Factors Influencing the Active Engagement of Undergraduate Students in Blended Learning

Abstract

Background and Objectives: This investigation identifies the multifaceted determinants facilitating active engagement among undergraduate students within blended learning environments at Islamic higher education institutions in Central Java, Indonesia. The transition to hybrid pedagogical models necessitates a deeper understanding of student behavior. Consequently, this research integrates the Technology Acceptance Model (Davis, 1989) and the Theory of Planned Behavior (Ajzen, 1991) into a unified framework to better explain student engagement. This integration analyzes how perceptions of technological ease and utility, alongside personal attitudes, social influences, and individual agency, collectively synthesize to form behavioral intentions that dictate actual participation levels in digital academic activities.

Methodology: The study employed a rigorous quantitative methodology using Partial Least Squares Structural Equation Modeling (PLS-SEM). Primary data were derived from a survey of 385 undergraduate respondents across various Islamic colleges in Central Java, selected through a systematic random sampling protocol. The research instrument utilized an adapted questionnaire validated by prior scholarship to ensure reliability. Data processing was executed using SmartPLS software, involving a two-stage evaluation. First, the measurement model was scrutinized for validity and internal consistency. Second, the structural model was assessed using a bootstrapping procedure to determine path coefficients and significance, maintaining a threshold of p < 0.05.

Main Results: The analytical results provided robust empirical support for all six proposed hypotheses. Specifically, findings revealed that perceived ease of use (β = 0.391, p < 0.05) and perceived utility (β = 0.295, p < 0.05) serve as significant precursors to student attitudes. Furthermore, these attitudes (β = 0.600, p < 0.05), combined with the influence of subjective norms (β = 0.018, p < 0.05) and perceived behavioral control (β = 0.171, p < 0.05), were instrumental in fostering behavioral intentions. The most substantial finding was the profound impact of behavioral intention on actual active engagement (β = 0.697, p < 0.05), acting as the primary driver of student participation. The overall model exhibited high explanatory power (R²) and strong predictive relevance (Q²), confirming its validity in this specific educational context.

Discussions: These findings validate the conceptual synergy between TAM and TPB, highlighting that engagement is a socio-psychological issue. The strong correlation between technological perceptions and attitude suggests that institutions must prioritize intuitive platforms. Moreover, the role of subjective norms reflects the unique communal and mentor-based learning culture inherent in Islamic institutions, where social pressure and teacher-student relationships significantly influence digital adoption. This underscores the need for a holistic approach to educational technology that considers both system design and social support structures.

Conclusions: While technical ease and utility are foundational, the cultivation of a positive attitude and a supportive social environment is what truly translates intention into proactive learning behavior. Institutions are encouraged to design mobile-friendly, accessible platforms and leverage the influential role of faculty to boost student confidence and participation. Limitations regarding self-reported data suggest that future research could benefit from longitudinal designs or the inclusion of objective log data to further refine these engagement dynamics.