Development and Validation of an Academic Engagement Scale for Undergraduate Students in Distance Learning
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Abstract
Distance education at open universities faces significant challenges, including high attrition rates driven by social isolation and the demands of self-directed learning. A context-specific instrument is essential for understanding the mechanisms that support student persistence. This study aimed to develop and validate an academic engagement scale, with development referring to the refinement of an existing framework. Drawing on constructivist and motivational theories, academic engagement was conceptualized as a second-order construct comprising cognitive, emotional, and behavioral dimensions. Data were collected from 440 students at Sukhothai Thammathirat Open University using cluster sampling based on academic disciplines (Social Sciences, Health Sciences, and Science and Technology). Confirmatory Factor Analysis (CFA) was conducted to examine the hierarchical structure, while Cronbach’s alpha, Average Variance Extracted (AVE), and the Heterotrait–Monotrait (HTMT) ratio were used to assess reliability and construct validity.
The model fit indices indicated an acceptable fit to the data (/df = 3.22, RMSEA = .07, CFI = .92, NFI = .91, and SRMR = .04). All dimensions demonstrated satisfactory reliability and validity, with HTMT values below .85, supporting the distinctiveness of the constructs. Cognitive engagement showed strong associations within the model, highlighting the role of self-regulated learning in distance education. The findings provide empirical support for a multidimensional structure of academic engagement and contribute a context-specific measurement tool that can be applied to support student engagement and retention in an open university distance learning context.
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References
Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020). Evaluating e-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004
Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health, 6, 149. https://doi.org/10.3389/fpubh.2018.00149
Fredricks, J. A. (2012). School engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 1–11). Springer.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Publications.
Kuh, G. D. (2001). Assessing what really matters to student learning: Inside the National Survey of Student Engagement. Change: The Magazine of Higher Learning, 33(3), 10–17. https://doi.org/10.1080/00091380109601795
Lerdpornkulrat, T. (2023). Analyzing student learning behavior in a blended-learning environment in Thai higher education. Journal of New Horizon in Higher Education, 1(1), 38–46. https://so09.tci-thaijo.org/index.php/NHHE/article/view/2147
Moore, M. G. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical principles of distance education (pp. 22–38). Routledge.
Piaget, J. (1970). Science of education and the psychology of the child (D. Coltman, Trans.). Orion Press.
Schaufeli, W. B., Martínez, I. M., Pinto, A. M., Salanova, M., & Bakker, A. B. (2002). Burnout and engagement in university students: A cross-national study. Journal of Cross-Cultural Psychology, 33(5), 464–481. https://doi.org/10.1177/0022022102033005003
Streiner, D. L., Norman, G. R., & Cairney, J. (2015). Health measurement scales: A practical guide to their development and use (5th ed.). Oxford University Press.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson Education.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wang, M. T., & Eccles, J. S. (2012). Social support matters: Longitudinal effects of social support on three dimensions of school engagement from middle to high school. Child Development, 83(3), 877–895. https://doi.org/10.1111/j.1467-8624.2012.01745.x
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7
Metheekul, P. (2022). Factors contributing to learning success in distance education: A case study of undergraduate students at Sukhothai Thammathirat Open University. Silpakorn University Journal of Education, 20(1), 85–101. (In Thai)
Sungsri, S. (2018). A model of educational services to enable graduate students of Sukhothai Thammathirat Open University to achieve learning success in the distance education system. STOU Education Journal, 10(2), 65–78. (In Thai)
Wangrungkit, P. (2018). Development of indicators and program to enhance academic engagement of undergraduate students with personal best goals as a mediator [Doctoral dissertation]. Srinakharinwirot University. (In Thai)
