A Hybrid TAM–Learning Analytics Framework for Predicting University Students’ Adoption of Educational Technology - Liu, Cheng; Yang, Shuang; Qi, Ning; Yan, Wenjing; Xu, Ruining (International Journal of Advanced AI Applications, 2025)
This study develops a hybrid analytical framework integrating the Technology Acceptance Model (TAM) with learning analytics indicators to explain university students’ adoption of educational technology. While TAM emphasizes perceptual constructs such as perceived usefulness (PU) and perceived ease of use (PEOU), modern digital learning systems generate rich behavioral data that may also shape learners’ adoption decisions. Data were collected from 162 undergraduate students using validated measurement scales for PU, PEOU, and behavioral intention (BI), together with self-reported learning analytics indicators including interaction frequency, time-on-task, and digital participation levels. Structural equation modeling was conducted using Python based SEM analysis. Results show that PU and PEOU significantly predict BI, consistent with classical TAM. Incorporating learning analytics indicators improves explanatory power, with hierarchical regression revealing an increase in R2 from 0.689 (TAM-only) to 0.739 in the hybrid model (∆R2 = 0.050). Random Forest analysis further confirms the predictive importance of PU and learning analytics features. These findings demonstrate that behavioral engagement data substantially enhance students’ technology adoption processes. The study contributes theoretically by integrating cognitive and behavioral perspectives of adoption, and offers practical implications for designing more engaging and data-informed digital learning environments.