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University students’ online learning attitudes and continuous intention to undertake online courses: a self-regulated learning perspective

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Abstract

Studies have been conducted on university students’ continuous intention to learn online from the perspectives of learning motivation and capability, perceptions or attitudes, and online learning experiences. However, few have examined how the above factors will relate to each other and contribute to students’ online learning intention. This research explored 94 university students’ online learning attitudes and experiences in a blended course. The researchers investigated the changes in the participants’ attitudes toward online learning and the relationships between their self-regulated learning capability, online interactions, attitudes, and online learning intention. These students participated in a pre- and post-survey at the beginning and end of the course. They also completed six weekly reports commenting on their learning activities of the week. At the end of the course, interviews were administered to eight participants to gather detailed information about their online learning experiences. It was found that (a) the participants’ online learning attitudes were generally positive and increased when completing the course; and (b) the participants’ continuous intention to learn online was significantly predicted by four self-regulatory factors and attitudes, mediated through perceived online social interactions. The analysis of the interviewees’ further comments provided more insights about the potential factors contributing to their online learning attitude changes. The strategies for future online course design with a view of improving students’ self-regulated learning skills are discussed in this paper.

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References

  • Adams, J. S. (1965). Inequity in social exchange. In L. Berkowitz (Ed.), Advances in experimental social psychology (pp. 267–299). New York: Academic Press.

    Chapter  Google Scholar 

  • Adams Becker, S., Cummins, M., Davis, A., Freeman, A., Hall Giesinger, C., & Ananthanarayanan, V. (2017). NMC Horizon Report: 2017 Higher (Education ed.). Austin, Texas: The New Media Consortium.

    Google Scholar 

  • Adams Becker, S., Brown, M., Dahlstrom, E., Davis, A., DePaul, K., Diaz, V., et al. (2018). NMC Horizon Report: 2018 Higher Education edition. Louisville, CO: EDUCAUSE.

    Google Scholar 

  • Adewole-Odeshi, E. (2014). Attitude of students towards E-learning in South-West Nigerian Universities: An application of technology acceptance model. Library Philosophy and Practice (e-journal), 1035.

  • Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action Control: From cognition to behavior (pp. 11–39). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes,50, 179–211.

    Article  Google Scholar 

  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Al-Busaidi, K. A. (2013). An empirical investigation linking learners’ adoption of blended learning to their intention of full e-learning. Behaviour & Information Technology,32(11), 1168–1176. https://doi.org/10.1080/0144929X.2013.774047.

    Article  Google Scholar 

  • Alexander, B., Ashford-Rowe, K., Barajas-Murphy, N., Dobbin, G., Knott, J., McCormack, M., et al. (2019). EDUCAUSE Horizon Report: 2019 Higher Education edition. Louisville, CO: EDUCAUSE.

    Google Scholar 

  • Alhamami, M. (2018). Beliefs about and intention to learn a foreign language in face-to-face and online settings. Computer Assisted Language Learning,31(1–2), 90–113. https://doi.org/10.1080/09588221.2017.1387154.

    Article  Google Scholar 

  • Aljukhadar, M., Senecal, S., & Nantel, J. (2014). Is more always better? Investigating the task-technology fit theory in an online user context. Information & Management,51(4), 391–397. https://doi.org/10.1016/j.im.2013.10.003.

    Article  Google Scholar 

  • Asterhan, C. S. C., & Schwarz, B. B. (2010). Online moderation of synchronous e-argumentation. Computer-Supported Collaborative Learning,5(3), 259–282. https://doi.org/10.1007/s11412-010-9088-2.

    Article  Google Scholar 

  • Atkinson, J. W. (1964). An introduction to motivation. Princeton, NJ: Van Nostrand.

    Google Scholar 

  • Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? the role of self-regulated learning. Educational Psychologist,40(4), 199–209. https://doi.org/10.1207/s15326985ep4004_2.

    Article  Google Scholar 

  • Bai, J., & Ng, S. (2005). Tests for Skewness, Kurtosis, and normality for times series data. Journal of Business & Economic Statistics American Statistical Association,23(1), 49–60.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Barnard, L., Paton, V., & Lan, W. (2008). Online self-regulatory learning behaviours as a mediator in the relationship between online course perceptions with achievement. International Review of Research in Open and Distance Learning, 9(2), 1–11. Retrieved from: https://www.learntechlib.org/p/49216/

  • Basak, E., & Calisir, F. (2015). An empirical study on factors affecting continuance intention of using Facebook. Computers in Human Behavior,48, 181–189. https://doi.org/10.1016/j.chb.2015.01.055.

    Article  Google Scholar 

  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly,25(3), 351–370. https://doi.org/10.2307/3250921.

    Article  Google Scholar 

  • Bhattacherjee, A., & Sanford, C. (2006). Influence processed for information technology acceptance: An elaboration likelihood model. MIS Quarterly,30(4), 805–825. https://doi.org/10.2307/25148755.

    Article  Google Scholar 

  • Bilde, J. D., Vansteenkiste, M., & Lens, W. (2011). Understanding the association between future time perspective and self-regulated learning through the lens of self-determination theory. Learning & Instruction,21(3), 332–344. https://doi.org/10.1016/j.learninstruc.2010.03.002.

    Article  Google Scholar 

  • Billings, D. M., Connors, H. R., & Skiba, D. J. (2001). Benchmarking best practices in Web-based nursing courses. Advances in Nursing Science,23(3), 41–52.

    Article  Google Scholar 

  • Brahmasrene, T., & Lee, J.-W. (2012). Determinants of intent to continue using online learning: A Tale of Two Universities. Interdisciplinary Journal of Information, Knowledge, and Management,7, 1–20.

    Article  Google Scholar 

  • Bremer, C. (2012). New format for online courses: The open course future of learning. Tagungsband zur eLearning Baltics eLBa. Retrieved from: https://core.ac.uk/reader/18325863

  • Brown, L. V. (2007). Psychology of motivation. New York: Nova Science Publishers.

    Google Scholar 

  • Çakıroğlu, Ü., & Öztürk, M. (2017). Flipped classroom with problem based activities: Exploring self-regulated learning in a programming language course. Educational Technology & Society,20(1), 337–349.

    Google Scholar 

  • Calvet Liñán, L., & Juan Pérez, Á. A. (2015). Educational data mining and learning analytics: Differences, similarities, and time evolution RUSC. Universities and Knowledge Society Journal,12(3), 98–112. https://doi.org/10.7238/rusc.v12i3.2515.

    Article  Google Scholar 

  • Chambers, S. M., & Clarke, V. A. (1987). Is inequality cumulative? The relationship between disadvantaged group membership and students’ computing experience, knowledge, attitudes and intentions. Journal of Educational Computing Research,3(4), 495–518. https://doi.org/10.2190/U4R4-DW4J-DLAA-0A50.

    Article  Google Scholar 

  • Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education,111, 128–143. https://doi.org/10.1016/j.compedu.2017.04.010.

    Article  Google Scholar 

  • Chen, Y.-C., Lin, Y.-C., Yeh, R. C., & Lou, S.-J. (2013). Examining factor affecting college students’ intention to use web-based instruction systems: Toward an integrated model. The Turkish Online Journal of Educational Technology,12(2), 111–121.

    Google Scholar 

  • Chen, B., Fan, Y.-Z., Zhang, G.-G., & Wang, Q. (2017, March). Examining motivation and self-regulated learning strategies of returning MOOCs learning. The Seventh International Learning Analytics & Knowledge Conference, Vancouver, BC, Canada.

  • Cheng, Y.-M. (2014a). What drives nurses’ blended e-learning continuance intention? Journal of Educational Technology & Society,17(4), 203–215.

    Google Scholar 

  • Cheng, Y.-M. (2014b). Extending the expectation-confirmation model with quality and flow to explore nurses’ continued blended e-learning intention. Information Technology & People,27(3), 230–258.

    Article  Google Scholar 

  • Chiu, C.-M., Sun, S.-Y., Sun, P.-C., & Ju, T. L. (2007). An empirical analysis of the antecedents of web-based learning continuance. Computers & Education,49(4), 1224–1245. https://doi.org/10.1016/j.compedu.2006.01.010.

    Article  Google Scholar 

  • Chou, C. H., Wang, Y. S., & Tang, T. I. (2015). Exploring the determinants of knowledge adoption in virtual communities: A social influence perspective. International Journal of Information Management,35(3), 364–376. https://doi.org/10.1016/j.ijinfomgt.2015.02.001.

    Article  Google Scholar 

  • Cole, A. W., & Timmerman, C. E. (2015). What do current college students think about MOOCs? MERLOT Journal of Online Learning and Teaching,11, 188–201.

    Google Scholar 

  • Conrad, R. M., & Donaldson, J. A. (2004). Engaging the online learner: Activities and resources for creative instruction. San Francisco, CA: John Wiley & Sons.

    Google Scholar 

  • Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco: Jossey-Bass.

    Google Scholar 

  • Csikszentmihalyi, M. (2014). Applications of flow in human development and education: The collected work of Mihaly Csikszentmihalyi. Dordrecht: Springer.

    Book  Google Scholar 

  • Dağhan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2016.02.066.

    Article  Google Scholar 

  • Daumiller, M., & Dresel, M. (2018). Supporting self-regulated learning with digital media using motivational regulation and metacognitive prompts. The Journal of Experimental Education. https://doi.org/10.1080/00220973.2018.1448744.

    Article  Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly,13, 319–340.

    Article  Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science,35(8), 982–1003.

    Article  Google Scholar 

  • de Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Ambrose, M., Dunwell, I., et al. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology,46(6), 1175–1188. https://doi.org/10.1111/bjet.12212.

    Article  Google Scholar 

  • DeLone, W. H., & Mclean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Systems.,19(4), 9–30.

    Google Scholar 

  • Dumford, A. D., & Miller, A. L. (2018). Online learning in higher education: Exploring advantages and disadvantages for engagement. Journal of Computing in Higher Education,6, 1–14. https://doi.org/10.1007/s12528-018-9179-z.

    Article  Google Scholar 

  • Eccles, J. S. (2010). Gender roles and women’s achievement-related decisions. Psychology of Women Quarterly,11(2), 135–172. https://doi.org/10.1111/j.1471-6402.1987.tb00781.x.

    Article  Google Scholar 

  • Elliot, A. J., & Covington, M. V. (2001). Approach and avoidance motivation. Educational Psychology Review,13(2), 73–92. https://doi.org/10.1023/A:100900901.

    Article  Google Scholar 

  • Erdem Aydin, I., & Gumus, S. (2016). Sense of classroom community and team development process in online learning. Turkish Online Journal of Distance Education,17(1), 60–77.

    Google Scholar 

  • Fizilcec, R. F., Perez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behaviour and goal attainment in Massive Open Online Courses. Computers & Education,104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001.

    Article  Google Scholar 

  • Gil-Jaurena, I., & Domínguez, D. (2018). Teachers’ roles in light of massive open online courses (MOOCs): evolution and challenges in higher distance education. International Review of Education,64(2), 197–219. https://doi.org/10.1007/s11159-018-9715-0.

    Article  Google Scholar 

  • Glogowska, M., Yound, P., Lockyer, L., & Moule, P. (2011). How ‘blended’ is blended learning?: Students’ perceptions of issues around the integration of online and face-to-face learning in a continuing professional development (CPD) health care context. Nurse Education Today,31, 887–891. https://doi.org/10.1016/j.nedt.2011.02.003.

    Article  Google Scholar 

  • Gould, D., Papadopoulos, I., & Kelly, D. (2014). Tutors’ opinions of suitability of online learning programmes in continuing professional development for midwives. Nurse Education Today,34(4), 613–618. https://doi.org/10.1016/j.nedt.2013.06.006.

    Article  Google Scholar 

  • Guo, Z., Xiao, L., Van Toorn, C., Lai, Y., & Seo, C. (2016). Promoting online learners’ continuance intention: An integrated flow framework. Information & Management.,53, 279–295. https://doi.org/10.1016/j.im.2015.10.010.

    Article  Google Scholar 

  • Hashim, K. F., Tan, F. B., & Rashid, A. (2014). Adult learners' intention to adopt mobile learning: A motivational perspective. British Journal of Educational Technology. https://doi.org/10.1111/bjet.12148.

    Article  Google Scholar 

  • Hillman, D. C., Willis, D. J., & Gunawardena, C. N. (1994). Learner interface interaction in distance education: An extension of contemporary models and strategies for practitioners. American Journal of Distance Education,8(2), 30–42.

    Article  Google Scholar 

  • Holmberg, B. (1983). Guided didactic conversation in distance education. In D. Sewart, D. Keegan, & B. Holmber (Eds.), Distance education: International perspectives (pp. 114–122). London: Routledge.

    Google Scholar 

  • Hood, M. (2013). Bricks or clicks? Predicting student intentions in a blended learning buffet. Australian Journal of Educational Technology,29(6), 762–776.

    Google Scholar 

  • Hood, N., Littlejohn, A., & Milligan, C. (2015). Context counts: How learners’ contexts influence learning in a MOOC. Computers & Education,91, 83–91. https://doi.org/10.14742/ajet.415.

    Article  Google Scholar 

  • Huang, L.-Q., Zhang, J., & Liu, Y. (2017). Antecedents of student MOOC revisit intention: Moderation effect of course difficulty. International Journal of Information Management,37, 84–91. https://doi.org/10.1016/j.ijinfomgt.2016.12.002.

    Article  Google Scholar 

  • Ifinedo, P. (2017). Examining students' intention to continue using blogs for learning: Perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior,72, 189–199. https://doi.org/10.1016/j.chb.2016.12.049.

    Article  Google Scholar 

  • Ifinedo, P. (2018). Determinants of students’ continuance intention to use blogs to learn: An empirical investigation. Behaviour & Information Technology,37(4), 381–392. https://doi.org/10.1080/0144929X.2018.1436594.

    Article  Google Scholar 

  • Ji, Z., Yang, Z., Liu, J., & Yu, C. (2019). Investigating users’ continued usage intentions of online learning applications. Information,10(6), 1–13.

    Article  Google Scholar 

  • Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC horizon report: 2016 higher education edition (pp. 1–50). Austin: The New Media Consortium.

    Google Scholar 

  • Jones, T., & Clarke, V. A. (1994). A computer attitude scale for secondary students. Computer and Education,22(4), 315–318. https://doi.org/10.1016/0360-1315(94)90053-1.

    Article  Google Scholar 

  • Jones, S. R., Torres, V., & Arminio, J. (2014). Issues in analysis and interpretation. In S. R. Jones, V. Torres, & J. Arminio (Eds.), Negotiating the complexities of qualitative research in higher education: Fundamental elements and issues (pp. 157–173). New York: Routledge.

    Google Scholar 

  • Joo, Y. J., So, H. J., & Kim, N. H. (2018). Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCS. Computers & Education,122, 260–272. https://doi.org/10.1016/j.compedu.2018.01.003.

    Article  Google Scholar 

  • Jülicher, T. (2018). Education 2.0: Learning analytics, educational data mining and Co. In T. Hoeren & B. Kolany-Raiser (Eds.), Big Data in Context: Legal, social and technological insight (pp. 47–53). Cham: Springer Open.

    Chapter  Google Scholar 

  • Kenny, A. (2002). Online learning: Enhancing nurse education? Journal of Advanced Nursing,38(2), 127–135. https://doi.org/10.1046/j.1365-2648.2002.02156.x.

    Article  Google Scholar 

  • Kim, Y. H., & Ahn, J.-H. (2016). A study on the application of big data to the Korean college education system. Information Technology and Quantitative Management,91, 855–861. https://doi.org/10.1016/j.procs.2016.07.096.

    Article  Google Scholar 

  • Kim, M. K., Kim, S. M., Khera, O., & Getman, J. (2014). The Experience of three flipped classrooms in an urban university: an exploration of design principles. The Internet and Higher Education,22, 37–50. https://doi.org/10.1016/j.iheduc.2014.04.003.

    Article  Google Scholar 

  • Kim, S., Park, C., & O"Rourke, J. (2017). Effectiveness of online simulation training: Measuring faculty knowledge, perceptions, and intention to adopt. Nurse Education Today,51, 102–107. https://doi.org/10.1016/j.nedt.2016.12.022.

    Article  Google Scholar 

  • Knowles, E., & Kerkman, D. (2007). An investigation of students’ attitude and motivation toward online learning. Student Motivation,2, 70–80.

    Google Scholar 

  • Krueger, R. A., & Casey, M. A. (2009). Developing a questioning route. In R. A. Krueger & M. A., Casey (Eds.), Focus groups: A practical guide for applied research (pp. 35–60). Thousand Oaks, CA: Sage.

  • Lan, W. Y., Bremer, R., Stevens, T., & Mullen, G. (2004). Self-regulated learning in the on-line environment. San Diego, CA: Paper presented at the annual meeting American Educational Research Association.

    Google Scholar 

  • Lee, M. C. (2010). Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education,54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002.

    Article  Google Scholar 

  • Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education,53, 1320–1329. https://doi.org/10.1016/j.compedu.2009.06.014.

    Article  Google Scholar 

  • Li, Y., Duan, Y., Fu, Z., & Alford, P. (2012). An empirical study on behavioral intention to reuse e-Learning systems in rural China. British Journal of Educational Technology,43, 933–948.

    Article  Google Scholar 

  • Liao, C., Palvia, P., & Chen, J.-L. (2009). Information technology adoption behaviour life cycle: Toward a Technology Continuance Theory (TCT). International Journal of Information Management,29(4), 309–320. https://doi.org/10.1016/j.ijinfomgt.2009.03.004.

    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. Computer & Education,58, 88–99. https://doi.org/10.1016/j.compedu.2011.07.008.

    Article  Google Scholar 

  • Lin, K. M., Chen, N. S., & Fang, K. T. (2010). Understanding e-learning continuance intention: A negative critical incidents perspective. Behaviour & Information Technology,30(1), 77–89. https://doi.org/10.1080/01449291003752948.

    Article  Google Scholar 

  • Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs: Motivations and self-regulated learning in MOOCs. The Internet and Higher Education,29, 40–48. https://doi.org/10.1016/j.iheduc.2015.12.003.

    Article  Google Scholar 

  • Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C.-H. (2010a). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computer & Education,54(2), 600–661.

    Article  Google Scholar 

  • Liu, Y., Han, S., & Li, H. (2010b). Understanding the factors driving m-learning adoption: A literature review. Campus-Wide Information Systems,27(4), 210–226. https://doi.org/10.1108/10650741011073761.

    Article  Google Scholar 

  • Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008–2012. The International Review of Research in Open and Distance Learning,14(3), 202–227.

    Article  Google Scholar 

  • Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior,47, 90–97. https://doi.org/10.1016/j.chb.2014.07.013.

    Article  Google Scholar 

  • López-Pérez, M. V., Pérez-López, M. C., & Rodríguez-Ariza, L. (2011). Blended learning in higher education: Students’ perceptions and their relation to outcomes. Computers & Education,56(3), 818–826. https://doi.org/10.1016/j.compedu.2010.10.023.

    Article  Google Scholar 

  • Lu, O. H. T., Huang, J. C. H., Huang, A. Y. Q., & Yang, S. J. H. (2017). Applying learning analytics for improving students engagement and learning outcomes in a MOOCs enabled collaborative programming course. Interactive Learning Environments. https://doi.org/10.1080/10494820.2016.1278391.

    Article  Google Scholar 

  • McCombs, B. L. (1986). The role of the self-system in self-regulated learning. Contemporary Educational Psychology,11, 314–332. https://doi.org/10.1016/0361-476X(86)90028-7.

    Article  Google Scholar 

  • McLaughlin, J. E., & Rhoney, D. H. (2015). Comparison of an interactive e-learning preparatory tool and a conventional downloadable handout used within a flipped neurologic pharmacotherapy lecture. Currents in Pharmacy Teaching and Learning,7(1), 12–19. https://doi.org/10.1016/j.cptl.2014.09.016.

    Article  Google Scholar 

  • Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of tam and is success model. Computers in Human Behavior,45, 359–374. https://doi.org/10.1016/j.chb.2014.07.044.

    Article  Google Scholar 

  • Moore, M. G. (1993). Three types of interaction. In K. Harry, M. John, & D. Keegan (Eds.), Distance education: New perspectives (pp. 19–24). New York: Routledge.

    Google Scholar 

  • Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning (3rd ed.). Belmont: Wadsworth.

    Google Scholar 

  • Mouakket, S. (2015). Factors influencing continuance intention to use social network sites: The Facebook case. Computers in Human Behavior,53, 102–111. https://doi.org/10.1016/j.chb.2015.06.045.

    Article  Google Scholar 

  • Ng, E. M. W. (2018). Integrating self-regulation principles with flipped classroom pedagogy for first year university students. Computers & Education,126, 65–74.

    Article  Google Scholar 

  • Nguyen, D.-D., & Zhang, Y. J. (2011). An empirical study of student attitudes toward acceptance of online instruction and distance learning. Contemporary Issues in Education Research,4(11), 23–38.

    Article  Google Scholar 

  • Nussbaumer, A., Kravcik, M., Renzel, D., Klamma, R., Berthold, M., & Albert, D. (2014). A framework for facilitating self-regulation in responsive open learning environments. eprint arXiv:1407.5891. Retrieved from https://arxiv.org/abs/1407.5891

  • Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research,17(4), 460–469. https://doi.org/10.2307/3150499.

    Article  Google Scholar 

  • Pérez, A., Marín, V. I., & Tur, G. (2018). Information management tools for the development of self-regulated learning skills in pre-service teacher education. @tic revista d’innovació educativa,21, 31–39.

    Article  Google Scholar 

  • Pérez-Álvarez, R., Maldonado-Mahauad, J., & Pérez-Sanagustín, M. (2018). Tools to support self-regulated learning in online environments. Literature Review,10, 16–30. https://doi.org/10.1007/978-3-319-98572-5_2.

    Article  Google Scholar 

  • Petter, S., DeLone, W. H., & McLean, E. R. (2013). Information systems success: The quest for the independent variables. Journal of Management Information Systems,29(4), 7–61.

    Article  Google Scholar 

  • Pintrich, P. R. (1995). Understanding self-regulated learning. San Francisco: Jossey-Bass.

    Book  Google Scholar 

  • Pintrich, P. R., & De Groot, E. V. (1990a). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology,82(1), 33–40.

    Article  Google Scholar 

  • Pintrich, P. R., & De Groot, E. V. (1990b). Individual differences in student motivational orientation, self-regulated learning and academic achievement. Kyoto, Japan: Paper presented at the International Congress of Applied Psychology.

    Google Scholar 

  • Pintrich, P. R., & Schrauben, B. (1992). Students’ motivational beliefs and their cognitive engagement in classroom tasks. In D. Schunk & J. Meece (Eds.), Student perceptions in the classroom: Causes and consequences (pp. 149–183). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor MI: The University of Michigan, Technical Report no. 91-B-004.

  • Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational Research,63(2), 167–199.

    Article  Google Scholar 

  • Rajecki, D. W. (1990). Attitudes. Sunderland Massachusetts: Sinauer Associates.

    Google Scholar 

  • Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0. (Software program). Hamburg: University of Hamburg.

    Google Scholar 

  • Robinson, R. P., & Doverspike, D. (2006). Factors predicting the choice of an online versus a traditional course. Teaching of Psychology,33(1), 64–68. https://doi.org/10.1207/s15328023top3301_10.

    Article  Google Scholar 

  • Rubin, H. J., & Rubin, I. S. (2012). Qualitative interviewing: The art of hearing data (3rd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist,55, 68–78.

    Article  Google Scholar 

  • Schunk, D. H., & Zimmerman, B. J. (Eds.). (1994). Self-regulation, learning and performance: Issues and educational applications. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Seidman, I. (2013). Interviewing as qualitative research: A guide researchers in education and the social sciences (4th ed.). New York: Teachers College Press.

    Google Scholar 

  • Sheffield, S. L. M., McSweeney, J. M., & Panych, A. (2015). Exploring future Teachers' awareness, competence, confidence, and attitudes regarding teaching online: Incorporating blended/online experience into the teaching and learning in higher education course for graduate students. The Canadian Journal of Higher Education,45(3), 1.

    Google Scholar 

  • Smalley, N., Graff, M., & Saunders, D. (2001). A revised computer attitude scale for secondary students. Educational and Child Psychology,18(3), 47–57.

    Google Scholar 

  • Spreng, R. A., Mackenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the determinants of consumer satisfaction. Journal of Marketing,60(3), 15–32.

    Article  Google Scholar 

  • Swan, K. (2002). Building learning communities in online courses: The importance of interaction. Education, Communication & Information,2(1), 23–49.

    Article  Google Scholar 

  • Tarhini, A., Hone, K., & Liu, X.-H. (2015). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology,46(4), 739–755. https://doi.org/10.1111/bjet.12169.

    Article  Google Scholar 

  • Teo, T., & Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: a structural equation modeling approach. Computers & Education,57, 1645–1653. https://doi.org/10.1016/j.compedu.2011.03.002.

    Article  Google Scholar 

  • Triandis, H. C. (1971). Attitude and attitude change. New York: Wiley.

    Google Scholar 

  • Tsai, Y.-H., Lin, C.-H., Hong, J.-C., & Tai, K.-H. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education,121, 18–29. https://doi.org/10.1016/j.compedu.2018.02.011.

    Article  Google Scholar 

  • Vanslambrouck, S., Zhu, C., Pynoo, B., Thomas, V., Lombaerts, K., & Tondeur, J. (2019). An in-depth analysis of adult students in blended environments: Do they regulate their learning in an ‘old school’ way? Computer & Education,128, 75–87.

    Article  Google Scholar 

  • Vinzi, V. E., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares. Heidelberg: Springer.

    Book  Google Scholar 

  • Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University.

    Google Scholar 

  • Winne, P. H. (1995). Inherent details in self-regulated learning. Educational Psychologist,30, 173–187.

    Article  Google Scholar 

  • Yudko, E., Hirokawa, R., & Chi, R. (2008). Attitudes, beliefs, and attendance in a hybrid course. Computer and Education,50(4), 1217–1227. https://doi.org/10.1016/j.compedu.2006.11.005.

    Article  Google Scholar 

  • Zhou, M. M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computer & Education,92–93, 194–203. https://doi.org/10.1016/j.compedu.2015.10.012.

    Article  Google Scholar 

  • Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology,81(3), 329–339.

    Article  Google Scholar 

  • Zimmerman, B. J. (1998). Developing self-fulfilling cycles of academic regulation: An analysis of exemplary instructional models. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 1–19). New York: Guilford.

    Google Scholar 

  • Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego, CA: Elsevier.

    Chapter  Google Scholar 

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Correspondence to Yue Zhu.

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This research protocol meets the requirements of the National Statement on Ethical Conduct in Human Research. The ethical approval for the research project was granted by University of South Australia’s (UniSA) Human Research Ethics Committee CRICOS provider number 00121B.

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Appendix

Appendix

See Tables 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 and 17.

Table 4 Descriptive information: Factors measured at T1
Table 5 Descriptive information: Factors measured at T2
Table 6 Paired samples t-test: Comparison of the participants’ attitudes between T1 and T2
Table 7 Pearson correlations between the participants’ perceived course difficulty level, workload, and stress level
Table 8 Pearson correlations between continuous intention, attitude, SRL, and perceived online interactions
Table 9 Frequency of the reasons for the participants’ continuous intention to learn online
Table 10 Frequency of the themes generated from the participants’ statements about their online interactions with the course content
Table 11 Examples of the participants’ statements about their online interactions with the course content
Table 12 Frequency of the themes generated from the participants’ statements about their interactions with the online learning system
Table 13 Examples of the participants’ statements about their interactions with the online learning system
Table 14 Frequency of the themes generated from the participants’ statements about the online interactions with their teachers
Table 15 Examples of the participants’ statements about the online interactions with their teachers
Table 16 Frequency of the themes generated from the participants’ statements about their online interactions with other students
Table 17 Examples of the participants’ statements about their online interactions with other students

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Zhu, Y., Zhang, J.H., Au, W. et al. University students’ online learning attitudes and continuous intention to undertake online courses: a self-regulated learning perspective. Education Tech Research Dev 68, 1485–1519 (2020). https://doi.org/10.1007/s11423-020-09753-w

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