From Self-Efficacy to Civic Literacy: Modeling the Motivational and Information Pathways among Chinese University Students
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Abstract
This study aims to establish the reliability and construct validity of four instruments, test a theory-driven structural model in which self-efficacy predicts learning motivation and information literacy, learning motivation predicts information literacy, and learning motivation and information literacy predict civic literacy, estimate total direct and indirect effects among the constructs and evaluate overall model fit, and examine differences by gender academic year and discipline. Undergraduate students from a university in Xi’an, China, provided 323 valid responses collected via stratified sampling. Pearson correlations were positive among all variables, with the strongest association between self-efficacy and learning motivation (r = .474). Instrument quality was adequate, with all α > .82, KMO = .902, a significant Bartlett’s test, four PCA factors explaining 63.626% of variance, and strong loadings (~.70–.82) without salient cross-loadings, indicating good structural and discriminant validity. The SEM showed excellent fit, χ²/df = 1.296, RMSEA = .030, GFI = .943, NFI = .930, TLI = .983, CFI = .983. Path tests supported the theorized chain, with self-efficacy predicting learning motivation β = .565, learning motivation predicting information literacy β = .352, and information literacy emerging as the strongest proximal predictor of civic literacy β = .398. Group differences indicated higher self-efficacy for women t = −3.787, p < .001, significant academic-year effects on information literacy and civic literacy F = 3.666, p = .013; F = 2.783, p = .041, significant discipline effects on self-efficacy and information literacy F = 3.929, p = .004; F = 3.640, p = .006, and nonsignificant effects for home location and daily internet-use duration. Findings inform course and program designs that scaffold motivation into high-quality information practices to strengthen civic readiness.
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