The Impact of Students' Motivational Drive and Attitude toward Online Learning on Their Academic Engagement during the Emergency Situation
- Authors: Yundayani A.1, Yuni J.1, Alghadari F.2
-
Affiliations:
- STKIP Kusuma Negara
- Universitas Jambi
- Issue: Vol 11, No 1 (2025)
- Pages: 131-147
- Section: Research Papers
- URL: https://ogarev-online.ru/2411-7390/article/view/297422
- DOI: https://doi.org/10.17323/jle.2025.12439
- ID: 297422
Cite item
Full Text
Abstract
Background: The advent of emergency remote teaching has significantly transformed the landscape of higher education through the Internet environment. The online learning environment elicits varying student engagement, apathy, and frustration. Nevertheless, digital literacy is not the exclusive factor determining students’ academic participation in online learning during an emergency. Students need an extra compelling element.
Purpose: To investigate students’ motivational urges and attitudes toward emergency online learning scenarios that impact their academic engagement.
Method: An explanatory research design was implemented in the research method to quantify the intensity and direction of the relationship between variables and elucidate the impact of a single variable on another. Two hundred-eight undergraduate students from a private higher education institution comprised the research's respondents. The structural equation modeling and Hayes' bootstrapping technique were employed to analyze the data further, which was collected through an internet-based poll. In addition, the Confirmatory Factor Analysis (CFA) method was employed to assess the reflective measurement models. This included the internal consistency (Cronbach's alpha, composite reliability), the convergent validity encompassed indicator reliability and average variance extracted (AVE), and the discriminant validity conducted using the cross-loadings approach and the Fornell-Larcker criterion.
Results: The research findings suggest that driven students are more inclined to participate in online learning during an emergency remote teaching scenario by actively controlling their study time and autonomously gaining a deeper comprehension of the academic content. Their active participation in online learning is further evidenced by their motivation derived from attention, relevance, confidence, and satisfaction in emergency remote teaching scenarios. The attitude towards online learning (AOL) fostered by these motivational elements had a negligible impact on the student effort. Furthermore, students residing in rural areas exhibit prevailing motivational elements, such as self-assurance and focus, that motivate them to invest time in creating and understanding educational resources. Concurrently, students residing in metropolitan regions exhibit a prevailing driving force in attention and satisfaction, resulting in a favorable disposition towards active academic participation in online learning by fostering the acquisition of time management abilities.
Conclusion: The results have implications for teachers developing teaching activities to encourage active student academic participation in online learning setting, considering the students’ specific needs, backgrounds, characteristics, and abilities.
About the authors
Audi Yundayani
STKIP Kusuma Negara
Email: fikialghadari@unja.ac.id
ORCID iD: 0000-0002-3003-6165
Indonesia, Jakarta
Jakarta Yuni
STKIP Kusuma Negara
Email: fikialghadari@unja.ac.id
ORCID iD: 0000-0003-3634-4019
Indonesia, Jakarta
Fiki Alghadari
Universitas Jambi
Author for correspondence.
Email: fikialghadari@unja.ac.id
ORCID iD: 0000-0003-2079-3952
Indonesia, Jambi
References
- Abidah, A., Hidaayatullaah, H. N., Simamora, R. M., Fehabutar, D., & Mutakinati, L. (2020). The impact of covid-19 to indonesian education and its relation to the philosophy of “merdeka belajar.” Studies in Philosophy of Science and Education, 1(1), 38–49. https://doi.org/10.46627/sipose.v1i1.9
- Adarkwah, M. A. (2021). “I’m not against online teaching, but what about us?”: ICT in Ghana post Covid-19. Education and Information Technologies, 26(2), 1665–1685. https://doi.org/10.1007/s10639-020-10331-z
- Agormedah, E. K., Henaku, E. A., Ayite, D. M. K., & Ansah, E. A. (2020). Online learning in higher education during Covid-19 pandemic: A case of Ghana. Journal of Educational Technology and Online Learning, 3(3), 183–210. https://doi.org/10.31681/jetol.726441
- Aguilera-Hermida, A. P. (2020). College students’ use and acceptance of emergency online learning due to Covid-19. International Journal of Educational Research Open, 1, 100011. https://doi.org/10.1016/j.ijedro.2020.100011
- Al-Hashmi, S. (2021). A study on the impact of the sudden change to online education on the motivation of higher education students. Higher Education Studies, 11(3), 78. https://doi.org/10.5539/hes.v11n3p78
- Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the Student Engagement Instrument. Journal of School Psychology, 44(5), 427–445. https://doi.org/10.1016/j.jsp.2006.04.002
- Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020). Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability, 12(20), 8438. https://doi.org/10.3390/su12208438
- Baranova, T., Kobicheva, A., & Tokareva, E. (2021). Total transition to online learning: Students’ and teachers’ motivation and attitudes. In D. Bylieva, A. Nordmann, O. Shipunova, & V. Volkova (Eds.), Knowledge in the information society. Lecture Notes in Networks and Systems (vol. 184, pp. 301–310). Springer Nature. https://doi.org/10.1007/978-3-030-65857-1_26
- Barden, J., & Tormala, Z. L. (2014). Elaboration and attitude strength: The new meta-cognitive perspective. Social and Personality Psychology Compass, 8(1), 17–29. https://doi.org/10.1111/spc3.12078
- Bhowmik, S., & Dipak Bhattacharya, M. (2021). Factors influencing online learning in higher education in the emergency shifts of Covid 19. The Online Journal of Distance Education and E-Learning, 9(1), 74–83. https://orcid.org/0000-0002-2215-7389
- Budiyanto, S., Jamil, M., & Rahayu, F. (2019). Feasibility analysis of the application of project loon as an equitable effort for communication infrastructure development in Indonesia. InComTech: Jurnal Telekomunikasi Dan Komputer, 9(2), 61. https://doi.org/10.22441/incomtech.v9i2.6469
- Chew, S. L., & Cerbin, W. J. (2021). The cognitive challenges of effective teaching. Journal of Economic Education, 52(1), 17–40. https://doi.org/10.1080/00220485.2020.1845266
- Chukwuedo, S. O., Mbagwu, F. O., & Ogbuanya, T. C. (2021). Motivating academic engagement and lifelong learning among vocational and adult education students via self-direction in learning. Learning and Motivation, 74, 101729. https://doi.org/10.1016/j.lmot.2021.101729
- Chung, E., & Mathew, V. N. (2020). Satisfied with online learning amidst Covid-19, but do you intend to continue using it? International Journal of Academic Research in Progressive Education and Development, 9(4), 67–77. https://doi.org/10.6007/ijarped/v9-i4/8177
- Churiyah, M., Sholikhan, S., Filianti, F., & Sakdiyyah, D. A. (2020). Indonesia education readiness conducting distance learning in Covid-19 pandemic situation. International Journal of Multicultural and Multireligious Understanding, 7(6), 491. https://doi.org/10.18415/ijmmu.v7i6.1833
- Cohen, A. D., & Henry, A. (2019). Focus on the language learner Styles, strategies, and motivation. In An introduction to applied linguistics (pp. 165–189). Routledge.
- Cole, A. W., Lennon, L., & Weber, N. L. (2019). Student perceptions of online active learning practices and online learning climate predict online course engagement. Interactive Learning Environments, 29(5), 866-880. https://doi.org/10.1080/10494820.2019.1619593
- Danesh, J., & Shahnaazari, M. (2020). A structural relationship model for resilience, L2 learning motivation, and L2 proficiency at different proficiency levels. Learning and Motivation, 72, 101636. https://doi.org/10.1016/j.lmot.2020.101636
- Del Valle, R., & Duffy, T. M. (2009). Online learning: Learner characteristics and their approaches to managing learning. Instructional Science, 37(2), 129–149. https://doi.org/10.1007/s11251-007-9039-0
- Dube, B. (2020). Rural online learning in the context of Covid 19 in South Africa: Evoking an inclusive education approach. Multidisciplinary Journal of Educational Research, 10(2), 135–157. https://doi.org/10.17583/remie.2020.5607
- Edmonds, W. A., & Kennedy, T. D. (2020). An applied guide to research designs: Quantitative, qualitative, and mixed methods. In An applied guide to research designs: Quantitative, qualitative, and mixed methods. Sage Publications. https://doi.org/10.4135/9781071802779
- Fatoni, Arifiati, N., Nurkhayati, E., Nurdiawati, E., Fidziah, Pamungkas, G., Adha, S., Irawan, Purwanto, A., Julyanto, O., & Azizi, E. (2020). University students online learning system during Covid-19 pandemic: Advantages, constraints and solutions. Systematic Reviews in Pharmacy, 11(7), 570–576. https://doi.org/10.31838/srp.2020.7.81
- Ferrer, J., Ringer, A., Saville, K., A Parris, M., & Kashi, K. (2020). Students’ motivation and engagement in higher education: The importance of attitude to online learning. Higher Education, 83, 317-388. https://doi.org/10.1007/s10734-020-00657-5
- Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In S. Christenson, A. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 763–782). Springer US. https://doi.org/10.1007/978-1-4614-2018-7_37
- Goksu, I., & Bolat, Y. I. (2020). Does the ARCS motivational model affect students’ achievement and motivation? A meta‐analysis. Review of Education. https://doi.org/10.1002/rev3.3231
- Guay, F. (2022). Applying self-determination theory to education: Regulations Types, psychological needs, and autonomy supporting behaviors. Canadian Journal of School Psychology, 37(1), 75–92. https://doi.org/10.1177/08295735211055355
- Guo, Y., & Chen, L. (2020). An investigation on online learning for K12 in rural areas in China during Covid-19 pandemic. 2020 Ninth International Conference of Educational Innovation through Technology (pp. 13–18). IEEE. https://doi.org/10.1109/EITT50754.2020.00009
- Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling. Sage Publications.
- Hanafi, Y., Taufiq, A., Saefi, M., Ikhsan, M. A., Diyana, T. N., Thoriquttyas, T., & Anam, F. K. (2021). The new identity of Indonesian Islamic boarding schools in the “new normal”: The education leadership response to Covid-19. Heliyon, 7(3). https://doi.org/10.1016/j.heliyon.2021.e06549
- Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers and Education, 90, 36–53. https://doi.org/10.1016/j.compedu.2015.09.005
- Karim, N. S., & Alam, M. (2021). Struggling with digital pandemic: Students’ Narratives about adapting to online learning at home during the Covid-19 outbreak. Southeast Asia: A Multidisciplinary Journal, 21(2), 15–29. https://doi.org/10.1108/seamj-02-2021-b1002
- Keller, J. M. (2010). The ARCS model of motivational design. In J. M. Keller (Ed.), Motivational design for learning and performance: The ARCS model approach (pp. 43–74). Springer. https://doi.org/10.1007/978-1-4419-1250-3
- Kemp, A., Palmer, E., & Strelan, P. (2019). A taxonomy of factors affecting attitudes towards educational technologies for use with technology acceptance models. British Journal of Educational Technology, 50(5), 2394–2413. https://doi.org/10.1111/bjet.12833
- Kemp, N. (2020). University students’ perceived effort and learning in face-to-face and online classes. Journal of Applied Learning & Teaching, 3(1), 69–77. https://doi.org/10.37074/jalt.2020.3.s1.14
- Lei, J., & Lin, T. (2022). Emergency online learning: The effects of interactional, motivational, self-regulatory, and situational factors on learning outcomes and continuation intentions. The International Review of Research in Open and Distributed Learning, 23(3), 43–60. https://doi.org/10.19173/irrodl.v23i3.6078
- Li, K., & Keller, J. M. (2018). Use of the ARCS model in education: A literature review. Computers & Education, 122, 54–62. https://doi.org/10.1016/j.compedu.2018.03.019
- Liew, J., Xiang, P., Johnson, A. Y., & Kwok, O. M. (2011). Effortful persistence and body mass as predictors of running achievement in children and youth: A longitudinal study. Journal of Physical Activity and Health, 8(2), 234–243. https://doi.org/10.1123/jpah.8.2.234
- Loyd, B. H., & Gressard, C. (1984). Reliability and factorial validity of CAS. Journal of Educational and Psychological Measurement, 44(2), 501–505.
- Lu, H. (2020). Online learning: The meanings of student engagement. Education Journal, 9(3), 73–79. https://doi.org/10.11648/j.edu.20200903.13
- Luschei, T. F., & Zubaidah, I. (2012). Teacher training and transitions in rural Indonesian schools: A case study of Bogor, West Java. Asia Pacific Journal of Education, 32(3), 333–350. https://doi.org/10.1080/02188791.2012.711241
- Mhlanga, D., & Moloi, T. (2020). Covid-19 and the digital transformation of education: What are we learning on 4IR in South Africa? Education Sciences, 10(7), 180. https://doi.org/10.3390/educsci10070180
- Nistor, N. (2013). Stability of attitudes and participation in online university courses: Gender and location effects. Computers and Education, 68, 284–292. https://doi.org/10.1016/j.compedu.2013.05.016
- OlOzdemir, T. Y. (2018). Investigation of students‘ commitment to schools in terms of some variables. Üniversitepark Bülten, 7(1), 51–65. https://doi.org/10.22521/unibulletin.2018.71.5
- 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, 1–11. https://doi.org/10.3389/fpsyg.2020.564294
- Pires, E. M. S. G., Daniel-Filho, D. A., de Nooijer, J., & Dolmans, D. H. J. M. (2020). Collaborative learning: Elements encouraging and hindering deep approach to learning and use of elaboration strategies. Medical Teacher, 42(11), 1261–1269. https://doi.org/10.1080/0142159X.2020.1801996
- Reeve, J., & Tseng, C. M. (2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36(4), 257–267. https://doi.org/10.1016/j.cedpsych.2011.05.002
- Roman, M., & Plopeanu, A.-P. (2021). The effectiveness of the emergency eLearning during Covid-19 pandemic. The case of higher education in economics in Romania. International Review of Economics Education, 37, 100218. https://doi.org/10.1016/j.iree.2021.100218
- Romero, J. C. G., Villa, E. G., Frías, N. S. C., & Hernández, P. E. (2020). Ambiente de aprendizaje positivo, compromiso académico y aprendizaje autorregulado en bachilleres. Acta Colombiana de Psicología, 23(2), 267–288. https://doi.org/10.14718/acp.2020.23.2.11
- Rusli, R., Rahman, A., & Abdullah, H. (2020). Student perception data on online learning using heutagogy approach in the Faculty of Mathematics and Natural Sciences of Universitas Negeri Makassar, Indonesia. Data in Brief, 29, 105152. https://doi.org/10.1016/j.dib.2020.105152
- Shin, M., & Bolkan, S. (2021). Intellectually stimulating students’ intrinsic motivation: the mediating influence of student engagement, self-efficacy, and student academic support. Communication Education, 70(2), 146–164. https://doi.org/10.1080/03634523.2020.1828959
- Stewart, W. H., & Lowenthal, P. R. (2022). Distance education under duress: a case study of exchange students’ experience with online learning during the Covid-19 pandemic in the Republic of Korea. Journal of Research on Technology in Education, 54(S1), S273–S287. https://doi.org/10.1080/15391523.2021.1891996
- Strunk, K. K., Cho, Y. J., Steele, M. R., & Bridges, S. L. (2013). Development and validation of a 2×2 model of time-related academic behavior: Procrastination and timely engagement. Learning and Individual Differences, 25, 35–44. https://doi.org/10.1016/j.lindif.2013.02.007
- Thornhill-Miller, B., Camarda, A., Mercier, M., Burkhardt, J. M., Morisseau, T., Bourgeois-Bougrine, S., Vinchon, F., El Hayek, S., Augereau-Landais, M., Mourey, F., Feybesse, C., Sundquist, D., & Lubart, T. (2023). Creativity, critical thinking, communication, and collaboration: assessment, certification, and promotion of 21st century skills for the future of work and education. Journal of Intelligence, 11(3). https://doi.org/10.3390/jintelligence11030054
- Valantinaitė, I., & Sederevičiūtė-Pačiauskienė, Ž. (2020). The change in students’ attitude towards favourable and unfavourable factors of online learning environments. Sustainability, 12(19), 1–14. https://doi.org/10.3390/su12197960
- van Eerde, W. (2015). Time Management and Procrastination. In M. D. Mumford & M. Frese (Eds.), The psychology of planning in organizations: research and applications (pp. 312–333). Routledge.
- Vanan, C. K., & Subramani, R. (2015). Digital divide: rural and urban college students ‘attitude towards technology acceptance. International Journal of Communication and Media Studies, 5(4), 1–8.
- Wang, C., Zhao, H., & Zhang, H. (2020). Chinese college students have higher anxiety in new semester of online learning during COVID-19: A machine learning approach. Frontiers in Psychology, 11, 3465. https://doi.org/10.3389/fpsyg.2020.587413
- Wang, J., & Jou, M. (2023). The influence of mobile-learning flipped classrooms on the emotional learning and cognitive flexibility of students of different levels of learning achievement. Interactive Learning Environments, 31(3), 1309–1321. https://doi.org/10.1080/10494820.2020.1830806
- Wang, Q., Lee, K. C. S., & Hoque, K. E. (2020). The effect of classroom climate on academic motivation mediated by academic self-efficacy in a higher education institute in China. International Journal of Learning, Teaching and Educational Research, 19(8), 194–213. https://doi.org/10.26803/ijlter.19.8.11
- Wijaya, T. T., Ying, Z., Purnama, A., & Hermita, N. (2020). Indonesian students’ learning attitude towards online learning during the coronavirus pandemic. Psychology, Evaluation, and Technology in Educational Research, 3(1), 17–25. https://doi.org/10.33292/petier.v3i1.56
- Wolters, C. A., & Brady, A. C. (2020). College students’ time management: A self-regulated learning perspective. Educational Psychology Review, 1–33. https://doi.org/10.1007/s10648-020-09519-z
- Wolters, C. A., Pintrich, P. R., & Karabenick, S. A. (2005). Assessing academic self-regulated learning. In K. A. Moore & L. H. Lippman (Eds.), What do children need to flourish?: Conceptualizing and measuring indicators of positive development (pp. 251–270). Springer. https://doi.org/https://doi.org/10.1007/0-387-23823-9_16
- Yundayani, A., Kardijan, D., & Apriliani, R. D. (2020). The impact of pbworks application on vocational students’ collaborative writing skill. Cakrawala Pendidikan, 39(3), 694–704. https://doi.org/10.21831/cp.v39i3.25077
- Zhang, X., Ji, Z., Zheng, Y., Ye, X., & Li, D. (2020). Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models. Cities, 107, 102869. https://doi.org/10.1016/j.cities.2020.102869
- Zimmerman, B. J. (2013). From cognitive modeling to self-regulation: A social cognitive career path. Educational Psychologist, 48(3), 135–147. https://doi.org/10.1080/00461520.2013.794676
- Zimmerman, B. J., & Kitsantas, A. (2014). Comparing students’ self-discipline and self-regulation measures and their prediction of academic achievement. Contemporary Educational Psychology, 39(2), 145–155. https://doi.org/10.1016/j.cedpsych.2014.03.004
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