The Impact of Students' Motivational Drive and Attitude toward Online Learning on Their Academic Engagement during the Emergency Situation

Keywords: ARCS model of motivational factors, academic engagement, attitudes to online learning, self-regulated learning method

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.

Downloads

Download data is not yet available.

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

Published
2025-03-31
How to Cite
YundayaniA., YuniY., & AlghadariF. (2025). The Impact of Students’ Motivational Drive and Attitude toward Online Learning on Their Academic Engagement during the Emergency Situation. Journal of Language and Education, 11(1), 131-147. https://doi.org/10.17323/jle.2025.12439