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Examining the Relationships between Academic Intrinsic Motivation, Online Learning Self-Efficacy, and Online Student Engagement: A Study on Distance Education Students

Year 2025, Volume: 13 Issue: 25, 80 - 105

Abstract

This research explored the relationships between online learning self-efficacy, academic intrinsic motivation, and student engagement in online learning, with particular attention given to the mediating role of academic intrinsic motivation on self-efficacy's influence on engagement. A research model was formulated in alignment with the study's hypotheses. Using a quantitative approach, the study applied both descriptive and relational survey models. The sample comprised 185 associate degree students participating in a distance education program at a state university. Data collection was conducted through a structured questionnaire. The research model and hypotheses were tested using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The findings supported the hypotheses, revealing that online learning self-efficacy positively influenced both academic intrinsic motivation and student engagement. Additionally, it was discovered that online learning self-efficacy indirectly affected engagement, with academic intrinsic motivation serving as a mediator.

Ethical Statement

Ethical Committee Permission Information Name of the board that carries out ethical assessment: Scientific Research and Publication Ethics Committee of Isparta University of Applied Sciences The date and number of the ethical assessment decision: 07.06.2024 -196/03

References

  • Aboobaker, N., & KH, M. (2022). Effectiveness of web-based learning environment: role of intrinsic learning motivation, computer self-efficacy, and learner engagement. Development and Learning in Organizations: An International Journal, 36(4), 13-16. https://doi.org/10.1108/DLO-07-2021-0139
  • Akhtar, S. N., Iqbal, M., & Tatlah, I. A. (2017). Relationship between intrinsic motivation and students' academic achievement: A secondary level evidence. Bulletin of Education and Research, 39(2), 19-29. https://eric.ed.gov/?id=EJ1210159
  • Alan, Ü. (2021). Distance education during the COVID-19 pandemic in Turkey: Identifying the needs of early childhood educators. Early Childhood Education Journal, 49(5), 987-994. https://doi.org/10.1007/s10643-021-01197-y
  • Alemayehu, L., & Chen, H. L. (2023). The influence of motivation on learning engagement: The mediating role of learning self-efficacy and self-monitoring in online learning environments. Interactive Learning Environments, 31(7), 4605-4618.
  • Alesi, M., Giordano, G., Gentile, A., Roccella, M., Costanza, C., & Caci, B. (2024). The mediating role of academic motivation in the relationship between self-efficacy and learning strategies during the COVID-19 pandemic. Frontiers in Education, 8, 1339211. https://doi.org/10.3389/feduc.2023.1339211
  • Aruğaslan, E., Özaydın Özkara, B., & Çivril, H. (2022). Pandemi döneminde ilkokul birinci sınıf öğrenci velilerinin uzaktan eğitime yönelik görüşleri. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 22(4), 1419-1446.
  • Bandura, A., & Adams, N. E. (1977). Analysis of self-efficacy theory of behavioral change. Cognitive Therapy and Research, 1(4), 287-310. https://doi.org/10.1007/BF01663995
  • Bates, R., & Khasawneh, S. (2007). Self-efficacy and college students’ perceptions and use of online learning systems. Computers in Human Behavior, 23(1), 175-191.
  • Bergdahl, N., & Nouri, J. (2021). Covid-19 and crisis-prompted distance education in Sweden. Technology, Knowledge and Learning, 26(3), 443-459.
  • Bolliger, D. U., & Martin, F. (2018). Instructor and student perceptions of online student engagement strategies. Distance Education, 39(4), 568-583.
  • Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17(2), 1-30. https://doi.org/10.1186/s41239-019-0176-8
  • Boyd, F. B. (2002). Motivation to continue: Enhancing literacy learning for struggling readers and writers. Reading & Writing Quarterly, 18(3), 257-277.
  • Casacchia, M., Cifone, M. G., Giusti, L., Fabiani, L., Gatto, R., Lancia, L., ... & Roncone, R. (2021). Distance education during COVID 19: An Italian survey on the university teachers’ perspectives and their emotional conditions. BMC medical education, 21(1), 335. https://doi.org/10.1186/s12909-021-02780-y
  • Chang, C. Y., Chung, M. H., & Yang, J. C. (2022). Facilitating nursing students' skill training in distance education via online game-based learning with the watch-summarize-question approach during the COVID-19 pandemic: A quasi-experimental study. Nurse Education Today, 109, (2022). https://doi.org/10.1016/j.nedt.2021.105256
  • Chau, S., & Cheung, C. (2018). Academic satisfaction with hospitality and tourism education in Macao: The influence of active learning, academic motivation, and student engagement. Asia Pacific Journal of Education, 38(4), 473-487.
  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. G. A. Marcoulides (Ed.), Modern methods for business research (ss. 295–336). Lawrence Erlbaum Associates Publishers.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Earlbaum Assoc.
  • 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.
  • Devkota, K. R. (2021). Inequalities reinforced through online and distance education in the age of COVID-19: The case of higher education in Nepal. International Review of Education, 67(1), 145-165. https://doi.org/10.1007/s11159-021-09886-x
  • Ferrer, J., Ringer, A., Saville, K., A Parris, M., & Kashi, K. (2022). Students’ motivation and engagement in higher education: The importance of attitude to online learning. Higher Education, 83(2), 317-338. https://doi.org/10.1007/s10734-020-00657-5
  • Fırat, M., Kılınç, H., & Yüzer, T. V. (2018). Level of intrinsic motivation of distance education students in e‐learning environments. Journal of Computer Assisted Learning, 34(1), 63-70. https://doi.org/10.1111/jcal.12214
  • Fornell, C. & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Getenet, S., Cantle, R., Redmond, P., & Albion, P. (2024). Students' digital technology attitude, literacy and self-efficacy and their effect on online learning engagement. International Journal of Educational Technology in Higher Education, 21(1), 3.
  • Goodman, S., Jaffer, T., Keresztesi, M., Mamdani, F., Mokgatle, D., Musariri, M., ... & Schlechter, A. (2011). An investigation of the relationship between students' motivation and academic performance as mediated by effort. South African Journal of Psychology, 41(3), 373-385. https://hdl.handle.net/10520/EJC98646
  • Hair, J.F., Hult, G.T.M., Ringle, C. & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM), Sage Publications.
  • Hair, J.F., Ringle, C.M. & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.
  • Hair Jr, J.F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128
  • Hari Rajan, M., Herbert, C., & Polly, P. (2024, January). Disrupted student engagement and motivation: Observations from online and face-to-face university learning environments. Frontiers in Education, 8(1320822).
  • Henseler, J., Hubona, G. & Ray, P. A. (2016), Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. https://doi.org/10.1108/IMDS-09-2015-0382
  • Hong, J. C., Hwang, M. Y., Tai, K. H., & Lin, P. H. (2017). Intrinsic motivation of Chinese learning in predicting online learning self-efficacy and flow experience relevant to students’ learning progress. Computer Assisted Language Learning, 30(6), 552-574. https://doi.org/10.1080/09588221.2017.1329215
  • Horzum, M. B., & Cakir, O. (2009). The validity and reliability study of the Turkish version of the online technologies self-efficacy scale. Educational Sciences: Theory and Practice, 9(3), 1343-1356. https://eric.ed.gov/?id=EJ858927
  • Hu, M., & Li, H. (2017, June). Student engagement in online learning: A review. In 2017 international symposium on educational technology (ISET) (pp. 39-43). IEEE.
  • Hu, M., Li, H., Deng, W., & Guan, H. (2016, September). Student engagement: one of the necessary conditions for online learning. In 2016 International Conference on Educational Innovation through Technology (EITT) (pp. 122-126). IEEE.
  • Kaya, D., Kutluca, T., & Dağhan, G. (2024). Investigation of the relationships between e-learning styles, educational technology self-efficacy perceptions and problem-solving skills of pre-service elementary mathematics teachers'. International e-Journal of Educational Studies, 8 (17), 157-174. https://doi.org/10.31458/iejes.1355282
  • Kaye, A. R. (1989). Distance education. In Titmus, J. T. (Ed.) Lifelong Education for Adults: An international handbook. Paragon Press.
  • Koca, F., Kılıç, S., & Dadandı, İ. (2024). Attitudes towards distance education and academic life satisfaction: The mediation role of academic self-efficacy and moderator role of gender. Technology, Knowledge and Learning, 29(2), 713-734.
  • Li, Y., Yang, H. H., Cai, J., & MacLeod, J. (2017, June). College students’ computer self-efficacy, intrinsic motivation, attitude, and satisfaction in blended learning environments. In Blended Learning. New Challenges and Innovative Practices: 10th International Conference, Hong Kong, China, 27-29, Proceedings 10 (pp. 65-73). Springer International Pub.
  • Lin, S., Longobardi, C., & Bozzato, P. (2022). The impact of academic self-efficacy on academic motivation: The mediating and moderating role of future orientation among Italian undergraduate students. In Academic Self-efficacy in Education: Nature, Assessment, and Research (pp. 191-209). Springer Singapore.
  • Liu, Y., Ma, S., & Chen, Y. (2024). The impacts of learning motivation, emotional engagement and psychological capital on academic performance in a blended learning university course. Frontiers in Psychology, 15, 1357936. https://doi.org/10.3389/fpsyg.2024.1357936 Mamolo, L. A. (2022). Online Learning and Students’ Mathematics Motivation, Self‐Efficacy, and Anxiety in the “New Normal”. Education Research International, 2022(1), 9439634. https://doi.org/10.1155/2022/9439634
  • Martens, R., Gulikers, J., & Bastiaens, T. (2004). The impact of intrinsic motivation on e‐learning in authentic computer tasks. Journal of Computer Assisted Learning, 20(5), 368-376. https://doi.org/10.1111/j.1365-2729.2004.00096.x
  • Meng, X., & Hu, Z. (2022). The relationship between student motivation and academic performance: the mediating role of online learning behavior. Quality Assurance in Education, 31(1), 167-180. https://doi.org/10.1108/QAE-02-2022-0046
  • Nguyen, V. T. T., & Chen, H. L. (2023). Examining impacts of information system success and perceived stress on students’ self-regulated learning mediated by intrinsic motivation in online learning environments: second-order structural equation modelling analyses. Education and Information Technologies, 28(10), 12945-12968.
  • Özaydin Özkara, B., & Ibili, E. (2021). Analysis of students' e-learning styles and their attitudes and self-efficacy perceptions towards distance education. International Journal of Technology in Education and Science, 5(4), 550-570.
  • Özüdoğru, G. (2022). Uzaktan eğitimin çevrimiçi teknolojilere yönelik öz-yeterlilik ve çevrimiçi öğrenme motivasyonu üzerindeki etkisi. Dijital Çağda Öğrenme ve Öğretim Dergisi, 7(1), 108-115. https://doi.org/10.53850/joltida.1003915
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Examining the Relationships between Academic Intrinsic Motivation, Online Learning Self-Efficacy, and Online Student Engagement: A Study on Distance Education Students

Year 2025, Volume: 13 Issue: 25, 80 - 105

Abstract

This research explored the relationships between online learning self-efficacy, academic intrinsic motivation, and student engagement in online learning, with particular attention given to the mediating role of academic intrinsic motivation on self-efficacy's influence on engagement. A research model was formulated in alignment with the study's hypotheses. Using a quantitative approach, the study applied both descriptive and relational survey models. The sample comprised 185 associate degree students participating in a distance education program at a state university. Data collection was conducted through a structured questionnaire. The research model and hypotheses were tested using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The findings supported the hypotheses, revealing that online learning self-efficacy positively influenced both academic intrinsic motivation and student engagement. Additionally, it was discovered that online learning self-efficacy indirectly affected engagement, with academic intrinsic motivation serving as a mediator.

Ethical Statement

Ethical Committee Permission Information Name of the board that carries out ethical assessment: Scientific Research and Publication Ethics Committee of Isparta University of Applied Sciences The date and number of the ethical assessment decision: 07.06.2024 -196/03

References

  • Aboobaker, N., & KH, M. (2022). Effectiveness of web-based learning environment: role of intrinsic learning motivation, computer self-efficacy, and learner engagement. Development and Learning in Organizations: An International Journal, 36(4), 13-16. https://doi.org/10.1108/DLO-07-2021-0139
  • Akhtar, S. N., Iqbal, M., & Tatlah, I. A. (2017). Relationship between intrinsic motivation and students' academic achievement: A secondary level evidence. Bulletin of Education and Research, 39(2), 19-29. https://eric.ed.gov/?id=EJ1210159
  • Alan, Ü. (2021). Distance education during the COVID-19 pandemic in Turkey: Identifying the needs of early childhood educators. Early Childhood Education Journal, 49(5), 987-994. https://doi.org/10.1007/s10643-021-01197-y
  • Alemayehu, L., & Chen, H. L. (2023). The influence of motivation on learning engagement: The mediating role of learning self-efficacy and self-monitoring in online learning environments. Interactive Learning Environments, 31(7), 4605-4618.
  • Alesi, M., Giordano, G., Gentile, A., Roccella, M., Costanza, C., & Caci, B. (2024). The mediating role of academic motivation in the relationship between self-efficacy and learning strategies during the COVID-19 pandemic. Frontiers in Education, 8, 1339211. https://doi.org/10.3389/feduc.2023.1339211
  • Aruğaslan, E., Özaydın Özkara, B., & Çivril, H. (2022). Pandemi döneminde ilkokul birinci sınıf öğrenci velilerinin uzaktan eğitime yönelik görüşleri. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 22(4), 1419-1446.
  • Bandura, A., & Adams, N. E. (1977). Analysis of self-efficacy theory of behavioral change. Cognitive Therapy and Research, 1(4), 287-310. https://doi.org/10.1007/BF01663995
  • Bates, R., & Khasawneh, S. (2007). Self-efficacy and college students’ perceptions and use of online learning systems. Computers in Human Behavior, 23(1), 175-191.
  • Bergdahl, N., & Nouri, J. (2021). Covid-19 and crisis-prompted distance education in Sweden. Technology, Knowledge and Learning, 26(3), 443-459.
  • Bolliger, D. U., & Martin, F. (2018). Instructor and student perceptions of online student engagement strategies. Distance Education, 39(4), 568-583.
  • Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17(2), 1-30. https://doi.org/10.1186/s41239-019-0176-8
  • Boyd, F. B. (2002). Motivation to continue: Enhancing literacy learning for struggling readers and writers. Reading & Writing Quarterly, 18(3), 257-277.
  • Casacchia, M., Cifone, M. G., Giusti, L., Fabiani, L., Gatto, R., Lancia, L., ... & Roncone, R. (2021). Distance education during COVID 19: An Italian survey on the university teachers’ perspectives and their emotional conditions. BMC medical education, 21(1), 335. https://doi.org/10.1186/s12909-021-02780-y
  • Chang, C. Y., Chung, M. H., & Yang, J. C. (2022). Facilitating nursing students' skill training in distance education via online game-based learning with the watch-summarize-question approach during the COVID-19 pandemic: A quasi-experimental study. Nurse Education Today, 109, (2022). https://doi.org/10.1016/j.nedt.2021.105256
  • Chau, S., & Cheung, C. (2018). Academic satisfaction with hospitality and tourism education in Macao: The influence of active learning, academic motivation, and student engagement. Asia Pacific Journal of Education, 38(4), 473-487.
  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. G. A. Marcoulides (Ed.), Modern methods for business research (ss. 295–336). Lawrence Erlbaum Associates Publishers.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Earlbaum Assoc.
  • 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.
  • Devkota, K. R. (2021). Inequalities reinforced through online and distance education in the age of COVID-19: The case of higher education in Nepal. International Review of Education, 67(1), 145-165. https://doi.org/10.1007/s11159-021-09886-x
  • Ferrer, J., Ringer, A., Saville, K., A Parris, M., & Kashi, K. (2022). Students’ motivation and engagement in higher education: The importance of attitude to online learning. Higher Education, 83(2), 317-338. https://doi.org/10.1007/s10734-020-00657-5
  • Fırat, M., Kılınç, H., & Yüzer, T. V. (2018). Level of intrinsic motivation of distance education students in e‐learning environments. Journal of Computer Assisted Learning, 34(1), 63-70. https://doi.org/10.1111/jcal.12214
  • Fornell, C. & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Getenet, S., Cantle, R., Redmond, P., & Albion, P. (2024). Students' digital technology attitude, literacy and self-efficacy and their effect on online learning engagement. International Journal of Educational Technology in Higher Education, 21(1), 3.
  • Goodman, S., Jaffer, T., Keresztesi, M., Mamdani, F., Mokgatle, D., Musariri, M., ... & Schlechter, A. (2011). An investigation of the relationship between students' motivation and academic performance as mediated by effort. South African Journal of Psychology, 41(3), 373-385. https://hdl.handle.net/10520/EJC98646
  • Hair, J.F., Hult, G.T.M., Ringle, C. & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM), Sage Publications.
  • Hair, J.F., Ringle, C.M. & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.
  • Hair Jr, J.F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128
  • Hari Rajan, M., Herbert, C., & Polly, P. (2024, January). Disrupted student engagement and motivation: Observations from online and face-to-face university learning environments. Frontiers in Education, 8(1320822).
  • Henseler, J., Hubona, G. & Ray, P. A. (2016), Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. https://doi.org/10.1108/IMDS-09-2015-0382
  • Hong, J. C., Hwang, M. Y., Tai, K. H., & Lin, P. H. (2017). Intrinsic motivation of Chinese learning in predicting online learning self-efficacy and flow experience relevant to students’ learning progress. Computer Assisted Language Learning, 30(6), 552-574. https://doi.org/10.1080/09588221.2017.1329215
  • Horzum, M. B., & Cakir, O. (2009). The validity and reliability study of the Turkish version of the online technologies self-efficacy scale. Educational Sciences: Theory and Practice, 9(3), 1343-1356. https://eric.ed.gov/?id=EJ858927
  • Hu, M., & Li, H. (2017, June). Student engagement in online learning: A review. In 2017 international symposium on educational technology (ISET) (pp. 39-43). IEEE.
  • Hu, M., Li, H., Deng, W., & Guan, H. (2016, September). Student engagement: one of the necessary conditions for online learning. In 2016 International Conference on Educational Innovation through Technology (EITT) (pp. 122-126). IEEE.
  • Kaya, D., Kutluca, T., & Dağhan, G. (2024). Investigation of the relationships between e-learning styles, educational technology self-efficacy perceptions and problem-solving skills of pre-service elementary mathematics teachers'. International e-Journal of Educational Studies, 8 (17), 157-174. https://doi.org/10.31458/iejes.1355282
  • Kaye, A. R. (1989). Distance education. In Titmus, J. T. (Ed.) Lifelong Education for Adults: An international handbook. Paragon Press.
  • Koca, F., Kılıç, S., & Dadandı, İ. (2024). Attitudes towards distance education and academic life satisfaction: The mediation role of academic self-efficacy and moderator role of gender. Technology, Knowledge and Learning, 29(2), 713-734.
  • Li, Y., Yang, H. H., Cai, J., & MacLeod, J. (2017, June). College students’ computer self-efficacy, intrinsic motivation, attitude, and satisfaction in blended learning environments. In Blended Learning. New Challenges and Innovative Practices: 10th International Conference, Hong Kong, China, 27-29, Proceedings 10 (pp. 65-73). Springer International Pub.
  • Lin, S., Longobardi, C., & Bozzato, P. (2022). The impact of academic self-efficacy on academic motivation: The mediating and moderating role of future orientation among Italian undergraduate students. In Academic Self-efficacy in Education: Nature, Assessment, and Research (pp. 191-209). Springer Singapore.
  • Liu, Y., Ma, S., & Chen, Y. (2024). The impacts of learning motivation, emotional engagement and psychological capital on academic performance in a blended learning university course. Frontiers in Psychology, 15, 1357936. https://doi.org/10.3389/fpsyg.2024.1357936 Mamolo, L. A. (2022). Online Learning and Students’ Mathematics Motivation, Self‐Efficacy, and Anxiety in the “New Normal”. Education Research International, 2022(1), 9439634. https://doi.org/10.1155/2022/9439634
  • Martens, R., Gulikers, J., & Bastiaens, T. (2004). The impact of intrinsic motivation on e‐learning in authentic computer tasks. Journal of Computer Assisted Learning, 20(5), 368-376. https://doi.org/10.1111/j.1365-2729.2004.00096.x
  • Meng, X., & Hu, Z. (2022). The relationship between student motivation and academic performance: the mediating role of online learning behavior. Quality Assurance in Education, 31(1), 167-180. https://doi.org/10.1108/QAE-02-2022-0046
  • Nguyen, V. T. T., & Chen, H. L. (2023). Examining impacts of information system success and perceived stress on students’ self-regulated learning mediated by intrinsic motivation in online learning environments: second-order structural equation modelling analyses. Education and Information Technologies, 28(10), 12945-12968.
  • Özaydin Özkara, B., & Ibili, E. (2021). Analysis of students' e-learning styles and their attitudes and self-efficacy perceptions towards distance education. International Journal of Technology in Education and Science, 5(4), 550-570.
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Details

Primary Language English
Subjects Curriculum and Instration (Other)
Journal Section Research Article
Authors

Emine Aruğaslan 0000-0002-8153-9117

Early Pub Date February 26, 2025
Publication Date
Submission Date September 14, 2024
Acceptance Date February 11, 2025
Published in Issue Year 2025 Volume: 13 Issue: 25

Cite

APA Aruğaslan, E. (2025). Examining the Relationships between Academic Intrinsic Motivation, Online Learning Self-Efficacy, and Online Student Engagement: A Study on Distance Education Students. Journal of Computer and Education Research, 13(25), 80-105.

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