3,679
Views
21
CrossRef citations to date
0
Altmetric
Articles

The influence of motivation on learning engagement: the mediating role of learning self-efficacy and self-monitoring in online learning environments

ORCID Icon & ORCID Icon
Pages 4605-4618 | Received 09 Mar 2021, Accepted 03 Sep 2021, Published online: 15 Sep 2021

References

  • Bandura, A. (1997). Self-efficacy: The exercise of control. Freema.
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238
  • Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structure. Psychological Bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588
  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications and programming (2nd ed.). Routledge.
  • Celikkaleli, O. (2014). The relation between cognitive flexibility and academic, social, and emotional self-efficacy beliefs among adolescents. Egitim ve Bilim (Education and Science), 39, 347–354.
  • Demuyakor, J. (2020). Coronavirus (COVID-19) and online learning in higher institutions of education: A survey of the perceptions of Ghanaian International students in China. Online Journal of Communication and Media Technologies, 10(3). https://doi.org/10.29333/ojcmt/8286
  • Elmaadaway, M. A. N. (2017). The effects of a flipped classroom approach on class engagement and skill performance in a blackboard course. British Journal of Educational Technology. https://doi.org/10.1111/bjet.12553
  • Fisher, M. J., & King, J. (2010). The self-directed learning readiness scale for nursing education revisited: A confirmatory factor analysis. Nurse Education Today, 30(1), 44–48. https://doi.org/10.1016/j.nedt.2009.05.020
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  • Garrison, D. R. (1997). Self-directed learning: Toward a comprehensive model. Adult Education Quarterly, 48(1), 18–33. https://doi.org/10.1177/074171369704800103
  • Hagaman, J. L., & Reid, R. (2008). The effects of the paraphrasing strategy on the reading comprehension of middle school students at risk for failure in reading. Remedial and Special Education, 29(4), 222–234. https://doi.org/10.1177/0741932507311638
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis with readings (7th ed.). Pearson.
  • Han, Y., & Hyland, F. (2015). Exploring learner engagement with written corrective feedback in a Chinese tertiary EFL classroom. Journal of Second Language Writing, 30, 31–44. https://doi.org/10.1016/j.jslw.2015.08.002
  • Harris, K. R. (1982). Cognitive-behavior modification: Application with exceptional students. Focus Except. Childr, 15(2), 1–16.
  • Harvey, S., & Goudvis, A. (2007). Strategies that work: Teaching comprehension for understanding and engagement. Stenhouse Publishers.
  • Hoban, S., & Hoban, G. (2004). Self-esteem, self-efficacy and self-directed learning: Attempting to undo the confusion. International Journal of Self-Directed Learning, 1(2), 7–25.
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
  • Jamaludin, R., & Osman, S. Z. (2014). The use of a flipped classroom to enhance engagement and promote active learning. Journal of Education and Practice, 5, 124–131.
  • Kahn, P., Everington, L., Kelm, K., Reid, I., & Watkins, F. (2017). Understanding student engagement in online learning environments: The role of reflexivity. Educational Technology Research & Development, 65(1), 203–218. https://doi.org/10.1007/s11423-016-9484-z
  • Kaplan, R. W., & Saccuzzo, D. P. (1982). Psychological testing: Principles, applications, and issues. Brooks & Cole.
  • Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). The Guilford Press. https://doi.org/10.1017/CBO9781107415324.004.
  • Kuh, G. D. (2001). The National survey of student engagement: Conceptual framework and overview of psychometric properties. Indiana University, Center for Postsecondary Research.
  • Luo, H., Koszalka, T. A., Arnone, M. P., & Choi, I. (2018). Applying case-based method in designing self-directed online instruction: A formative research study. Educational Technology Research and Development, 66(2), 515–544. https://doi.org/10.1007/s11423-018-9572-3
  • MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130
  • Martin, A. J., Anderson, J., Bobis, J., Way, J., & Vellar, R. (2012). Switching on and switching off in mathematics: An ecological study of future intent and disengagement among middle school students. Journal of Educational Psychology, 104(1), 1–18. https://doi.org/10.1037/a0025988
  • Matell, M. S., & Jacoby, J. (1971). Is there an optimal number of alternatives for Likert scale items? Study1: Reliability and validity. Educational and Psychological Measurement, 31(3), 657–674. https://doi.org/10.1177/001316447103100307
  • McDonald, R. P. (1978). Generalizability in factorable domains: Domain validity and generalizability. Educational and Psychological Measurement, 38(1), 75–79. https://doi.org/10.1177/001316447803800111
  • Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of evidence based practices in online learning: A meta-analysis and review of online learning studies. US Department of Education. http://eric.ed.gov/?id=ED505824
  • Pardo, A., Han, F., & Ellis, R. A. (2017). Combining University student self-regulated learning indicators and engagement with online learning events to predict academic performance. IEEE Transactions on Learning Technologies, 10(1), 82–92. https://doi.org/10.1109/tlt.2016.2639508
  • Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G., & Hanson, J. (2016). Attitude, digital literacy and self-efficacy: Flow-on effects for online learning behavior. Internet and Higher Education, 29, 91–97. https://doi.org/10.1016/j.iheduc.2016.01.001
  • Purarjomandlangrudi, A., & Chen, D. (2020). Exploring the influence of learners’ personal traits and perceived course characteristics on online interaction and engagement. Educational Technology Research and Development, 1–23.
  • Ray, A. E., Greene, K., Pristavec, T., Hecht, M. L., Miller-Day, M., & Banerjee, S. C. (2020). Exploring indicators of engagement in online learning as applied to adolescent health prevention: A pilot study of REAL media. Educational Technology Research and Development, https://doi.org/10.1007/s11423-020-09813-1
  • Rohs, M., & Ganz, M. (2015). MOOCs and the claim of education for all: A disillusion by empirical data. The International Review of Research in Open and Distributed Learning, https://doi.org/10.19173/irrodl.v16i6.2033
  • Ryan, A. M., & Patrick, H. (2001). The classroom Social environment and changes in adolescents’ motivation and engagement during middle school. American Educational Research Journal, 38(2), 437–460. https://doi.org/10.3102/00028312038002437
  • Schunk, D. H., & Mullen, C. A. (2012). Self-efficacy as an engaged learner. In S. J. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 219–235). Springer.
  • Shen, D., Cho, M.-H., Tsai, C.-L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. The Internet and Higher Education, 19, 10–17. https://doi.org/10.1016/j.iheduc.2013.04.001
  • Steiger, J. H., & Lind, J. C. (1980). Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society, Iowa City, IA, May 28-30.
  • 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. https://doi.org/10.1111/j.1467-8535.2010.01157.x
  • Sun, Y., Ni, L., Zhao, Y., Shen, X. L., & Wang, N. (2019). Understanding students’ engagement in MOOCs: An integration of self-determination theory and theory of relationship quality. British Journal of Educational Technology, 50(6), 3156–3174. https://doi.org/10.1111/bjet.12724
  • Sze-yeng, F., & Hussain, R. M. R. (2010). Self-directed learning in a socio constructivist learning environment. Procedia Social and Behavioral Sciences, 9, 1913–1917. https://doi.org/10.1016/j.sbspro.2010.12.423
  • Tseng, H., Kuo, Y.-C., & Walsh, E. J. (2020). Exploring first-time online undergraduate and graduate students’ growth mindsets and flexible thinking and their relations to online learning engagement. Educational Technology Research and Development, 68(5), 2285–2303. https://doi.org/10.1007/s11423-020-09774-5
  • Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10. https://doi.org/10.1007/BF02291170
  • Walker, C. O., Greene, B. A., & Mansell, R. A. (2006). Identification with academics, intrinsic/extrinsic motivation, and self-efficacy as predictors of cognitive engagement. Learn Individual Differences, 16(1), 1–1. https://doi.org/10.1016/j.lindif.2005.06.004
  • Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. John Wiley & Sons.
  • Williamson, S. N. (2007). Development of a self-rating scale of self-directed learning. Nurse Researcher, 14(2), 66–83. https://doi.org/10.7748/nr2007.01.14.2.66.c6022
  • Wu, H., Li, S., Zheng, J., & Guo, J. (2020). Medical students’ motivation and academic performance: The mediating roles of self-efficacy and learning engagement. Medical Education Online, 25(1), 1742964. https://doi.org/10.1080/10872981.2020.1742964
  • Wu, J.-H., Tennyson, R. D., & Hsia, T.-L. (2010). A study of student satisfaction in a blended e-learning system environment. Communication Education, 55(1), 155–164.
  • Yu, S., Zhang, Y., Zheng, Y., Yuan, K., & Zhang, L. (2019). Understanding student engagement with peer feedback on master’s theses: A Macau study. Assessment & Evaluation in Higher Education, 44(1), 50–65. https://doi.org/10.1080/02602938.2018.1467879
  • Zhu, M., Bonk, C. J., & Doo, M. Y. (2020). Self-directed learning in MOOCs: Exploring the relationships among motivation, self-monitoring, and self-management. Educational Technology Research and Development, 1–21. https://doi.org/10.1007/s11423-020-09747-8.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.