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Hamdan Husein Batubara
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Gedung FITK Lt.3 Kampus 2 UIN Walisongo Semarang. Jl. Prof. Dr. Hamka, Tambakaji, Kec. Ngaliyan, Kota Semarang, Jawa Tengah, Indonesia, Kode Pos 50185
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INDONESIA
Journal of Integrated Elementary Education
Published by Universitas Islam Negeri Walisongo Semarang
ISSN : 2828223X     EISSN : 27761657     DOI : https://doi.org/10.21580/jieed.v1i1
Core Subject : Education, Social,
Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other
Journal of Integrated Elementary Education is a multi-disciplinary, peer-reviewed and open-access journal published online by the Faculty of Tarbiyah and Teacher Training, Universitas Islam Negeri Walisongo Semarang, Indonesia in two issues a year (March and September). This journal invites researchers and academics to publish original and actual research results about teaching and learning at elementary school, such as: development of attitudes, knowledge and skills of students, teaching and learning models, technology utilization, learning evaluation, and teacher competencies development.
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 162 Documents
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Supporting elementary teachers’ pedagogical decision-making through machine learning–based behavioral diagnostics Taqwa Nur Ibad; Istiningsih Istiningsih; Sumarsono Sumarsono; Alfiatus Safaah; Hartono Hartono; Ihya’ Ulumuddin
Journal of Integrated Elementary Education Vol. 6 No. 1 (2026)
Publisher : Universitas Islam Negeri Walisongo Semarang in collaboration with PD PGMI Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jieed.v6i1.31141

Abstract

This study investigates how elementary school teachers interpret and use a machine–learning–based behavioral diagnostic system to support pedagogical decision-making regarding students’ non-cognitive competencies. Employing a descriptive-analytical approach olxtogel grounded in the principles of human-centered artificial intelligence, data were gathered through questionnaires, semi-structured interviews, and document analysis. The findings indicate that teachers were able to clearly interpret the diagnostic outputs and deemed them relevant to their classroom practices. Additionally, there were high levels of perceived usefulness in identifying students' learning needs (M = 4.25) and supporting instructional planning (M = 4.22). Over 80% of teachers reported using the system to differentiate learning activities and adjust instructional strategies. Moreover, the diagnostic system served primarily as a decision-support tool, assisting teachers in validating their professional intuition, designing more responsive differentiation strategies, and engaging in deeper reflective practice regarding the development of students' non-cognitive skills. Importantly, teachers maintained full professional autonomy by selectively interpreting and contextualizing system recommendations based on their students' knowledge. These findings suggest that machine learning-based behavioral diagnostics can effectively enhance teachers' pedagogical reasoning when designed within a human-centered framework. Rather than replacing teacher judgment, the system improved teachers’ awareness of students’ motivation, engagement, self-regulation, and perseverance, thereby facilitating more informed and responsive instructional decision-making in elementary education.
The Influence of growth mindset and multiple intelligences on elementary students’ self-regulation Nurul Zikri Filina; Tria Marvida; Dian Erlita; Lindayani Lindayani; Putri Fakhrina Sari
Journal of Integrated Elementary Education Vol. 6 No. 1 (2026)
Publisher : Universitas Islam Negeri Walisongo Semarang in collaboration with PD PGMI Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jieed.v6i1.31488

Abstract

This study examines the association between ALEXISTOTO growth mindset, multiple intelligences, and elementary school students’ self-regulation. Self-regulation is a key competency in elementary education, yet it is often treated as a technical learning skill rather than an integrated cognitive and behavioral capacity. Using a quantitative explanatory design, the study involved 239 fifth-grade students from elementary schools in Aceh. Data on growth mindset, multiple intelligences, and self-regulation were collected through structured questionnaires and analyzed using multiple regression analysis. The results show that growth mindset and multiple intelligences together account for a substantial proportion of variance in students’ self-regulation (R² = 0.673, p < 0.001). Growth mindset was the stronger predictor (β = 0.678, p < 0.001), while multiple intelligences also showed a smaller but statistically significant association (β = 0.155, p = 0.024). These findings indicate that students with higher growth mindset scores and more developed multiple intelligences tend to report higher levels of self-regulation. The study provides empirical evidence on the relative contribution of growth mindset and multiple intelligences to self-regulation among elementary students, without implying causal relationships. The findings may inform educational practices aimed at supporting students’ self-regulatory development.

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