On the Regulatory Engagement, Internet Self-efficacy, Course Satisfaction, and Reading Comprehension in Online English Classes

Nejati, R.

doi:10.22061/tej.2025.11412.3145

Articles in Press

Document Type : Original Research Paper-English Issue

Author

R. Nejati

Department of English, Faculty of Humanities, Shahid Rajaee Teacher Training University, Tehran, Iran

10.22061/tej.2025.11412.3145

Abstract

Background and Objectives: Online learning involves a complex array of factors that collectively shape the educational experience. One key element is learning engagement, which enhances academic performance, knowledge retention, and overall course satisfaction. Another important aspect is self-regulation; these skills are essential for success in online courses due to the lack of external structure and supervision. Additionally, internet self-efficacy and course satisfaction significantly impact the effectiveness of online education. Considering and addressing these elements can lead to a deeper understanding of online learning.
Materials and Methods: This study involved 315 students, aged 18-22, enrolled in a general language course at a university in Tehran, Iran. Of these, 282 completed the online questionnaires. The study utilized four questionnaires and an English test, including the Online Self-Regulation Questionnaire (30 items), the Online Student Engagement Scale (19 items), the Online Learning Self-Efficacy Scale (22 items), the Course Satisfaction Questionnaire (21 items), and the reading section of the TOEFL (40 items). These were administered through the university's virtual education platform. Persian versions of the questionnaires were used to ensure students’ comprehension. Since these questionnaires had not been previously published or available in Iranian research journals, the researcher translated them with the assistance of two professors proficient in both languages. Back translations ensured accuracy. This descriptive correlational study included statistical analyses such as reliability, correlation, and regression, alongside a thorough construct validity assessment. Additionally, mediation analysis was conducted to examine the complex effects of course satisfaction and internet self-efficacy on the relationship between regulatory engagement and reading comprehension.
Findings: The Cronbach's alpha values for self-regulation, engagement, self-efficacy, course satisfaction, and the reading test were .94, .90, .90, .93, and .86, respectively, indicating strong reliability and internal consistency of the instruments. The RMSEA values for engagement, self-regulation, satisfaction, self-efficacy, and reading were 0.08, 0.07, 0.08, 0.07, and 0.01, respectively, demonstrating satisfactory results. Notably, a coefficient of 0.84 was observed between self-regulation and engagement, and 0.7 between self-efficacy and satisfaction. A bivariate correlation of 0.7 or higher can present challenges in testing and interpreting regression coefficients. To better understand these relationships, the researcher created composite variables named 'regulatory engagement' and 'satisficacy' (course satisfaction and internet self-efficacy). The β index for satisficacy was .47 (p = .00), indicating it as a significant predictor of the learning outcome, while the β index for regulatory engagement was .06 (p = .26), showing a weaker prediction. This finding suggested that regulatory engagement indirectly influenced learning outcomes via satisficacy, with satisficacy (η2=.81) being a significant predictor and regulatory engagement (η2=.47) having an indirect effect through satisficacy. This underscores the critical role of regulatory engagement in enhancing satisficacy and its impact on learning.
Conclusions: The results of this study highlight the importance of promoting regulatory engagement to improve satisfaction and self-efficacy, thereby enhancing reading comprehension. However, this study has its limitations. The data collected from the current sample may not be fully applicable to other contexts. Moreover, the composite variables introduced in this study may add complexity and require further validation in different settings. Future research should investigate the intricate relationships between regulatory engagement, satisficacy, and reading comprehension, considering additional variables and assessing the generalizability of these findings across various populations.

Keywords

Main Subjects

Education technology - higher education

COPYRIGHTS
© 2025 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)

References

[1] Marshall J. Motivating e-learners: Application of the ARCS Model to e-learning for San Diego Zoo global’s animal care professional. J Appl Instr Des. 2018;3(2):21-30.

[2] Taylor DL, Yeung M, Bashet AZ. Personalized and Adaptive Learning. In: Ryoo J, Winkelmann K, editors. Innovative Learning Environments in STEM Higher Education. Springer Briefs in Statistics. Cham: Springer; 2021.

https://doi.org/10.1007/978-3-030-58948-6

[3] Curtis DD, Lawson MJ. Exploring collaborative online learning. J Asynchronous Learn Networks. 2019;5(1):21-34. https://doi.org/10.24059/olj.v5i1.1885

[4] Chiu PHP, Cheng SH. Effects of active learning classrooms on student learning: a two-year empirical investigation on student perceptions and academic performance. High Educ Res Dev. 2016; 36(2):269-79.

https://doi.org/10.1080/07294360.2016.1196475

[5] Cole AW, Lennon L, Weber NL. Student perceptions of online active learning practices and online learning climate predict online course engagement. Interact Learn Environ. 2019;29(5):1-15. https://doi.org/10.1080/10494820.2019.1619593

[6] Renninger KA, Bachrach JE. Studying Triggers for Interest and Engagement Using Observational Methods. Educ Psychol. 2015;50(1):58-69. https://doi.org/10.1080/00461520.2014.999920

[7] She L, Ma L, Jan A, Sharif Nia H, Rahmatpour P. Online Learning Satisfaction During COVID-19 Pandemic Among Chinese University Students: The Serial Mediation Model. Front Psychol. 2021;12. https://doi.org/10.3389/fpsyg.2021.743936

[8] Bradley RL, Browne BL, Kelley HM. Examining the Influence of Self-Efficacy and Self-Regulation in Online Learning. Coll Stud J. 2017;51(4):518-30.

[9] Lim CL, Jalil HA, Ma’rof AM, Saad WZ. Differences in Self-Regulated Learning (SRL) and Online Learning Satisfaction Across Academic Disciplines: A Study of a Private University in Malaysia. Int J Learn Teach. 2020;6(2):62-67.

https://doi.org/10.18178/ijlt.6.2.62-67

[10] Puzziferro M. Online Technologies Self-Efficacy and Self-Regulated Learning as Predictors of Final Grade and Satisfaction in College-Level Online Courses. Am J Distance Educ. 2008;22(2):72-89.

https://doi.org/10.1080/08923640802039024

[11] Warden CA, Yi-Shun W, Stanworth JO, Chen JF. Millennials’ technology readiness and self-efficacy in online classes. Innov Educ Teach Int. 2020;1-11.

https://doi.org/10.1080/14703297.2020.1798269

[12] Omar MK, Hussein N. Factors influencing E-learning satisfaction among students: a study of a public university in Malaysia. World Appl Sci J. 2017;35(4):568-573.

https://doi:10.5829/idosi.wasj.2017.568.573

[13] Nguyen KA, Borrego M, inelli CJ, De Monbrun M, Crockett C, Tharayil S, Shekhar P, Waters C, Rosenberg R. Instructor Strategies to Aid Implementation of Active learning: a Systematic Literature Review. Int J STEM Educ. 2021;8(1).

https://doi.org/10.1186/s40594-021-00270-7

[14] Zimmerman BJ, Schunk DH, editors. Self-Regulated Learning and Academic Achievement. Routledge; 2013. https://doi.org/10.4324/9781410601032

[15] Cho MH, Shen D. Self-regulation in online learning. Distance Educ. 2013;34(3):290-301.

https://doi.org/10.1080/01587919.2013.835770

[16] Kim C, Park SW, Cozart J, Lee H. From motivation to engagement: The role of effort regulation of virtual high school students in mathematics courses. Educ Technol Soc. 2015;18(4):261-72. Forum Pedagog. 2018;8(2):283-86.

https://doi.org/10.21697/fp.2018.2.20

[17] Kuo YC, Walker AE, Schroder KEE, Belland BR. Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. Internet High Educ. 2014; 20:35-50.

https://doi.org/10.1016/j.iheduc.2013.10.001

[18] Peterson S. Self-regulation and online course satisfaction in high school [dissertation]. Dissertation Abstracts International; 2011. 71(10A) (UMI No. 3466080).

[19] Price J. An Analysis of Self-Regulated Learning Strategies, Academic Performance, and Satisfaction among Recent Online High School Graduates; 2017.

https://doi.org/10.25777/vxxn-4j83

[20] Solis O, Tech V, Turner W, Turner W. Building Positive Student-Instructor Interactions: Engaging Students through Caring Leadership in the Classroom. J Empower Teach Excell. 2017;1. https://doi.org/10.15142/T37P72

[21] Edisherashvili N, Saks K, Pedaste M, Leijen Ä. Supporting Self-Regulated Learning in Distance Learning Contexts at Higher Education Level: Systematic Literature Review. Front Psychol. 2022;12. https://doi.org/10.3389/fpsyg.2021.792422

[22] Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000;55(1):68-78. https://doi.org/10.1037/0003-066X.55.1.68

[23] Lo CK, Hew KF. A comparison of flipped learning with gamification, traditional learning, and online independent study: the effects on students’ mathematics achievement and cognitive engagement. Interact Learn Environ. 2018;28(4):1-18.

https://doi.org/10.1080/10494820.2018.1541910

[24] Kizilcec RF, Pérez-Sanagustín M, Maldonado JJ. Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Comput Educ. 2017; 104:18-33.

https://doi.org/10.1016/j.compedu.2016.10.001

[25] Karlen Y, Bäuerlein K, Brunner S. Teachers’ assessment of self-regulated learning: Linking professional competences, assessment practices, and judgment accuracy. Soc Psychol Educ. 2023. https://doi.org/10.1007/s11218-023-09845-4

[26] Rowe FA, Rafferty JA. Instructional design interventions for supporting self-regulated learning: enhancing academic outcomes in postsecondary e-learning environments. J Online Learn Teach. 2013;9(4):590-601.

[27] Nicol D. Assessment for learner self‐regulation: enhancing achievement in the first year using learning technologies. Assessment & Evaluation in Higher Education. 2019;34(3):335–52. https://doi.org/10.1080/02602930802255139

[28] Joo YJ, Joung S, Kim J. Structural relationships among self-regulated learning, learning flow, satisfaction, and learning persistence in cyber universities. Interactive Learning Environments. 2012;22(6):752–70.

https://doi.org/10.1080/10494820.2012.745421

[29] Kuo YC, Walker AE, Belland BR, Schroder KEE. A predictive study of student satisfaction in online education programs. Int Rev Res Open Distrib Learn. 2013;14(1):16.

https://doi.org/10.19173/irrodl.v14i1.1338

[30] Bandura A. Social Cognitive Theory in Cultural Context. Applied Psychology. 2002; 51(2):269–90.

https://doi.org/10.1111/1464-0597.00092

[31] Joo YJ, Bong M, Choi HJ. Self-efficacy for self-regulated learning, academic self-efficacy, and internet self-efficacy in web-based instruction. Educational Technology Research and Development. 2000;48(2):5–17.

https://doi.org/10.1007/bf02313398

[32] Pintrich PR, de Groot EV. Motivational and self-regulated learning components of classroom academic performance. J Educ Psychol. 1990;82(1):33–40.

https://doi.org/10.1037/0022-0663.82.1.33