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Misriandi Misriandi
Program Studi Pendidikan Dasar, Universitas Muhammadiyah Jakarta

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Artificial Intelligence Modules and Teacher Creativity in Student Engagement: Modul Artificial Intelligence dan Kreativitas Guru dalam Keterlibatan Siswa Septian Dwi Anto; Misriandi Misriandi; Sri Imawati
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.13190

Abstract

General Background: The rapid development of Artificial Intelligence (AI) has introduced new directions in elementary education, particularly in supporting deep learning through adaptive teaching modules. Specific Background: In practice, the use of AI in instructional design remains closely linked to teachers’ creativity in managing learning activities. Knowledge Gap: Existing studies often examine AI-based modules or teacher creativity separately, leaving limited understanding of their combined role in shaping student engagement in elementary schools. Aims: This study aims to examine the role of AI utilization in developing deep learning teaching modules together with teacher creativity in managing learning toward student engagement in South Bekasi elementary schools. Results: Using a quantitative causal associative design with multiple linear regression analysis, the findings indicate that teacher creativity shows a significant relationship with student engagement, while AI utilization alone does not demonstrate a statistically significant relationship. Simultaneously, both variables account for a substantial proportion of the variation in student engagement. Novelty: This study highlights the combined role of AI-based deep learning modules and teacher creativity as an integrated pedagogical construct rather than isolated factors. Implications: The findings suggest that AI-supported instructional planning requires creative pedagogical management by teachers to foster meaningful student engagement in elementary learning contexts. Highlights • Teacher creativity shows a significant statistical relationship with student engagement• AI-based deep learning modules alone do not demonstrate a significant relationship• Combined variables explain a large proportion of engagement variation in classrooms Keywords Artificial Intelligence; Deep Learning Modules; Teacher Creativity; Student Engagement; Elementary Education
Deep Learning Based Coding And Artificial Intelligence For Computational Thinking In Elementary Schools: Pembelajaran Koding Dan Kecerdasan Artifisial Berbasis Pembelajaran Mendalam Untuk Berpikir Komputasional Siswa Sekolah Dasar Ainul Mardhiyah; Muhamad Sofian Hadi; Misriandi Misriandi
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.13221

Abstract

General Background: The rapid advancement of digital technology requires elementary education to cultivate higher-order thinking and digital literacy as foundational competencies for the twenty-first century. Specific Background: Coding and artificial intelligence learning have been introduced in elementary schools as concrete approaches to support computational thinking through meaningful and contextual learning experiences. Knowledge Gap: Previous studies in Indonesia largely emphasize technical implementation or teacher training, while limited attention has been given to pedagogical strategies grounded in deep learning approaches for fostering computational thinking at the elementary level. Aims: This study explores deep learning–based coding and artificial intelligence learning strategies for developing computational thinking among elementary school students in North Serpong District. Results: Using a descriptive qualitative approach involving teachers and students from two elementary schools, the findings indicate that learning strategies emphasizing active participation, problem solving, reflection, and contextual activities support students’ ability to decompose problems, recognize patterns, construct abstractions, and formulate algorithmic steps. Variations in computational thinking development were observed across schools, reflecting differences in learning implementation quality. Novelty: This study confirms that deep learning–based coding and artificial intelligence learning demonstrates differentiated reinforcement across computational thinking dimensions according to contextual implementation in elementary classrooms. Implications: The findings position deep learning–based coding and artificial intelligence as a strategic pedagogical alternative for strengthening computational thinking and digital literacy within elementary education. Highlights • Computational Thinking Developed Through Contextual Coding Activities• Deep Learning Approach Supports Reflective And Meaningful Learning• Differentiated Implementation Shapes Student Learning Outcomes Keywords Coding Learning; Artificial Intelligence; Deep Learning; Computational Thinking; Elementary School
Integrated Collaborative Learning Storybooks and Animation Improve Explanatory Writing: Integrasi Pembelajaran Kolaboratif Buku Cerita Dan Animasi Meningkatkan Menulis Eksplanasi Nunik Istanti; Adiyati Fathu Roshonah; Misriandi Misriandi
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.13623

Abstract

General Background: Explanatory text writing is a fundamental literacy competence in elementary education requiring logical organization, causal reasoning, and structured academic language. Specific Background: Prior studies report that students experience difficulties in understanding explanatory text structure and cause–effect relationships, often due to limited interactive pedagogy and insufficiently engaging learning media. Knowledge Gap: Although collaborative learning, storybooks, and animation media have been examined separately, limited empirical research integrates these three approaches simultaneously within explanatory writing instruction at the elementary level. Aims: This study aims to examine the partial, simultaneous, and interaction effects of collaborative learning, storybooks, and animation media on sixth-grade students’ explanatory writing skills. Results: Using a quasi-experimental pretest–posttest control group design involving 120 students across four public elementary schools, findings revealed significant effects of collaborative learning (F=78.239; p<0.05), storybooks (F=58.153; p<0.05), and animation media (F=96.504; p<0.05). Simultaneous regression analysis indicated a significant combined model (F=287.779; p<0.05), while factorial ANCOVA demonstrated a strong interaction effect (F=352.605; p<0.05; Partial Eta Squared=0.597). Novelty: This research provides empirical evidence of a collaborative–multimodal instructional model integrating social interaction, narrative scaffolding, and visual animation in explanatory text writing. Implications: The findings support the implementation of integrated collaborative learning supported by storybooks and animation media to foster structured reasoning, text organization, and sustainable written literacy development in elementary classrooms. Highlights Collaborative group work produced substantially higher posttest scores than conventional instruction. Narrative-based materials provided the strongest predictive contribution in the regression model. Multimodal integration generated a large interaction effect size in factorial ANCOVA analysis. KeywordsCollaborative Learning; Storybooks; Animation Media; Explanatory Writing; Elementary Education