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All Journal INVOTEC Samakia: Jurnal Ilmu Perikanan Journal of Aquaculture and Fish Health Jurnal Kemaritiman: Indonesian Journal of Maritime Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Jurnal Marshela (Marine and Fisheries Tropical Applied Journal) Darma Diksani: Jurnal Pengabdian Ilmu Pendidikan, Sosial, dan Humaniora
Ahmad Satibi
Universitas Pendidikan Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Social Sciences Transportation Other

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Mapping the Research Landscape of Artificial Intelligence Innovation for Dropout Prevention and Learning Opportunity Loss Mitigation Policy Iik Nurulpaik; Ahmad Satibi; Encep Supriatna; Oki Suprianto
Jurnal Kependidikan : Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran, dan Pembelajaran Vol. 12 No. 3 (2026): September
Publisher : LPPM Universitas Pendidikan Mandalika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v12i3.21069

Abstract

This study aims to map the research landscape of Explainable Artificial Intelligence (XAI) for dropout prevention and learning loss mitigation. The study employed bibliometric science mapping to identify publication trends, conceptual structures, and emerging research directions. A Scopus search using the query TITLE-ABS-KEY("Explainable Artificial Intelligence" OR "Explainable AI") AND TITLE-ABS-KEY(Dropout OR "Learning Loss Mitigation") initially returned 207 records. No restrictions on language, document type, or publication year were applied during the retrieval process. Seven records with missing author metadata were excluded, leaving 200 documents published between 2020 and 2026 for analysis using Biblioshiny and VOSviewer. The results revealed rapid publication growth, with an annual growth rate of 57.04%, peaking in 2025 with 99 publications. Dominant themes included deep learning, explainable AI, learning systems, machine learning, dropout prediction, SHAP, LIME, interpretability, and uncertainty, while three broad conceptual clusters centered on deep learning, explainable artificial intelligence, and contrastive learning. Learning loss mitigation did not form a stable research cluster, indicating that the field has progressed more rapidly in explaining dropout prediction than in establishing connections between XAI and longitudinal learning decline. Theoretically, this study identifies a research agenda that integrates learning loss indicators with explainability, uncertainty, and fairness. Practically, it suggests that education policymakers should prioritize human-supervised early-warning systems whose explanations can be translated into timely and actionable interventions.
Co-Authors Agung Setyo Sasongko Ahmad Beryliumsyah Ikmaludin Ahmad Beryliumsyah Ikmaludin Andre Aprinaldo Andre Aprinaldo Andrian Asuda Ardana Kurniaji Bella Dinda Rahayu Daffa Zhaffir Aqilah Dwi Rifky Darmawan Encep Supriatna Ferdi Ardiansyah Ferry Dwi Cahyadi Fitri Ratnasari Hari Din Nugraha Himawan Prasetiyo Iik Nurulpaik Mad Rudi Mechel Yesua Nada Nur Fadhilah Nur Fidya Destianti Oki Suprianto Tarigan, Daniel Julianto Trisno Kunto Nugroho Willdan Aprizal Arifin