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All Journal Jurnal Varian Online Learning in Educational Research
Deshinta Arrova Dewi
INTI International University, Indonesia
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Social Sciences Other

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Natural Disaster Mapping on Java Island Using Biplot Analysis Pressylia Aluisina Putri Widyangga; M. Fariz Fadillah Mardianto; Firda Aulia Pratiwi; Andi Vania Ghalliyah Putrie; Putu Eka Andriani; Dita Amelia; Deshinta Arrova Dewi
Jurnal Varian Vol 7 No 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.2634

Abstract

Indonesia is located in the ring of fire region. This condition causes Indonesia to have the potential to experience various disasters, such as volcano eruptions. In addition, rapid population growth has led to rampant land conversions that cause floods, landslides, tornadoes, droughts, and forest fires. The research aims to map natural disasters in Indonesia, especially Java Island to find out the provinces and their natural disasters tendency using Biplot analysis. Based on the results, Central Java, East Java, and West Java have a tendency to have floods and landslides. East Java tends to undergo earthquakes and Central Java has the potential to experience volcano eruptions. Through the natural disasters mapping, the government, especially the BMKG, will be able to find various solutions to overcome the natural disasters that have great potential to occur in provinces in Indonesia, especially Java Island as the manifestation toward SDGs Target 2030.
Beyond One-Size-Fits-All Learning: An AI-Driven Personalized Learning Pathway Framework Eko Risdianto; Joseline Santos; Noel Lomerio; Rita Sinthia; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Laura Mahendratta Tjahjono; Mona Ardina; Desvi Wahyuni
Online Learning In Educational Research (OLER) Vol. 6 No. 2 (2026): Online Learning in Educational Research
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/oler.v6i2.1101

Abstract

Artificial intelligence has accelerated the evolution of personalized learning, yet most learning management systems continue to rely on static instructional pathways that inadequately accommodate learners’ cognitive and behavioral diversity. This study evaluated an AI-driven Personalized Learning Pathway (PLP) framework that integrates learner analytics, intelligent content recommendation, and adaptive assessment within a unified learning environment to enhance student engagement and academic achievement. A mixed-methods quasi-experimental design was conducted with 240 undergraduate students from four universities across Southeast Asia. Students in the experimental group learned through an AI-augmented learning management system, while the control group used a conventional platform. The findings demonstrate that the AI-driven PLP framework consistently improved student engagement, motivation, course completion, and academic achievement compared with traditional learning management systems. Students also exhibited stronger learning adaptability and more effective responses to personalized feedback, both of which emerged as key contributors to academic success. By integrating behavioral analytics with real-time instructional adaptation, the proposed framework moves beyond content personalization toward a responsive learning ecosystem. This study contributes a scalable AI-enabled instructional framework that supports learner-centered higher education and provides practical guidance for implementing adaptive digital learning environments
Co-Authors Andi Vania Ghalliyah Putrie Desvi Wahyuni Dita Amelia Eko Risdianto Firda Aulia Pratiwi Joseline Santos Laura Mahendratta Tjahjono M. Fariz Fadillah Mardianto Mona Ardina Noel Lomerio Pressylia Aluisina Putri Widyangga Putu Eka Andriani Rita Sinthia Tri Basuki Kurniawan