Investigating Students' Learning Efficacy in Private Universities: An Extended Technology Acceptance Model
Keywords:
Technology Acceptance Model, Learning Efficacy, Learning Analytics, Sociocultural Theory, Private Universities, ; Structural Equation Modeling (SEM), ChinaAbstract
The conventional Technology Acceptance Model (TAM) is widely adopted in educational technology research yet suffers notable limitations: it mainly predicts educational technology adoption intentions and lacks sufficient explanatory power for actual learning outcomes. Rooted in Vygotsky’s sociocultural theory and learning analytics research, this study develops an extended TAM by incorporating three constructs: Digital Teaching Model (DTM), Learning Analytics Engagement (LAE), and Learning Self-Efficacy (LSE), to address the theoretical deficits of the original model. Questionnaire data were collected from 512 undergraduate students at Shandong Xiehe College, and structural equation modelling (SEM) was employed to test direct and mediating relationships among latent variables. The empirical results indicate that DTM exerts significantly positive effects on students’ perceived usefulness, perceived ease of use, and LAE. Furthermore, LAE serves as a critical mediator that transforms learners’ technology perceptions into improved learning efficacy. This study contributes to the literature in three ways. Theoretically, it extends TAM by integrating sociocultural perspectives and learning analytics to better predict learning performance. Empirically, it verifies the mediating role of LAE and clarifies the causal path linking technology acceptance to effective learning. Practically, it provides context-specific evidence for private higher education in China and delivers actionable strategies for implementing data-driven digital teaching
References
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256.
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17, Article 2.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593.
Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63–84.
Jivet, I., Wong, J., Scheffel, M., Valle Torre, M., Specht, M., & Drachsler, H. (2021). Quantum of choice: How learners' feedback monitoring decisions, goals and self-regulated learning skills are related. LAK21: 11th International Learning Analytics and Knowledge Conference, 416–427.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wertsch, J. V. (1991). Voices of the mind: A sociocultural approach to mediated action. Harvard University Press.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39.
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