Volume 15 (2025) Download Cover Page

Motivation in Self-regulated Learning and Technology-use Efficacy among Filipino University Students on an Island: Indirect Effects of Perceived Value, Pressure, Interest, and Effort

Article Number: e2025112  |  Published Online: March 2025  |  DOI: 10.22521/edupij.2025.15.112

Vilma P. Gayrama

Abstract

Background. This study underscores the importance of motivation in self-regulated learning and technology-use efficacy, particularly in the context of online learning modality. The transition to blended and hybrid learning modalities has necessitated a reevaluation of the factors influencing student success.

Methods. This quantitative survey study was conducted at Biliran Province State University (BiPSU) in the Philippines. Using a convenience sampling approach, data were collected through a Likert scale questionnaire to 800 respondents’ undergraduate students enrolled in the second semester of the 2022-2023 academic year. The study employed Partial Least Squares - Structural Equation Modeling (PLS-SEM) through SmartPLS to explore the relationships between perceived competence, value, pressure/tension, interest, effort, and technology-use efficacy. The measurement model was validated by assessing indicator reliability, internal consistency, construct reliability, and discriminant validity. The study's exploratory nature and statistical approach enabled a robust analysis of factors influencing students’ technology-use efficacy.

Results. The results revealed that reducing pressure/tension and enhancing the perceived value of tasks are significant pathways to improving technology-use efficacy. Specifically, perceived choice and relatedness reduce pressure/tension, and both perceived choice and competence increase the value of the task, leading to higher technology-use efficacy. Effort/importance and interest/enjoyment did not significantly mediate the relationships between the predictors and technology-use efficacy.

Conclusion. Fostering a sense of autonomy, competence, and relatedness may be more critical to promoting effective technology use than focusing solely on effort or enjoyment.

Keywords: Motivation, self-regulated learning; technology-use efficacy, online learning modality, higher education, perceived value

References

Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction. International Review of Research in Open and Distributed Learning, 4(2).

Areepattamannil, S., & Santos, I. M. (2019). Adolescent students’ perceived information and communication technology (ICT) competence and autonomy: Examining links to dispositions toward science in 42 countries. Computers in Human Behavior, 98, 50–58. https://doi.org/https://doi.org/10.1016/j.chb.2019.04.005

Artino, A. R. (2007). Self-regulated learning in online education: A review of the empirical literature. International Journal of Instructional Technology and Distance Learning, 4(6). http://itdl.org/Journal/Jun_07/article01.htm

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191

Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.

Biliran Province State University. (2020). Framework of the Pilot Implementation of the Flexible Learning Delivery: The BiPSU Model.

Center for Self-Determination Theory. (n.d.). Intrinsic Motivation Inventory. https://selfdeterminationtheory.org/intrinsic-motivation-inventory/

CHED. (2015). Establishing the Policies and Guidelines on Gender and Development in the Commission on Higher Education and Higher Education Institutions (HEIs): CHED Memorandum Order No. 1, s.2015 (26 January 2015).

CHED. (2020). Guidelines on the Implementation of Flexible Learning: CHED Memorandum Order No. 4, s.2020 (2 September 2020).

Chiu, T. K. F. (2022). Applying the Self-determination Theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(sup1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998

Cohen, J. (1998). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01

Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human     behavior. Springer.

Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109-132

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavioral Research Methods, 41, 1149–1160.

Gagné, M., Parker, S. K., Griffin, M. A., Dunlop, P. D., Knight, C., Klonek, F. E., & Parent-Rocheleau, X. (2022). Understanding and shaping the future of work with self-determination theory. Nature Reviews Psychology, 1(7), 378–392. https://doi.org/10.1038/s44159-022-00056-w

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87–105.

Hair, J. F., Frisher, J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage.

Hair, J., Sarstedt, M., Ringle, C., & Gudergan, S. (2017). Advanced Issues in Partial Least Squares Structural Equation Modeling. Sage Publications, Inc.

Hsiao, C. H., & Yang, C. (2011). The intellectual development of the technology acceptance model: A co-citation analysis. International Journal of Information Management, 31(2), 128–136. https://doi.org/10.1016/j.ijinfomgt.2010.07.003

Joo, Y.-J., Bong, M., & Choi, H.-J. (2000). Self-efficacy for self-regulated learning, academic self-efficacy, and internet self-efficacy in web-based instruction. Educational Technology Research and Development, 48(2), 5–17. https://doi.org/10.1007/BF02313398

Kocdar, S., Karadeniz, A., Bozkurt, A., & Buyuk, K. (2018). Measuring self-regulation in self-paced open and distance learning environments. The International Review of Research in Open and Distributed Learning, 19(1). https://doi.org/10.19173/irrodl.v19i1.3255

Li, J., Ye, H., Tang, Y., Zhou, Z., & Hu, X. (2018). What Are the Effects of Self-Regulation Phases and Strategies for Chinese Students? A Meta-Analysis of Two Decades Research of the Association Between Self-Regulation and Academic Performance   . In Frontiers in Psychology   (Vol. 9). https://www.frontiersin.org/articles/10.3389/fpsyg.2018.02434

Miltiadou, M., & Yu, C. H. (2000). Validation of the Online Technologies Self-Efficacy Scale (OTSES). https://eric.ed.gov/?id=ED445672

Moore, M. G. (1993). Theory of transactional distance. In Keegan, D. (Ed.), Theoretical principles of distance education (pp. 22-38). Routledge.

Nota, L., Soresi, S., & Zimmerman, B. J. (2004). Self-regulation and academic achievement and resilience: A longitudinal study. International Journal of Educational Research, 41(3), 198–215. https://doi.org/https://doi.org/10.1016/j.ijer.2005.07.001

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Or, Caleb. (2024). Thirty-Five Years of the Technology Acceptance Model: Insights From Meta-   Analytic Structural Equation Modelling. Technology in Education, Society, and Scholarship Association Journal: 2024, Vol. 4(3) 1 DOI: https://www.doi.org/10.18357/otessaj.2024.4.3.66

Pan, X. (2020). Technology acceptance, technological self-efficacy, and attitude toward technology-based self-directed learning: Learning motivation as a mediator. Frontiers in Psychology, 11(564294). https://doi.org/10.3389/fpsyg.2020.564294

Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315-341.

Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS. http://www.smartpls.com

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic   motivation, social development, and well-being. American Psychologist, 55(1), 68-78.

Sun, J. C.-Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204.

Schunk, D. H., & Usher, E. L. (2019). Social Cognitive Theoretical Perspective of Self-Regulation. In J. A. Greene, W. A. Sandoval, & I. Bråten (Eds.), Handbook of Self-Regulation of Learning and Performance (2nd ed., pp. 34–48). Routledge.

Stern, B. S. (2004). A comparison of online and face-to-face instruction in an undergraduate foundations of American education course. Contemporary Issues in Technology and Teacher Education, 4(2). https://citejournal.org/volume-4/issue-2-04/general/a-comparison-of-online-and-face-to-face-instruction-in-an-undergraduate-foundations-of-american-education-course

Stolk, J. D., Gross, M. D., & Zastavker, Y. V. (2021). Motivation, pedagogy, and gender: examining the multifaceted and dynamic situational responses of women and men in college STEM courses. International Journal of STEM Education, 8(1), 35. https://doi.org/10.1186/s40594-021-00283-2

Wang, C.-H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323. https://doi.org/10.1080/01587919.2013.835779

Zimmerman, B. J. (2015). Self-Regulated Learning: Theories, Measures, and Outcomes (J. D. B. T.-I. E. of the S. & B. S. (Second E. Wright (Ed.); pp. 541–546). Elsevier. https://doi.org/https://doi.org/10.1016/B978-0-08-097086-8.26060-1

Zimmerman, B. J., & Schunk, D. H. (1989). Self-regulated learning and academic achievement: Theory, research, and practice. In Self-regulated learning and academic achievement: Theory, research, and practice. Springer-Verlag Publishing. https://doi.org/10.1007/978-1-4612-3618-4

Announcement

EDUPIJ Citation Metrics

EDUPIJ News!

► Educational Process International Journal has changed to publish in article number order instead of in page range order beginning with Volume 14 (2025).