Enhancing Multidimensional Student Engagement in a College Chemistry Class Through Group-Based Flipped Instruction

This quasi-experimental study investigated the effects of group-based flipped instruction (GBFI) compared to a conventional blended learning setup (CBLS) on multidimensional student engagement in a college chemistry course in the Philippines. Student engagement was conceptualized as behavioral, emotional, cognitive, and agentic, and interpreted through Self-Determination Theory (SDT), which highlights autonomy, competence, and relatedness needs. Two purposively selected, high-risk first-year classes (n = 61) were matched on pre-intervention engagement and randomly assigned to either GBFI (experimental) or CBLS (control). Both groups studied identical chemistry topics over nine weeks using blended formats. Engagement was assessed before and after the intervention with adapted multidimensional self-report scales, which were pilot-tested and reviewed by experts for reliability and content validity. Data analysis included covariate screening (Mann-Whitney U tests, Chi-square tests, and simple linear regression), t-tests, and Analysis of Covariance (ANCOVA) or Quade’s ANCOVA, with significant covariates controlled. Both groups demonstrated improvement; however, GBFI yielded significantly greater gains across all four engagement dimensions, with large effects observed for agentic and cognitive engagement. These results suggest that GBFI, when implemented with sufficient facilitation and fidelity, can function as a need-supportive instructional design that enhances behavioral participation, emotional investment, cognitive effort, and student agency in blended chemistry learning. The study recommends phased implementation of GBFI in blended science courses, supported by consistent structures, robust teacher facilitation, faculty training, institutional support, the use of adapted engagement measures, and further research involving broader learning outcomes, larger samples, and diverse contexts to enhance effectiveness and scalability.