimensions. The dataset comprised 102 digital drawings obtained from Indonesian children aged 4 to 6 years using the Test for Creative Thinking - Drawing Production (TCT-DP). This study employed a quantitative model development approach, where ground-truth labels were derived from the 14 TCT-DP scoring criteria aggregated into divergent and convergent scores through label engineering. Using a Multi-Task Convolutional Neural Network (MT-CNN) based on MobileNetV2 architecture, the study analyzed extracted visual features to predict expert-rated scores. The results revealed a strong positive correlation (r = +0.51) between divergent and convergent thinking scores, challenging the traditional view of these processes as antagonistic and supporting an integrated model of creative cognition. From a technical perspective, the model demonstrated satisfactory predictive capability as a proof-of-concept, achieving a lower error rate for convergent scores (RMSE = 1.52) compared to divergent scores (RMSE = 1.97). It indicates that while structured convergent features are more machine-learnable, the abstract nature of divergent thinking remains a complex challenge. In conclusion, this study validates the feasibility of automated creativity assessment while offering empirical evidence for the interplay between generative and evaluative thinking in early childhood.
Co-Authors Anik Indarwati Edi Suwandi Firsta Hannni Enggaring Galih Frangky Tupamahu Hapidin Hapidin Muhammad Naufal Fairuzillah Satya Raj Joshi Yuli Pujianti