GRADUATE STUDENTS’ ONLINE RESILIENCE: A PREDICTIVE FRAMEWORK BASED ON SELF-EFFICACY, SOCIAL SUPPORT, SATISFACTION, AND DIGITAL ADAPTABILITY IN A PHILIPPINE PUBLIC HEI

Abstract

This study examined graduate students’ online learning resilience by developing and testing a predictive framework that integrates self-efficacy, social support, satisfaction, and digital adaptability within a Philippine public higher education institution. Guided by a convergent parallel mixed-methods design, quantitative and qualitative data were collected and analyzed concurrently to provide a comprehensive understanding of resilience in online learning environments. The quantitative strand employed a correlational design using survey data from 200 graduate students. Descriptive statistics and Structural Equation Modeling (SEM) were utilized to assess variable levels, direct effects, and mediation pathways. The qualitative strand involved semi-structured interviews with 30 purposively selected participants and was analyzed using reflexive thematic analysis. Integration was conducted through joint displays and meta-inference generation. Results indicated that self-efficacy, social support, satisfaction, and digital adaptability were all rated at high levels. SEM results revealed that self-efficacy had a strong and significant direct effect on online resiliency, while social support demonstrated a moderate but significant effect. Satisfaction partially mediated the relationship between self-efficacy and resiliency, whereas digital adaptability fully mediated the relationship between social support and resiliency. Qualitative findings reinforced these results, highlighting confidence, peer and faculty support, instructional clarity, and adaptive use of technology as key components of resilience. Integrated findings suggest that online learning resilience among graduate students emerges from the interaction of internal beliefs, external support systems, positive learning experiences, and adaptive technological engagement. The study contributes to resilience and online learning literature by validating a contextually grounded predictive framework and offers practical implications for designing resilience-oriented graduate education in digital learning environments.