Skip to main content
SpringerLink
Log in

A two-staged SEM: artificial neural network approach for understanding and predicting the factors of students’ satisfaction with emergency remote teaching

  • Cultural and Regional Perspectives
  • Published:
Educational technology research and development Aims and scope Submit manuscript

Abstract

This study seeks to address knowledge gaps regarding the role of self-regulated learning as a mediator in the relationship between interactions, internet self-efficacy, and student satisfaction. We conducted a survey of 1590 students from north Indian universities about their level of satisfaction, self-regulated learning, internet self-efficacy, and different interactions (learner-learner interaction, learner-content interaction, and learner-instructor interaction) during emergency remote teaching. By employing a two-stage SEM-ANN approach, this study contributes to methodological advancements and provides a comprehensive analysis of complex relationships. According to the findings, the identified factors are significant predictors of students’ satisfaction with online education in synchronous settings. Our research also shows that self-regulated learning fully mediates the effect of internet self-efficacy on student satisfaction during emergency remote teaching. This suggests that internet self-efficacy alone may not guarantee student satisfaction unless accompanied by self-regulated learning skills.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Adekannbi, J. O., & Ipadeola, O. (2023). Predictors of satisfaction with emergency remote teaching (ERT) during Covid 19 among undergraduate students of Nigeria’s premier university. SN Social Sciences, 3(3), 51. https://doi.org/10.1007/s43545-023-00638-2

    Article  Google Scholar 

  • Agler, R., & de Boeck, P. (2017). On the interpretation and use of mediation: Multiple perspectives on mediation analysis. Frontiers in Psychology, 8, 1–11. https://doi.org/10.3389/fpsyg.2017.01984

    Article  Google Scholar 

  • Ahoto, A. T., Mbaye, M. B., Anyigbah, E., Ahoto, A. T., Mbaye, M. B., & Anyigbah, E. (2022). The impacts of learner-instructor interaction, learner-learner, learner-content interaction, internet self-efficacy and self-regulated learning on satisfaction of online education of African medical students. Open Access Library Journal, 9(9), 1–16. https://doi.org/10.4236/OALIB.1109202

    Article  Google Scholar 

  • Albahri, A. S., Alnoor, A., Zaidan, A. A., Albahri, O. S., Hameed, H., Zaidan, B. B., Peh, S. S., Zain, A. B., Siraj, S. B., Masnan, A. H. B., & Yass, A. A. (2022). Hybrid artificial neural network and structural equation modelling techniques: A survey. Complex & Intelligent Systems, 8(2), 1781–1801. https://doi.org/10.1007/S40747-021-00503-W

    Article  Google Scholar 

  • Aldhahi, M. I., Alqahtani, A. S., Baattaiah, B. A., & Al-Mohammed, H. I. (2021). Exploring the relationship between students’ learning satisfaction and self-efficacy during the emergency transition to remote learning amid the coronavirus pandemic: A cross-sectional study. Education and Information Technologies. https://doi.org/10.1007/s10639-021-10644-7

    Article  Google Scholar 

  • Ali, S., & Mirza, M. S. (2020). Relationship between various forms of interaction and students’ satisfaction in online learning: Case of an open university of Pakistan. Pakistan Journal of Distance and Online Learning, 6(2), 1–17.

    Google Scholar 

  • Almusharraf, N. M., & Khahro, S. H. (2020). Students’ satisfaction with online learning experiences during the COVID-19 pandemic. International Journal of Emerging Technologies in Learning, 15(21), 246–267. https://doi.org/10.3991/ijet.v15i21.15647

    Article  Google Scholar 

  • Anderson, T. D., & Garrison, D. R. (1998). Learning in a networked world: New roles and responsibilities. Distance learners in higher education (pp. 97–112). Atwood Publishing.

    Google Scholar 

  • Arbaugh, J. B. (2000). How classroom environment and student engagement affect learning in internet-based MBA courses. Business Communication Quarterly, 63(4), 9–26. https://doi.org/10.1177/108056990006300402

    Article  Google Scholar 

  • Artino, A. R., Jr. (2007). Online military training: Using a social cognitive view of motivation and self-regulation to understand students’ satisfaction, perceived learning, and choice. Quarterly Review of Distance Education, 8(3), 191–202.

    Google Scholar 

  • Bailey, D. (2022). Interactivity during Covid-19: Mediation of learner interactions on social presence and expected learning outcome within videoconference EFL courses. Journal of Computers in Education, 9(2), 291–313. https://doi.org/10.1007/s40692-021-00204-w

    Article  Google Scholar 

  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191

    Article  Google Scholar 

  • Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122.

  • Barzegar, M. (2012). The relationship between goal orientation and academic achievement—the mediation role of self-regulated learning strategies-A path analysis. Online Submission. http://search.ebscohost.com/login.aspx?direct=true&db=eric&AN=ED537835&site=ehost-live&scope=site

  • Bell, P. D. (2006). Can factors related to self-regulated learning and epistemological beliefs predict learning achievement in undergraduate asynchronous Web-based courses? Perspectives in Health Information Management/AHIMA, American Health Information Management Association, 3.

  • Bhatt, G. D. (2001). Knowledge management in organizations: Examining the interaction between technologies, techniques, and people. Journal of Knowledge Management, 5(1), 68–75. https://doi.org/10.1108/13673270110384419/FULL/PDF

    Article  Google Scholar 

  • Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach’s alpha. BMJ, 314(7080), 572. https://doi.org/10.1136/bmj.314.7080.572

    Article  Google Scholar 

  • Bozkurt, A., & Sharma, R. (2020). Emergency remote teaching in a time of global crisis due to corona virus pandemic. Asian Journal of Distance Education, 15(1), 1–6. https://doi.org/10.5281/ZENODO.3778083

    Article  Google Scholar 

  • Bray, E., Aoki, K., & Dlugosh, L. (2008). Predictors of learning satisfaction in japanese online distance learners. The International Review of Research in Open and Distributed Learning. https://doi.org/10.19173/IRRODL.V9I3.525

    Article  Google Scholar 

  • Brownlee, J. (2021). How to choose an activation function for deep learning. Machine Learning Mastery. https://machinelearningmastery.com/choose-an-activation-function-for-deep-learning/

  • Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education, 46(23).

  • Castellanos-Reyes, D. (2020). 20 years of the community of inquiry framework. TechTrends, 64(4), 557–560. https://doi.org/10.1007/S11528-020-00491-7/FIGURES/1

    Article  Google Scholar 

  • Chang, C. S., Liu, E. Z. F., Sung, H. Y., Lin, C. H., Chen, N. S., & Cheng, S. S. (2014). Effects of online college student’s Internet self-efficacy on learning motivation and performance. Innovations in Education and Teaching International, 51(4), 366–377. https://doi.org/10.1080/14703297.2013.771429

    Article  Google Scholar 

  • Chejlyk, S. (2006). The effects of online course format and three components of student perceived interactions on overall course satisfaction. (Doctoral dissertation, Capella University). ProQuest Dissertations and Theses.

  • Chen, M., & Wu, X. (2021). Attributing academic success to giftedness and its impact on academic achievement: The mediating role of self-regulated learning and negative learning emotions. School Psychology International, 42(2), 170–186. https://doi.org/10.1177/0143034320985889

    Article  Google Scholar 

  • Chen, T., Cong, G., Peng, L., Yin, X., Rong, J., & Yang, J. (2020). Analysis of user satisfaction with online education platforms in China during the COVID-19 pandemic. Healthcare. https://doi.org/10.3390/healthcare8030200

    Article  Google Scholar 

  • Chong, A. Y. L. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240–1247. https://doi.org/10.1016/j.eswa.2012.08.067

    Article  Google Scholar 

  • Chu, R. J., & Chu, A. Z. (2010). Multi-level analysis of peer support, Internet self-efficacy and e-learning outcomes—the contextual effects of collectivism and group potency. Computers and Education, 55(1), 145–154. https://doi.org/10.1016/j.compedu.2009.12.011

    Article  Google Scholar 

  • Chuah, S. H. W., El-Manstrly, D., Tseng, M. L., & Ramayah, T. (2020). Sustaining customer engagement behavior through corporate social responsibility: The roles of environmental concern and green trust. Journal of Cleaner Production, 262, 1–15. https://doi.org/10.1016/J.JCLEPRO.2020.121348

    Article  Google Scholar 

  • Clark, R. C., Mayer, R. E. (2016). E-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. Germany: Wiley.

  • Coman, C., Țîru, L. G., Meseșan-Schmitz, L., Stanciu, C., & Bularca, M. C. (2020). Online teaching and learning in higher education during the coronavirus pandemic: Students’ perspective. Sustainability (switzerland), 12(24), 1–22. https://doi.org/10.3390/su122410367

    Article  Google Scholar 

  • Danks, N. P., & Ray, S. (2018). Predictions from partial least squares models. Applying Partial Least Squares in Tourism and Hospitality Research. https://doi.org/10.1108/978-1-78756-699-620181003

    Article  Google Scholar 

  • Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019). Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences of the United States of America, 116(39), 19251–19257. https://doi.org/10.1073/pnas.1821936116

    Article  Google Scholar 

  • Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018

    Article  Google Scholar 

  • Doyumğaç, İ, Tanhan, A., & Kiymaz, M. S. (2020). Understanding the most important facilitators and barriers for online education during COVID-19 through online photovoice methodology. International Journal of Higher Education, 10(1), 166. https://doi.org/10.5430/ijhe.v10n1p166

    Article  Google Scholar 

  • Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer-Mediated Communication. https://doi.org/10.1111/J.1083-6101.2000.TB00110.X

    Article  Google Scholar 

  • Eom, S. B., Wen, H. J., & Ashill, N. (2006). The determinants of students' perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215–235.

  • Erragcha, N., & Babay, H. (2023). Perceived quality and satisfaction with e-Learning during COVID-19: Moderating role of co-production. International Journal of Information and Education Technology, 13(1), 64–72. https://doi.org/10.18178/ijiet.2023.13.1.1781

    Article  Google Scholar 

  • Fein, A. D., & Logan, M. C. (2003). Preparing instructors for online instruction. New Directions for Adult and Continuing Education, 2003(100), 45–55. https://doi.org/10.1002/ace.118

    Article  Google Scholar 

  • Ferri, F., Grifoni, P., & Guzzo, T. (2020). Online learning and emergency remote teaching: Opportunities and challenges in emergency situations. Societies, 10(4), 86. https://doi.org/10.3390/soc10040086

    Article  Google Scholar 

  • Fogerson, D. L. (2006). Readiness factors contributing to participant satisfaction in online higher education courses. Doctoral dissertation, University of Tennessee, Knoxville. https://trace.tennessee.edu/utk_graddiss/1952/

  • Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192–3198. https://doi.org/10.1016/j.jbusres.2015.12.008

    Article  Google Scholar 

  • Garrison, D. R. (2011). E-Learning in the 21st century: A framework for research and practice (2nd ed.). Routledge. https://doi.org/10.4324/9780203838761

    Book  Google Scholar 

  • George, D., & Mallery, P. (2010). SPSS for Windows step by step. A simple study guide and reference (10. Baskı). Boston: Pearson Education Inc.

    Google Scholar 

  • Gomezelj, D., & Čivre, Ž. (2012). Tourism graduate students’ satisfaction with online learning. Tourism: an International Interdisciplinary Journal, 60(2), 159–174.

    Google Scholar 

  • Gunzler, D., Chen, T., Wu, P., & Zhang, H. (2013). Introduction to mediation analysis with structural equation modeling. Shanghai Archives of Psychiatry, 25(6), 390–394. https://doi.org/10.3969/j.issn.1002-0829.2013.06.009

    Article  Google Scholar 

  • Guo, K. (2016). Empirical study on factors of student satisfaction in higher education. Revista Iberica De Sistemas e Tecnologias De Informacao, 2016(E11), 344–355.

    Google Scholar 

  • Hadi, N. U., & Abdullah, N. (2016). Making sense of mediating analysis: A marketing perspective. Review of Integrative Business and Economics Research, 5(2), 62–76.

    Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: In Sage.

    Google Scholar 

  • 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. https://doi.org/10.2753/MTP1069-6679190202

    Article  Google Scholar 

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001

    Article  Google Scholar 

  • Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

    Article  Google Scholar 

  • Hamdan, K. M., AI-Bashaireh, A. M., Zahran, Z., AI-Daghestani, A., AL-Habashneh, S., & Shaheen, A. M. (2021). University students’ interaction, internet self-efficacy, self-regulation and satisfaction with online education during pandemic crises of COVID-19 (SARS-CoV-2). International Journal of Educational Management, 35(3), 713–725. https://doi.org/10.1108/IJEM-11-2020-0513

    Article  Google Scholar 

  • Haron, H., Mustafa, S. M., & Alias, R. A. (2010). Gender influences on emotional self-regulation among Malaysian academicians. International Journal of Innovation, Management and Technology, 1(1), 2010.

    Google Scholar 

  • Heinemann, M. H. (2007). Teacher− student interaction and learning in online theological education. Part Four: Findings and conclusions. Christian Higher Education, 6(3), 185–206.

  • Hodges, C. B., Moore, S., Lockee, B. B., Trust, T., & Bond, M. A. (2020). The difference between emergency remote teaching and online learning.

  • Hundleby, J. D., & Nunnally, J. (1968). Psychometric theory. American Educational Research Journal, 5(3), 431–433. https://doi.org/10.2307/1161962

    Article  Google Scholar 

  • Iglesias-Pradas, S., Hernández-García, Á., Chaparro-Peláez, J., & Prieto, J. L. (2021). Emergency remote teaching and students’ academic performance in higher education during the COVID-19 pandemic: A case study. Computers in Human Behavior, 119, 106713. https://doi.org/10.1016/j.chb.2021.106713

    Article  Google Scholar 

  • Jansen, R. S., van Leeuwen, A., Janssen, J., Jak, S., & Kester, L. (2019). Self-regulated learning partially mediates the effect of self-regulated learning interventions on achievement in higher education: A meta-analysis. Educational Research Review, 28, 1–20. https://doi.org/10.1016/J.EDUREV.2019.100292

    Article  Google Scholar 

  • Joo, Y.-J., Bong, M., & Choi, H.-J. (2000). Self-efficacy for self-regulated learning, academic self-efficacy, and internet self-efficacy in web-based instruction. Educationa Journal of Research and Development, 48(2), 5–17. https://doi.org/10.1007/BF02313398

    Article  Google Scholar 

  • Jordan, P. J., & Troth, A. C. (2020). Common method bias in applied settings: The dilemma of researching in organizations. Australian Journal of Management, 45(1), 3–14. https://doi.org/10.1177/0312896219871976

    Article  Google Scholar 

  • Jung, I., Choi, S., Lim, C., & Leem, J. (2002). Effects of different types of interaction on learning achievement, satisfaction and participation in web-based instruction. Innovations in Education and Teaching International, 39(2), 153–162. https://doi.org/10.1080/14703290252934603

    Article  Google Scholar 

  • Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., Barman, B., Das, P., & Chouhan, P. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal. India. Children and Youth Services Review, 116, 105194. https://doi.org/10.1016/j.childyouth.2020.105194

    Article  Google Scholar 

  • Kaur, N., Dwivedi, D., Arora, J., & Gandhi, A. (2020). Study of the effectiveness of e-learning to conventional teaching in medical undergraduates amid COVID-19 pandemic. National Journal of Physiology, Pharmacy and Pharmacology, 10(7), 563–567. https://doi.org/10.5455/NJPPP.2020.10.04096202028042020

    Article  Google Scholar 

  • Keeler, L. C. (2006). Student satisfaction and types of interaction in distance education courses. Doctoral dissertation, Colorado State University. ProQuest Dissertations and Theses.

  • Khan, M. A., Kamal, T., Illiyan, A., & Asif, M. (2021). School students’ perception and challenges towards online classes during covid-19 pandemic in India: An econometric analysis. Sustainability, 13(9), 1–15. https://doi.org/10.3390/su13094786

    Article  Google Scholar 

  • Koh, K. (2014). Univariate normal distribution. Encyclopedia of quality of life and well-being research (pp. 6817–6819). Dordrecht: Springer. https://doi.org/10.1007/978-94-007-0753-5_3109

    Chapter  Google Scholar 

  • Kovačević, I., Anđelković Labrović, J., Petrović, N., & Kužet, I. (2021). Recognizing predictors of students’ emergency remote online learning satisfaction during COVID-19. Education Sciences, 11(11), 1–16. https://doi.org/10.3390/educsci11110693

    Article  Google Scholar 

  • Kuo, Y. C. (2010). Interaction, internet self-efficacy, and self-regulated learning as interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in distance education courses predictors of student satisfaction in distance education [All Graduate Theses and Dissertations, Spring 1920 to Summer 2023, Utah State University]. https://digitalcommons.usu.edu/etd/741

  • Kuo, Y. C. (2013). A Case Study of Learner Interaction in Web-based Learning. In: T. Bastiaens & G. Marks (Eds), Proceedings of E-Learn 2013–World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 363–369). Las Vegas, NV, USA: Association for the Advancement of Computing in Education (AACE). Retrieved November 14, 2023, from, https://www.learntechlib.org/primary/p/114864/.

  • Kuo, Y. C., Walker, A. E., Belland, B. R., & Schroder, K. E. E. (2013). A predictive study of student satisfaction in online education programs. International Review of Research in Open and Distance Learning, 14(1), 16–39. https://doi.org/10.19173/irrodl.v14i1.1338

    Article  Google Scholar 

  • Kuo, Y. C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. Internet and Higher Education, 20, 35–50. https://doi.org/10.1016/j.iheduc.2013.10.001

    Article  Google Scholar 

  • Lee, J. W., & Mendlinger, S. (2011). Perceived self-efficacy and its effect on online learning acceptance and student satisfaction. Journal of Service Science and Management, 04(03), 243–252. https://doi.org/10.4236/JSSM.2011.43029

    Article  Google Scholar 

  • Lei, M., & Lomax, R. G. (2005). The effect of varying degrees of nonnormality in structural equation modeling. Structural Equation Modeling, 12(1), 1–27. https://doi.org/10.1207/s15328007sem1201_1

    Article  Google Scholar 

  • Leong, L. Y., Hew, T. S., Ooi, K. B., & Chong, A. Y. L. (2020). Predicting the antecedents of trust in social commerce—a hybrid structural equation modeling with neural network approach. Journal of Business Research, 110, 24–40. https://doi.org/10.1016/j.jbusres.2019.11.056

    Article  Google Scholar 

  • Leong, L. Y., Hew, T. S., Tan, G. W. H., & Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604–5620. https://doi.org/10.1016/j.eswa.2013.04.018

    Article  Google Scholar 

  • Liébana-Cabanillas, F., Marinkovic, V., Ramos de Luna, I., & Kalinic, Z. (2018). Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change, 129, 117–130. https://doi.org/10.1016/j.techfore.2017.12.015

    Article  Google Scholar 

  • Lim, C. K. (2001). Computer self–efficacy, academic self–concept, and other predictors of satisfaction and future participation of adult distance learners. International Journal of Phytoremediation, 21(1), 41–51. https://doi.org/10.1080/08923640109527083

    Article  Google Scholar 

  • Lin, L., & Johnson, T. (2021). Shifting to digital: informing the rapid development, deployment, and future of teaching and learning. Educational Technology Research and Development. https://doi.org/10.1007/s11423-021-09960-z

    Article  Google Scholar 

  • Lin, W. S., & Wang, C. H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers and Education, 58(1), 88–99. https://doi.org/10.1016/J.COMPEDU.2011.07.008

    Article  Google Scholar 

  • Liu, X., He, W., Zhao, L., & Hong, J. C. (2021). Gender differences in self-regulated online learning during the COVID-19 lockdown. Frontiers in Psychology, 12, 1–8. https://doi.org/10.3389/fpsyg.2021.752131

    Article  Google Scholar 

  • Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Transactions on Professional Communication, 57(2), 123–146. https://doi.org/10.1109/TPC.2014.2312452

    Article  Google Scholar 

  • Maciaszczyk, M., Depta, A., Kocot, M., Kwasek, A., & Kocot, D. (2021). Assessment and effectiveness of e-learning and students’ satisfaction with online classes: The example of Polish universities. European Research Studies Journal. https://doi.org/10.35808/ersj/2457

    Article  Google Scholar 

  • MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614. https://doi.org/10.1146/annurev.psych.58.110405.085542

    Article  Google Scholar 

  • Maltby, J. R., Whittle, J. (2000). Learning programming online : student perceptions and performance. Proceedings of ASCILITE 2000 conference.

  • Maqableh, M., & Alia, M. (2021). Evaluation online learning of undergraduate students under lockdown amidst COVID-19 Pandemic: The online learning experience and students’ satisfaction. Children and Youth Services Review, 128, 1–11. https://doi.org/10.1016/j.childyouth.2021.106160

    Article  Google Scholar 

  • Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1–7. https://doi.org/10.1080/08923648909526659

    Article  Google Scholar 

  • Muilenburg, L. Y., & Berge, Z. L. (2005). Students barriers to online learning: A factor analytic study. Distance Education, 26(1), 29–48. https://doi.org/10.1080/01587910500081269

    Article  Google Scholar 

  • Muthuprasad, T., Aiswarya, S., Aditya, K. S., & Jha, G. K. (2021). Students’ perception and preference for online education in India during COVID-19 pandemic. Social Sciences & Humanities Open, 3(1), 1–11. https://doi.org/10.1016/j.ssaho.2020.100101

    Article  Google Scholar 

  • Narimani, M., Zamani, B. E., & Asemi, A. (2015). Qualified instructors, students’ satisfaction and electronic education. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 6(3), 31–39.

    Google Scholar 

  • Natarajan, J., & Joseph, M. A. (2022). Impact of emergency remote teaching on nursing students’ engagement, social presence, and satisfaction during the COVID-19 pandemic. Nursing Forum, 57(1), 42–48. https://doi.org/10.1111/nuf.12649

    Article  Google Scholar 

  • Obermeier, R., Gläser-Zikuda, M., Bedenlier, S., Kammerl, R., Kopp, B., Ziegler, A., & Händel, M. (2022). Stress development during emergency remote teaching in higher education. Learning and Individual Differences, 98, 1–10. https://doi.org/10.1016/j.lindif.2022.102178

    Article  Google Scholar 

  • Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated learning. Educational Psychologist, 36(2), 89–101. https://doi.org/10.1207/S15326985EP3602_4

    Article  Google Scholar 

  • Peng, H., Tsai, C.-C., & Wu, Y.-T. (2006). University students’ self efficacy and their attitudes toward the internet: The role of students’ perceptions of the internet. Educational Studies, 32(1), 73–86. https://doi.org/10.1080/03055690500416025

    Article  Google Scholar 

  • Phan, T. T. N., & Dang, L. T. T. (2017). Teacher readiness for online teaching: A critical review. International Journal on Open and Distance e-Learning, 3(1), 1–16.

    Google Scholar 

  • Pike, G. R. (1993). The relationship between perceived learning and satisfaction with college: An alternative view. Research in Higher Education, 34(1), 23–40.

    Article  Google Scholar 

  • Potyrała, K., Demeshkant, N., Czerwiec, K., Jancarz-Łanczkowska, B., & Tomczyk, Ł. (2021). Head teachers’ opinions on the future of school education conditioned by emergency remote teaching. Education and Information Technologies, 26(6), 7451–7475.

  • Price, L. (2006). Gender differences and similarities in online courses: Challenging stereotypical views of women. Journal of Computer Assisted Learning, 22(5), 349–359. https://doi.org/10.1111/J.1365-2729.2006.00181.X

    Article  Google Scholar 

  • Puzziferro, M. (2008). Online technologies self-efficacy and self-regulated learning as predictors of final grade and satisfaction in college-level online courses. International Journal of Phytoremediation, 21(1), 72–89. https://doi.org/10.1080/08923640802039024

    Article  Google Scholar 

  • Quispe, M. D. C. A., & Alecchi, B. A. (2021). Business school student satisfaction with emergency remote teaching. Journal of Education and E-Learning Research, 8(4), 375–384.

    Article  Google Scholar 

  • Ramayah, T., Hwa, C. J., Chuah, F., & Ting, H. (2018). PLS-SEM using SmartPLS 3.0: Chapter 13: Assessment of Moderation Analysis Effectiveness of a Structured Group-Based Educational Program MEDIHEALTH in Improving Medication Adherence among Malay Patients with Underlying Type 2 Diabetes Mellitus in the State. 2nd edition. Pearson. https://www.researchgate.net/publication/341357609

  • Raykov, T. (2007). Reliability if deleted, not ‘alpha if deleted’: Evaluation of scale reliability following component deletion. British Journal of Mathematical and Statistical Psychology, 60(2), 201–216. https://doi.org/10.1348/000711006X115954

    Article  Google Scholar 

  • Reinhart, J., Reinhart, J., & Schneider, P. (2001). Student satisfaction, self-efficacy, and the perception of the two-way audio/video learning environment: A preliminary examination. Quarterly Review of Distance Education, 2(4), 357–365.

    Google Scholar 

  • Robles, F. M. R. (2006). Learner characteristic, interaction and support service variables as predictors of satisfaction in web-based distance education. Dissertation, New Mexico: The University of New Mexico.

  • Rodriguez-Rivero, R., Lantada, A. D., Ballesteros-Sanchez, L., & Juan, J. (2021). The impact of emergency remote teaching on students’ stress and satisfaction in project-based learning experiences. International Journal of Engineering Education, 37(6), 1594–1604.

    Google Scholar 

  • Sangwan, A., Sangwan, A., & Punia, P. (2021). Development and validation of an attitude scale towards online teaching and learning for higher education teachers. TechTrends, 65, 187–195. https://doi.org/10.1007/s11528-020-00561-w

    Article  Google Scholar 

  • Schunk, D. H. (2010). Self-regulated learning: The educational legacy of Paul R. Pintrich. Educational Psychologist, 40(2), 85–94. https://doi.org/10.1207/S15326985EP4002_3

    Article  Google Scholar 

  • Shin, M., & Hickey, K. (2021). Needs a little TLC: Examining college students’ emergency remote teaching and learning experiences during COVID-19. Journal of Further and Higher Education, 45(7), 973–986. https://doi.org/10.1080/0309877X.2020.1847261

    Article  Google Scholar 

  • Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310. https://doi.org/10.1214/10-STS330

    Article  Google Scholar 

  • Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189

    Article  Google Scholar 

  • Stern, C., & Garson, G. D. (1999). Review of neural networks. An introductory guide for social scientists. Contemporary Sociology, 28(6), 753–753.

    Article  Google Scholar 

  • Sternad Zabukovšek, S., Kalinić, Z., Bobek, S., & Tominc, P. (2019). SEM-ANN based research of factors’ impact on extended use of ERP systems. Central European Journal of Operations Research, 27, 703–735. https://doi.org/10.1007/s10100-018-0592-1

    Article  Google Scholar 

  • Su, F., Zou, D., Wang, L., & Kohnke, L. (2023). Student engagement and teaching presence in blended learning and emergency remote teaching. Journal of Computers in Education. https://doi.org/10.1007/s40692-023-00263-1

    Article  Google Scholar 

  • Subramanian, A., Timberlake, M., Mittakanti, H., Lara, M., & Brandt, M. L. (2012). Novel educational approach for medical students: Improved retention rates using interactive medical software compared with traditional lecture-based format. Journal of Surgical Education, 69(4), 449–452. https://doi.org/10.1016/J.JSURG.2012.05.013

    Article  Google Scholar 

  • The problem of multicollinearity. (2007). Understanding regression analysis (pp. 176–180). Springer. https://doi.org/10.1007/978-0-585-25657-3_37

    Book  Google Scholar 

  • Thompson, L. F., Meriac, J. P., & Cope, J. G. (2002). Motivating online performance: The influences of goal setting and internet self-efficacy. Social Science Computer Review, 20(2), 149–160. https://doi.org/10.1177/089443930202000205

    Article  Google Scholar 

  • Turley, C., Turley, C., & Graham, C. (2019). Interaction, student satisfaction, and teacher time investment in online high school courses. Journal of Online Learning Research, 5(2), 169–198.

    Google Scholar 

  • Walker, S. L., & Fraser, B. J. (2005). Development and validation of an instrument for assessing distance learning environments in higher education: The distance education learning environments survey (DELES). Learning Environments Research, 8(3), 289–308. https://doi.org/10.1007/S10984-005-1568-3

    Article  Google Scholar 

  • Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323. https://doi.org/10.1080/01587919.2013.835779

    Article  Google Scholar 

  • Weiser, E. B. (2000). Gender differences in Internet use patterns and Internet application preferences: A two-sample comparison. Cyberpsychology and Behavior, 3(2), 167–178. https://doi.org/10.1089/109493100316012

    Article  Google Scholar 

  • Weller, M. (2007). Virtual learning environments: Using, choosing and developing your VLE. Virtual learning environments: Using, choosing and developing your VLE. Routledge Taylor & Francis Group. https://doi.org/10.4324/9780203964347

    Chapter  Google Scholar 

  • Wertz, R. E. H. (2022). Learning presence within the community of inquiry framework: An alternative measurement survey for a four-factor model. Internet and Higher Education, 52, 1–15. https://doi.org/10.1016/J.IHEDUC.2021.100832

    Article  Google Scholar 

  • Wilhelm, J., Mattingly, S., & Gonzalez, V. H. (2022). Perceptions, satisfactions, and performance of undergraduate students during COVID-19 emergency remote teaching. Anatomical Sciences Education, 15(1), 42–56. https://doi.org/10.1002/ase.2161

    Article  Google Scholar 

  • Wong, L., & Fong, M. (2014). Student attitudes to traditional and online methods of delivery. Journal of Information Technology Education: Research, 13(1), 1–3. https://doi.org/10.28945/1943

    Article  Google Scholar 

  • Wu, A. Y., Tsai, C., Journal, S., & Wu, Y. (2007). International forum of educational technology & society developing an information commitment survey for assessing students ‘ web information searching strategies and evaluative standards for web materials. International Forum of Educational Technology & Society Developing, 10(2), 120–132.

    Google Scholar 

  • Xiao, J. (2017). Learner-content interaction in distance education: The weakest link in interaction research. Distance Education, 38(1), 123–135. https://doi.org/10.1080/01587919.2017.1298982

    Article  Google Scholar 

  • Yavuzalp, N., & Bahcivan, E. (2021). A structural equation modeling analysis of relationships among university students’ readiness for e-learning, self-regulation skills, satisfaction, and academic achievement. Research and Practice in Technology Enhanced Learning, 16(1), 1–17. https://doi.org/10.1186/s41039-021-00162-y

    Article  Google Scholar 

  • Yekefallah, L., Namdar, P., Panahi, R., & Dehghankar, L. (2021). Factors related to students’ satisfaction with holding e-learning during the COVID-19 pandemic based on the dimensions of e-learning. Heliyon, 7(7), e07628. https://doi.org/10.1016/j.heliyon.2021.e07628

    Article  Google Scholar 

  • Yukselturk, E., & Bulut, S. (2005). Relationships among self-regulated learning components, motivational beliefs and computer programming achievement in an online learning environment. Mediterranean Journal of Educational Studies, 10(1), 91–112.

  • Zabukovšek, S., Bobek, S., Zabukovšek, U., Kalinić, Z., & Tominc, P. (2022). Enhancing PLS-SEM-enabled research with ANN and IPMA: Research study of enterprise resource planning (ERP) systems’ acceptance based on the technology acceptance model (TAM). Mathematics, 10(9), 1–28. https://doi.org/10.3390/math10091379

    Article  Google Scholar 

  • Zhang, W., Wang, Y., Yang, L., & Wang, C. (2020). Suspending classes without stopping learning: china’s education emergency management policy in the COVID-19 outbreak. Journal of Risk and Financial Management, 13(3), 55. https://doi.org/10.3390/JRFM13030055

    Article  Google Scholar 

  • Zimmerman, B. J., & Schunk, D. H. (2013). Self-regulated learning and academic achievement: Theoretical perspectives. Taylor & Francis.

    Book  Google Scholar 

Download references

Acknowledgements

All study participants are greatly appreciated and thanked for their collaboration with the research team, which enabled this study to be performed.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, GJUS & T, Hisar, India

    Anupma Sangwan & Anju Sangwan

  2. UGC-Human Resource Development Centre, GJUS & T, Hisar, India

    Anurag Sangwan

  3. BPS Women University, Khanpur Kalan, Sonepat, India

    Poonam Punia

Authors
  1. Anupma Sangwan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anurag Sangwan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anju Sangwan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Poonam Punia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Poonam Punia.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not report on any studies with animals performed by any of the authors. However, ethical approval from the participants was taken at the time of filling out the questionnaire as a Google form.The research was carried out in conformity with the ethical principles outlined in the 1964 Declaration of Helsinki and its subsequent revisions or equivalent ethical norms.

Informed consent

Informed consent has been obtained at the time of filling out the questionnaire as a Google form.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 18 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sangwan, A., Sangwan, A., Sangwan, A. et al. A two-staged SEM: artificial neural network approach for understanding and predicting the factors of students’ satisfaction with emergency remote teaching. Education Tech Research Dev (2024). https://doi.org/10.1007/s11423-023-10335-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11423-023-10335-9

Keywords

Search

Navigation