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Updated: Jul 25, 2025

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
Published on: July 1, 2015
Influence of Individual-technology-task-environment Fit on University Student Online Learning Performance: The
Kaifeng Liu1,2,3, Jingtao Yao2,3, Da Tao4
1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Abstract:
The present study aimed to examine whether and to what extent university student online learning performance was influenced by individual-technology fit (ITF), task-technology fit (TTF), environment-technology fit (ETF), and whether the influence was mediated by their behavioral, emotional, and cognitive engagement. A theoretical research model was developed by integrating the extended TTF theory and student engagement framework. The validity of the model was assessed using a partial least squares structural equation modeling approach based on data collected from 810 university students. Student learning performance was influenced by TTF (β = 0.25, p < 0.001), behavioral engagement (β = 0.25, p < 0.001), and emotional engagement (β = 0.27, p < 0.001). Behavioral engagement was affected by TTF (β = 0.31, p < 0.001) and ITF (β = 0.41, p < 0.001). TTF, ITF, and ETF were observed as significant antecedents of emotional engagement (β = 0.49, p < 0.001; β = 0.19, p < 0.001; β = 0.12, p = 0.001, respectively) and cognitive engagement (β = 0.28, p < 0.001; β = 0.34, p < 0.001; β = 0.16, p < 0.001, respectively). Behavioral and emotional engagement served as mediators between fit variables and learning performance. We suggest the need for an extension to the TTF theory by introducing ITF and ETF dimensions and demonstrate the important role of these fit variables in facilitating student engagement and learning performance. Online education practitioners should carefully consider the fit between the individual, task, environment, and technology to facilitate student learning outcomes.
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