The effect of motivation and learning discipline on student learning outcomes in online-based Buddhist religious education subjects

Mujiyanto Mujiyanto* -  STIAB Smaratungga, Indonesia, Indonesia

DOI : 10.30863/ekspose.v21i1.3402

This study aims to determine the effect of learning motivation and learning discipline on student learning outcomes in the subject of Buddhist education using E-Learning learning. This study uses a quantitative approach with the ex post facto method, the nature of the ex post facto is that there is no control over the variables. The sample of this study consisted of junior high school Buddhist religious education students in Semarang district. The data in this study were obtained through non-test techniques in the form of questionnaires and also documentation techniques in the form of student learning outcomes in Buddhist subjects. The data in this study were analyzed using inferential statistical analysis techniques, namely multiple regression analysis. The results of this study indicate that there is a significant influence between students' motivation and learning discipline simultaneously on student learning outcomes in e-learning-based Buddhist subjects (0.00 <0.05); and the magnitude of the joint contribution between motivation and student learning discipline on student learning outcomes in e-learning-based Buddhist subjects is 75%.
Learning; Motivation Learning; Discipline Learning; Outcomes e-Learning
  1. Addae, D. (2020). Learning behind bars: Motivations and challenges of learners in a correctional facility in Ghana. International Journal of Educational Research, 104, 101650.
  2. Beymer, P. N., & Robinson, K. A. (2022). Motivating by measuring motivation? Examining reactivity in a diary study on student motivation. Contemporary Educational Psychology, 70, 102072.
  3. Caputo, A. (2015). The motivational patterns of school learning: A correlational study on self-concept and attributions. Journal of Educational, Cultural and Psychological Studies, 2015(12), 143–167.
  4. Daumiller, M., Janke, S., Hein, J., Rinas, R., Dickhäuser, O., & Dresel, M. (2021). Do teachers’ achievement goals and self-efficacy beliefs matter for students’ learning experiences? Evidence from two studies on perceived teaching quality and emotional experiences. Teacher Motivation: Implications for Instruction and Learning, 76, 101458.
  5. Franco, E., Coterón, J., Gómez, V., & Spray, C. M. (2021). A person-centred approach to understanding dark-side antecedents and students’ outcomes associated with physical education teachers’ motivation. Psychology of Sport and Exercise, 57, 102021.
  6. Janssens, O., Haerens, L., Valcke, M., Beeckman, D., Pype, P., & Embo, M. (2022). The role of ePortfolios in supporting learning in eight healthcare disciplines: A scoping review. Nurse Education in Practice, 63, 103418.
  7. Javadizadeh, B., Aplin-Houtz, M., & Casile, M. (2022). Using SCARF as a motivational tool to enhance students′ class performance. The International Journal of Management Education, 20(1), 100594.
  8. Kartianom, K, & Retnawati, H. (2018). Why Are Their Mathematical Learning Achievements Different? Re-Analysis Timss 2015 Data in Indonesia, Japan And Turkey. International Journal on New Trends in Education & Their Implications (IJONTE), 9(2).
  9. Kartianom, Kartianom, & Ndayizeye, O. (2017). What‘s wrong with the Asian and African Students’ mathematics learning achievement? The multilevel PISA 2015 data analysis for Indonesia, Japan, and Algeria. Jurnal Riset Pendidikan Matematika, 4(2), 200–210.
  10. Kowitlawakul, Y., Tan, J. J. M., Suebnukarn, S., Nguyen, H. D., Poo, D. C. C., Chai, J., … Devi, K. (2022). Utilizing educational technology in enhancing undergraduate nursing students’ engagement and motivation: A scoping review. Journal of Professional Nursing, 42, 262–275.
  11. Kryshko, O., Fleischer, J., Waldeyer, J., Wirth, J., & Leutner, D. (2020). Do motivational regulation strategies contribute to university students’ academic success? Learning and Individual Differences, 82, 101912.
  12. Law, K. M. Y., Geng, S., & Li, T. (2019). Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Computers & Education, 136, 1–12.
  13. Little, T. D. (2013). The Oxford handbook of quantitative methods in psychology: Vol. 2: statistical analysis (Vol. 2). Oxford University Press.
  14. Liu, Y., Hau, K.-T., & Zheng, X. (2018). Does instrumental motivation help students with low intrinsic motivation? Comparison between Western and Confucian students. International Journal of Psychology : Journal International de Psychologie.
  15. Singh, M., James, P. S., Paul, H., & Bolar, K. (2022). Impact of cognitive-behavioral motivation on student engagement. Heliyon, 8(7), e09843.
  16. Strelan, P., Osborn, A., & Palmer, E. (2020). The flipped classroom: A meta-analysis of effects on student performance across disciplines and education levels. Educational Research Review, 30, 100314.
  17. Teig, N., & Nilsen, T. (2022). Profiles of instructional quality in primary and secondary education: Patterns, predictors, and relations to student achievement and motivation in science. Studies in Educational Evaluation, 74, 101170.
  18. Valle, N., Antonenko, P., Valle, D., Dawson, K., Huggins-Manley, A. C., & Baiser, B. (2021). The influence of task-value scaffolding in a predictive learning analytics dashboard on learners’ statistics anxiety, motivation, and performance. Computers & Education, 173, 104288.
  19. Vettori, G., Vezzani, C., Bigozzi, L., & Pinto, G. (2020). Cluster profiles of university students’ conceptions of learning according to gender, educational level, and academic disciplines. Learning and Motivation, 70, 101628.
  20. Wilson, A., Madjar, I., & McNaughton, S. (2016). Opportunity to learn about disciplinary literacy in senior secondary English classrooms in New Zealand. The Curriculum Journal. Retrieved from
  21. Woltman, H., Feldstain, A., MacKay, C., & Rocchi, M. (2012). An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology, 8(1), 52–69.
  22. Zamecnik, A., Kovanović, V., Joksimović, S., & Liu, L. (2022). Exploring non-traditional learner motivations and characteristics in online learning: A learner profile study. Computers and Education: Artificial Intelligence, 3, 100051.

Full Text:
Article Info
Submitted: 2022-12-12
Published: 2022-12-12
Section: Articles
Article Statistics: 208 135