3 STRUCTURAL EQUATION MODEL ANALYSIS OF FACTORS INFLUENCING SELF-REGULATED LEARNING COMPETENCIES IN THE DIGITAL WORLD OF GRADE 9 STUDENTS
Article Sidebar
Main Article Content
Abstract
This research aimed to develop and examine a structural equation model (SEM) of factors influencing self-regulated learning competencies in the digital world among ninth-grade students. The sample consisted of 225 ninth-grade students selected through multi-stage random sampling. The data collection instrument was a 98-item questionnaire using a five-point Likert scale, with discrimination indices ranging from 0.402 to 0.807 and reliability coefficients between 0.904 and 0.959. Structural equation modeling was analyzed using statistical software.
The research findings revealed that: 1) the variables within the model had a statistically significant positive relationship at the .01 level with 325 values, with a correlation coefficient between 0.249 and 0.725, indicating low to high levels. 2) The model demonstrated a good fit with empirical data ( (269, N = 225) = 307.040, /df = 1.141, p = 0.0551, CFI = 0.991, TLI =0.989, RMSEA = 0.025, SRMR = 0.031). 3) Learning adaptability, perceived ease of use of technology, and technology acceptance collectively influenced self-regulated learning competencies in the digital world. Factors that positively influenced self-regulated learning competencies in the digital world were learning adaptability, perceived internet self-efficacy, and technology acceptance, while perceived ease of use of technology had a negative direct influence and perceived internet self-efficacy had a negative indirect influence on self-regulated learning competencies in the digital world with statistical significance at the .05 level.
Article Details
References
An, F., Xi, L., & Yu, J. (2024). The relationship between technology acceptance and self-regulated learning: The mediation roles of intrinsic motivation and learning engagement. Education and Information Technologies, 29(3), 2605-2623.
Anuntachai, N. & Niramon, P. (2021). Factors affecting Self-Regulated Learning of Generation Z (GEN Z) students of Mahanakorn University of Technology. MUT Journal of Business Administration, 18(1), 1-28.
Chianchana, C., Wattananonsakul, S., & Aieamyeesoon, P. (2010). Development of Indicators of Student’s Self-regulation: An Application of the Second-order Confirmatory Factor Analysis Model. Technical Education Journal King Mongkut’s University of Technology North Bangkok, 1(2), 1-10. Available on http://ojs.kmutnb.ac.th/index.php/jote/article/view/2836/2166
Dong, X., Yuan, H., Xue, H., Li, Y., Jia, L., Chen, J., Shi, Y., & Zhang, X. (2024). Factors influencing college students' self-regulated learning in online learning environments: A systematic review. Nurse Education Today, 133, 106071. Available on https://doi.org/10.1016/j.nedt.2023.106071
Huang, Y. M., & Liaw, S. S. (2023). Exploring the relationship between adaptability in learning and technology acceptance in digital education. Computers & Education, 203, 104332. Available on https://doi.org/10.1016/j.compedu.2023.104332
Kumyoung, A., Kessung, P., Pinasa, C., Srijumnong, J., & Inyai, C. (2024). Development of a causal model of self-regulated learning by students at Loei Rajabhat University [Original Research]. Frontiers in Education, 9. Available on https://doi.org/10.3389/feduc.2024.1334995
Navarro, R., Vega, V., Bayona, H., Bernal, V., & Garcia, A. (2023). Relationship between technology acceptance model, self-regulation strategies, and academic self-efficacy with academic performance and perceived learning among college students during remote education [Original Research]. Frontiers in Psychology, 14. Available on https://doi.org/10.3389/fpsyg.2023.1227956
OECD. (2019). PISA 2018 assessment and analytical framework. Available on https://doi.org/10.1787/b25efab8-en
OECD. (2023). PISA 2025 learning in the digital world assessment framework (Second Draft). OECD Publishing. Available on https://www.oecd.org/PISA2025-Learning-in-the-Digital-World-Assessment-Framework-Second-Draft.pdf
Office of the National Economic and Social Development Council. (2023). The 13th National Economic and Social Development Plan (2023–2027). Prime Minister's Office.
Pan, X. (2020). Technology acceptance, technological self-efficacy, and attitude toward technology-based self-directed learning: Learning motivation as a mediator [Original Research]. Frontiers in Psychology, 11. Available on https://doi.org/10.3389/fpsyg.2020.564294
Peng, M., Xu, Y., & Xu, C. (2023). Enhancing students’ English language learning via M-learning: Integrating technology acceptance model and S-O-R model. Heliyon, 9(2). Available on https://doi.org/10.1016/j.heliyon.2023.e13302
Schunk, D. H., & Zimmerman, B. J. (1994). Self-regulation in education: Retrospect and prospect. In Self-Regulation of Learning and Performance: Theory, Research, and Practice. Erlbaum.
She, C., Liang, Q., Jiang, W., & Xing, Q. (2023). Learning adaptability facilitates self-regulated learning at school: The chain mediating roles of academic motivation and self-management. Frontiers in Psychology, 14. Available on https://doi.org/10.3389/fpsyg.2023.1162072
Sheninger, E. (2021). Digital leadership: Changing paradigms for changing times. Corwin.
Soetanto, T. V., Proboyo, A., & Putri, P. A. (2020). The indirect effect of computer self-efficacy of e-commerce users on intention to use. Petra International Journal of Business Studies, 3(2), 78-85. Available on https://doi.org/10.9744/ijbs.3.2.75-85
Soper, D. S. (2024). A-priori sample size calculator for structural equation models [Software]. Available on https://www.danielsoper.com/statcalc/
Sui, C.-J., Yen, M.-H., & Chang, C.-Y. (2024). Investigating effects of perceived technology-enhanced environment on self-regulated learning. Education and Information Technologies, 29(1), 161-183. Available on https://doi.org/10.1007/s10639-023-12270-x
Supriyono, Y., Ivone, F. M., Heryadi, D., Beduya, L., & Valencia, L. L. E. A. (2024). Predicting EFL learners’ self-regulated learning through technology acceptance model. JEELS (Journal of English Education and Linguistics Studies), 11(1), 347-376.
Yang, Y., Chen, M., & Li, Z. (2021). Investigating the role of self-regulated learning in enhancing student learning outcomes in digital learning environments. Journal of Educational Computing Research, 59(4), 865-887.
Yavuzalp, N., & Bahcivan, E. (2021). A structural equation modeling analysis of relationships among university students’ readiness for e-learning, self-regulation skills, satisfaction, and academic achievement. Research and Practice in Technology Enhanced Learning, 16(1), 15.
Zhang, H., Xiong, H., Chung, L. Y. F., Wang, Y., Wang, P., Fang, L., Han, L., & Yang, Y. (2023). What affects self-regulated learning ability in undergraduate nursing students: A structural equation modelling approach. Nursing Open, 10(8), 5728-5740.
Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70.
