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Journal Neosantara Hybrid Learning
Published by Yayasan Adra Karima Hubbi
ISSN : 29872316     EISSN : 2986979X     DOI : 10.70177/jnhl
Core Subject :
Journal Neosantara Hybrid Learning provides pedagogical, learning and educational perspectives on topics relevant to the study, implementation and management of e-learning initiatives. Journal Neosantara Hybrid Learning has published regular issues since 2023 and averages 3 issues a year. In 2025, the journal introduced a change in its publication frequency to a bimonthly schedule, publishing issues in February, April, June, August, October, and December. The journal contributes to the development of both theory and practice in the field of e-learning. The Editorial team consider academically robust papers and welcome empirical research, case studies, action research, theoretical discussions, literature reviews and other work which advances learning in this field. All papers are double-blind peer reviewed.
Arjuna Subject : -
Articles 3 Documents
 1 
Search results for , issue "vol. 4 no. 3 (2026)" : 3 Documents clear
DIGITAL WELLBEING IN HYBRID LEARNING ENVIRONMENTS: BALANCING ACADEMIC PERFORMANCE AND MENTAL HEALTH Catherine Li; Andres Villanueva; Josefa Flores; Noorhani Dyani Laksmi
Journal Neosantara Hybrid Learning Vol. 4 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnhl.v4i3.3974

Abstract

The widespread institutionalization of hybrid learning environments has intensified student exposure to digitally mediated academic demands, generating mounting concern among educators, policymakers, and mental health professionals regarding the cumulative effects of prolonged screen engagement, boundary dissolution between academic and personal digital spaces, and algorithmically driven distraction on student psychological wellbeing and academic performance outcomes. Existing frameworks for digital wellbeing have been developed predominantly for general consumer technology contexts and remain insufficiently theorized for the specific cognitive, emotional, and social demands of hybrid educational settings. This study aims to examine the relationship between digital wellbeing practices, academic performance, and mental health outcomes among students engaged in hybrid learning environments across tertiary educational institutions. A sequential explanatory mixed-methods design was employed, combining a quantitative survey administered to 624 undergraduate students across four universities with qualitative interviews conducted with 36 purposively selected participants, analyzed through structural equation modeling and reflexive thematic analysis respectively. Digital wellbeing practices significantly predicted both academic performance (? = 0.512, p < .001) and mental health outcomes (? = 0.467, p < .001), with screen boundary management and purposeful technology disengagement emerging as the strongest protective factors. Intentional digital wellbeing integration within hybrid learning design substantially reduces psychological distress while sustaining academic performance, necessitating institution-level policy commitments beyond individual student self-regulation.
ARTIFICIAL INTELLIGENCE–DRIVEN PERSONALIZATION IN HYBRID LEARNING: OPPORTUNITIES, RISKS, AND PEDAGOGICAL IMPLICATIONS I Nengah Artawan; Ren Suzuki; Daiki Nishida
Journal Neosantara Hybrid Learning Vol. 4 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnhl.v4i3.4101

Abstract

Growing adoption of hybrid learning has accelerated the integration of Artificial Intelligence (AI) to deliver personalized educational experiences that accommodate diverse learner characteristics, enhance instructional effectiveness, and support data-informed pedagogical decisions. Increasing reliance on AI-driven personalization has simultaneously introduced concerns regarding algorithmic transparency, learner autonomy, data privacy, and educational equity, creating a need for balanced evaluation of its educational potential and associated risks. This study aimed to examine the opportunities, risks, and pedagogical implications of AI-driven personalization within hybrid learning environments. A mixed-methods sequential explanatory design was employed, combining quantitative analysis of learning analytics and academic performance with qualitative investigation through interviews, classroom observations, and reflective journals. Participants included undergraduate students and lecturers engaged in AI-supported hybrid learning across multiple disciplines. Findings revealed that AI-driven personalization significantly improved academic achievement, learner engagement, self-regulated learning, learning satisfaction, and course completion while maintaining high recommendation accuracy and adaptive instructional responsiveness. Qualitative evidence further demonstrated that effective implementation depended on meaningful teacher facilitation, transparent algorithmic support, and responsible institutional governance addressing privacy and ethical concerns. Results indicate that Artificial Intelligence enhances hybrid learning most effectively when functioning as a pedagogical partner rather than a replacement for educators. Balanced integration of adaptive technologies, human expertise, ethical governance, and learner-centered instructional design provides a sustainable framework for responsible educational innovation and long-term digital transformation.
BRAIN-BASED LEARNING STRATEGIES FOR PERSONALIZED HYBRID EDUCATION Raul Gomez; Clara Mendes; Rudy Surbakti
Journal Neosantara Hybrid Learning Vol. 4 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnhl.v4i3.4233

Abstract

Personalized hybrid education offers flexible learning opportunities, yet technological adaptation alone often fails to address differences in attention, memory, cognitive load, prior knowledge, and self-regulation that shape individual learning. This study examined the effectiveness of evidence-informed brain-based learning strategies within personalized hybrid education and developed an adaptive framework for cognitively responsive instruction. A quasi-experimental mixed-methods design compared students receiving brain-informed personalized hybrid instruction with those experiencing conventional hybrid learning. The intervention integrated prior-knowledge activation, retrieval practice, spaced learning, cognitive-load management, adaptive scaffolding, formative feedback, and metacognitive reflection across physical and digital environments. Findings indicated stronger academic achievement, knowledge retention, cognitive engagement, motivation, and self-regulated learning among students receiving personalized instruction, accompanied by more manageable cognitive demands. Learning analytics and qualitative evidence further demonstrated that effective personalization required dynamic adjustments based on learner readiness, performance, progress, and support needs rather than fixed learning-style classifications. The study concludes that brain-informed personalization is most effective when grounded in credible learning science and implemented through continuous cycles of diagnosis, adaptation, retrieval, feedback, monitoring, and recalibration. The proposed framework advances personalized hybrid education by integrating cognitive responsiveness, pedagogical orchestration, and adaptive support while avoiding unsupported neuromyth-based instructional assumptions in contemporary educational practice.

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