Task-Technology Fit in Action: Understanding Learning-Task Alignment in Math Classes at Community Colleges
The rapid expansion of online learning in community college math courses has intensified the need to understand how educational technologies align with students' learning processes. Grounded in the Task–Technology Fit (TTF) framework, this study examines Learning–Task Fit, defined as the degree to which digital tools support the cognitive and procedural demands of instructional math learning tasks. Hence, the purpose of this research is to investigate the dimensionality and reliability of the Learning–Task Fit construct and to identify the key factors shaping students' learning experiences in technology-mediated math learning environments. Data were collected from 134 students enrolled in gateway math courses at a local community college. Moreover, an exploratory factor analysis (EFA) using principal component analysis as the extraction method yielded a single-factor solution, the Learning-Task Fit construct, with strong loadings across six items and an eigenvalue of 3.19, accounting for 53.21% of the variance. Internal consistency was high (Cronbach's α = 0.817), indicating the construct's reliability. Thus, the findings provide strong empirical evidence for the unidimensional and psychometric soundness of the Learning–Task Fit scale in math courses across all modalities at community colleges. Whence, the study highlights the importance of aligning educational technologies with the structure of learning tasks to enhance student engagement, persistence, and performance.
Accepted as Brief Paper