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All Journal Journal Neosantara Hybrid Learning International Journal of Language and Ubiquitous Learning
Jordan Oson
Nauru College

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THE USE OF PREDICTIVE ANALYTICS AND AI FOR EARLY INTERVENTION WITH AT-RISK STUDENTS IN A LARGE-SCALE HYBRID LEARNING MODEL Carissa Ien; Jordan Oson; Elizabeth Aiton
Journal Neosantara Hybrid Learning Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnhl.v3i4.3348

Abstract

The rapid growth of large-scale hybrid learning environments has increased the need for data-driven approaches to identify and support at-risk students before disengagement or dropout occurs. Many institutions struggle to respond proactively due to the absence of predictive mechanisms that translate real-time learning data into actionable interventions. This study investigates the use of predictive analytics and artificial intelligence (AI) for early identification and intervention among at-risk students within a large-scale hybrid university program. The research aims to evaluate how machine learning models can detect behavioral and academic risk patterns and how these predictions can inform timely academic support strategies. A quantitative predictive research design was employed using secondary data from the university’s learning management system (LMS) and student information records. Data from 5,000 hybrid learners were analyzed using regression-based predictive models and supervised machine learning algorithms, including random forest and logistic regression, to determine key predictors of risk. Validation was conducted through cross-validation and accuracy metrics. The results revealed that engagement frequency, assessment completion rate, and login regularity were the strongest predictors of student risk, with predictive accuracy reaching 89%. Early interventions informed by predictive insights such as personalized feedback and AI-assisted tutoring led to a 23% reduction in course withdrawal rates. The study concludes that predictive analytics and AI can significantly enhance institutional capacity for proactive intervention in hybrid education. The integration of automated early-warning systems represents a transformative approach to promoting equity, retention, and personalized learning support at scale
Cross-Cultural Communication Through Virtual Language Exchanges: A Southeast Asian Perspective Lucas Mae; Pietro Guidi; Jordan Oson
International Journal of Language and Ubiquitous Learning Vol. 3 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijlul.v3i4.2995

Abstract

Background. Globalization and rapid technological advancement have transformed the development of intercultural understanding, particularly through virtual language exchanges that connect learners across national boundaries. Purpose. This study aimed to explore the influence of virtual language exchange programs on cross-cultural communication among Southeast Asian university students, with a focus on the development of intercultural awareness, linguistic adaptability, and digital collaboration skills. Method. A mixed-method research design was employed involving 120 university students from Indonesia, Malaysia, Thailand, and Vietnam. Quantitative data were collected through surveys administered before and after participation in virtual exchanges, while qualitative data were obtained from online interviews and discussion logs. Results. The findings showed a significant improvement in participants’ cross-cultural sensitivity and confidence in intercultural communication (p < .05). Qualitative analysis revealed that sustained virtual interaction fostered empathy, respect for linguistic diversity, and greater awareness of cultural nuances in communication. Conclusion. The study concludes that virtual language exchange programs are effective pedagogical tools for enhancing cross-cultural communication and promoting regional connectivity in Southeast Asia.
Co-Authors Carissa Ien Elizabeth Aiton Lucas Mae Pietro Guidi