Abstract

Although Massive Open Online Courses (MOOCs) have attracted extensive attention among educational stakeholders, the issue of the high dropout rate has yet to be solved. The current study aimed to unpack teacher influence on MOOCs learners’ continuance learning intention, and to examine the mediating roles of students’ perceived usefulness and satisfaction. Quantitative data were collected from 166 Chinese university students located in 18 provinces. Results indicated that teacher influence is significantly associated with learners’ continuous learning intention, and when considering perceived usefulness and satisfaction, this relationship did not achieve significance but was mediated by students’ perceived usefulness and satisfaction, in addition, teacher influence did not exert a direct and significant impact on students’ satisfaction. The serial mediation model explained 65.8% of the variance of students’ continuance intention. This study uncovered the important role of teacher influence on students’ continuance learning intention in the Chinese MOOCs learning context. Results provided suggestions to policymakers, MOOCs platform and lecturers to promote MOOCs and design useful courses so as to engage students to learn continuously.

Keywords

References

• Abdullatif, H., & Velázquez-Iturbide, J. Á. (2020). Relationship between motivations, personality traits and intention to continue using MOOCs. Education and Information Technologies, 25(5), 4417–4435. https://doi.org/10.1007/s10639-017-9578-1.
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
• Aldowah, H., Al-Samarraie, H., Alzahrani, A. I., & Alalwan, N. (2020). Factors affecting student dropout in MOOCs: A cause and effect decision-making model. Journal of Computing in Higher Education, 32, 1–26. https://doi.org/10.1007/s12528-019-09241-y
• Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28–38. https://doi.org/10.1016/j.compedu.2014.08.006
• Alturki, U., & Aldraiweesh, A. (2023). An Empirical Investigation into Students’ Actual Use of MOOCs in Saudi Arabia Higher Education. Sustainability, 15(8), 6918. https://doi.org/10.3390/su15086918
• Badali, M., Hatami, J., Farrokhnia, M., & Noroozi, O. (2022). The effects of using Merrill’s first principles of instruction on learning and satisfaction in MOOC. Innovations in Education and Teaching International, 59(2), 216-225. https://doi.org/10.1080/14703297.2020.1813187
• Bhardwaj, A., & Goundar, S. (2018). Student’s Perspective of eLearning and the Future of Education with MOOCs. International Journal of Computer Science Engineering, 7(5), 2319-7323. https://doi.org/10.13140/RG.2.2.17725.92643
• Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
• Buhr, E. E., Daniels, L. M., & Goegan, L. D. (2019). Cognitive appraisals mediate relationships between two basic psychological needs and emotions in a massive open online course. Computers in Human Behavior, 96, 85–94. https://doi.org/10.1016/j.chb.2019.02.009
• Borrella, I., Caballero-Caballero, S., & Ponce-Cueto, E. (2022). Taking action to reduce dropout in MOOCs: Tested interventions. Computers & Education, 179, 104412. https://doi.org/10.1016/j.compedu.2021.104412
• Carmines, E. G., & McIver, J. P. (1981). Analyzing models with unobserved variables. In G. W. Bohrnstedt & E. F. Borgatta (Eds.), Social measurement: Current issues (pp. 65–115). Sage.
• Charo, R., Maite, A. S., & Guillermo, M. (2020). Self-regulation of learning and MOOCS retention. Computers in Human Behavior, 111, 106423. https://doi.org/10.1016/j.chb.2020.106423
• Chen, C. C., Lee, C. H., & Hsiao, K. L. (2018). Comparing the determinants of non-MOOC and MOOC continuance intention in Taiwan. Library Hi Tech, 36(4), 705–719. https://doi.org/10.1108/LHT-11-2016-0129
• Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175. https://doi.org/10.1016/j.compedu.2012.12.003
• Cronbach, L.J. (1951). Coefficient Alpha and the Internal Structure of Tests. Psychometrika, 16, 297-334. https://doi.org/10.1007/BF02310555
• Dai, H. M., Teo, T., & Rappa, N. A. (2020a). Understanding continuance intention among MOOC participants: The role of habit and MOOC performance. Computers in Human Behavior, 112, 106455. https://doi.org/10.1016/j.chb.2020.106455
• Dai, H. M., Teo, T., Rappa, N. A., & Huang, F. (2020b). Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Computers & Education, 150, 103850. https://doi.org/10.1016/j.compedu.2020.103850
• Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning: An International Journal, 11(2), 201–214. https://doi.org/10.34105/j.kmel.2019.11.010
• 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
• Eriksson, T., Adawi, T., & Stöhr, C. (2017). “Time is the bottleneck”: A qualitative study exploring why learners drop out of MOOCs. Journal of Computing in Higher Education, 29(1), 133–146. https://doi.org/10.1007/s12528-016-9127-8
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
• Goopio, J., & Cheung, C. (2021). The MOOCS dropout phenomenon and retention strategies. Journal of Teaching in Travel & Tourism, 21(2), 177-197. https://doi.org/10.1080/15313220.2020.1809050
• Hair, J. F. Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
• Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. Guilford Publications.
• Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45–58. https://doi.org/10.1016/j.edurev.2014.05.001
• Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157–168. https://doi.org/10.1016/j.compedu.2016.03.016
• Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Huang, F., Teo, T., & Zhao, X. (2023). Examining factors influencing Chinese ethnic minority English teachers’ technology adoption: An extension of the UTAUT model. Computer Assisted Language Learning, 1–23. https://doi.org/10.1080/09588221.2023.2239304
• Huang, F, Teo, T., & Zhou, M. (2020). Chinese students’ intentions to use the Internet for learning. Educational Technology Research & Development, 68(1), 575-591. https://doi.org/10.1007/s11423-019-09695-y
• Jansen, R. S., van Leeuwen, A., Janssen, J., Conijn, R., & Kester, L. (2020). Supporting learners' self-regulated learning in Massive Open Online Courses. Computers & Education, 146, 103771. https://doi.org/10.1016/j.compedu.2019.103771
• 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
• Junjie, Z. (2017). Exploring the factors affecting learners’ continuance intention of moocs for online collaborative learning: An extended ecm perspective. Australasian Journal of Educational Technology, 33(5), 123–135. https://doi.org/10.14742/ajet.2914
• Khan, M. A., Vivek, V., Nabi, M. K., Khojah, M., & Tahir, M. (2020). Students’ perception towards E-learning during COVID-19 pandemic in India: An empirical study. Sustainability, 13(1), 57. https://doi.org/10.3390/su13010057
• Khoushehgir, F., & Sulaimany, S. (2023). Negative link prediction to reduce dropout in Massive Open Online Courses. Education and Information Technologies, 28, 10385-10404. https://doi.org/10.1007/s10639-023-11597-9
• Kim, H., & Lee, J. (2020). Toward Serving MOOC Learners Globally: Focusing on Intent to Continue Using K-MOOCs. International Journal of Contents, 16(1), 65–74.
• Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.
• Lai, H. M., & Chen, C. P. (2011). Factors influencing secondary school teachers’ adoption of teaching blogs. Computers & Education, 56(4), 948–960. https://doi.org/10.1016/j.compedu.2010.11.010
• 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
• Lee, Y. Y., Gan, C. L., & Liew, T. W. (2023). Do E-wallets trigger impulse purchases? An analysis of Malaysian Gen-Y and Gen-Z consumers. Journal of Marketing Analytics, 11(2), 244-261. https://doi.org/10.1057/s41270-022-00164-9
• Lavidas, K., Achriani, A., Athanassopoulos, S., Messinis, I., & Kotsiantis, S. (2020). University students’ intention to use search engines for research purposes: A structural equation modeling approach. Education and Information Technologies, 25, 2463-2479. https://doi.org/10.1007/s10639-019-10071-9
• Lavidas, K., Komis, V., & Achriani, A. (2022). Explaining faculty members’ behavioral intention to use learning management systems. Journal of Computers in Education, 9(4), 707-725. https://doi.org/10.1007/s40692-021-00217-5
• Martín-Monje, E., Castrillo, M. D., & Ma ̃ nana-Rodríguez, J. (2018). Understanding online interaction in language MOOCs through learning analytics. Computer Assisted Language Learning, 31(3), 251–272. https://doi.org/10.1080/09588221.2017.1378237
• Maya-Jariego, I., Holgado, D., González-Tinoco, E., Castaño-Muñoz, J., & Punie, Y. (2020). Typology of motivation and learning intentions of users in MOOCs: The MOOCKNOWLEDGE study. Educational Technology Research and Development, 68(1), 203–224. https://doi.org/10.1007/s11423-019-09682-3
• Nong, Y., Buavaraporn, N., & Punnakitikashem, P. (2022). Exploring the factors influencing users’ satisfaction and continuance intention of MOOCs in China. Kasetsart Journal of Social Sciences, 43(2), 403-408. https://doi.org/10.14742/ajet.2914
• Olasina, G. (2018). Factors of best practices of e-learning among undergraduate students. Knowledge Management & E-Learning, 10(3), 265–289.
• Ouyang, Y., Tang, C., Rong, W., Zhang, L., Yin, C., & Xiong, Z. (2017). Task-technology fit aware expectation-confirmation model towards understanding of MOOCs continued usage intention. In Proceedings of the 50th Hawaii international conference on system sciences, 174–183.
• Rafque, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732. https://doi.org/10.1016/j.compedu.2019.103732
• Rekha, I. S., Shetty, J., & Basri, S. (2023). Students’ continuance intention to use MOOCs: empirical evidence from India. Education and Information Technologies, 28, 4265-4286. https://doi.org/10.1007/s10639-022-11308-w
• Shanshan, S. & Wenfei, L. (2022). Understanding the impact of quality elements on MOOCs continuance intention. Education and Information Technologies, 27, 10949–10976. https://doi.org/10.1007/s10639-022-11063-y
• Suanpang, P., Netwong, T., Manisri, T., & Duantrakoonsil, W. (2021). The factors affecting learning outcome intention of MOOCs for an online learning platform. Psychology and Education, 58(4), 451–455.
• 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
• Uchidiuno, J. O., Ogan, A., Yarzebinski, E., & Hammer, J. (2018). Going global: Understanding English language learners’ student motivation in English-language MOOCs. International Journal of Artificial Intelligence in Education, 28(4), 528–552. https://doi.org/10.1007/s40593-017-0159-7
• Wang, W., Zhao, Y., Wu, Y. J., & Goh, M. (2023). Factors of dropout from MOOCs: a bibliometric review. Library Hi Tech, 41(2), 432-453. https://doi.org/10.1108/LHT-06-2022-0306
• Wang, X., Lu, A., Lin, T., Liu, S., Song, T., Huang, X., & Jiang, L. (2022). Perceived usefulness predicts second language learners’ continuance intention toward language learning applications: a serial multiple mediation model of integrative motivation and flow. Education and Information Technologies, 27, 5033–5049. https://doi.org/10.1007/s10639-021-10822-7
• Wang, Y., Dong, C., & Zhang, X. (2020). Improving MOOC learning performance in China: An analysis of factors from the TAM and TPB. Computer Applications in Engineering Education, 28(6), 1421–1433. https://doi.org/10.1002/cae.22310
• Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028
• Xing, W., & Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 547–570. https://doi.org/10.1177/0735633118757015
• Zhao, Y., Wang, A., Sun, Y. (2020). Technological environment, virtual experience, and MOOC continuance: A stimulus–organism–response perspective. Computers & Education, 144, 103721. https://doi.org/10.1016/j.compedu.2019.103721
• Zhou, M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computers & Education, 92–93, 194–203. https://doi.org/10.1016/j.compedu.2015.10.012
• Zhou, J. (2017). Exploring the factors affecting learners’ continuance intention of MOOCs for online collaborative learning: An extended ECM perspective. Australasian Journal of Educational Technology, 33(5), 123–135. https://doi.org/10.14742/ajet.2914

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