Factors Influencing Undergraduates’ Engagement and Satisfaction with Online Teaching in Chengdu, China
DOI:
https://doi.org/10.14456/au-ejir.2025.3Keywords:
Online Teaching, Undergraduate, Engagement, SatisfactionAbstract
Purpose: The research aimed to examine the factors influencing undergraduates' engagement and satisfaction with online teaching in Chengdu, China. The conceptual framework proposed the relationships among Teachers' self-efficacy, Teachers' technical readiness, Teachers' empathy, Teachers' responsiveness, Students' sensory requirements, Students' engagement and Students' satisfaction. Research design, data and methodology: The researcher used multistage sampling techniques to select the sample. A questionnaire survey was conducted among 500 undergraduates from Xihua University in Chengdu, China. Cronbach's Alpha was used to assess the reliability, while skewness and kurtosis tests evaluated data normality. Confirmatory Factor Analysis (CFA) was performed to ensure the model’s validity and, and Structural Equation Modeling (SEM) was used to assess model fit and test hypotheses. Results: The results explicated that teachers' self-efficacy and teachers' technical readiness have significant influence on students' engagement. Teachers' self-efficacy, teachers' technical readiness, teachers' responsiveness, teachers' empathy, students' sensory requirements and students' engagement have significant influence on students' satisfaction. Conclusions: Eight hypotheses were proven to fulfil the research objectives. Universities are suggested to continuously enhance teachers' self-efficacy and online teaching techniques, while also prioritizing students' reactions and emotional well-being. Additionally, fostering students' engagement in online teaching can effectively improve students' satisfaction with online teaching, ultimately leading to better learning outcomes.
References
Ahmed, I., Nawaz, M. M., Ahmad, Z., Ahmad, Z., Shaukat, M. Z., Usman, A., Wasim-ul-Rehman, A., & Ahmed, N. (2010). Does service quality affect students’ performance? Evidence from institutes of higher learning. African Journal of Business Management, 4, 2527-2533. https://www.semanticscholar.org/paper/Does-service-quality-affect-students%27-performance-Ahmed-Nawaz/72fc4de4c6021656328b22df799d602b1be2ca95
Ahmed, I., Nawaz, M. M., Ahmad, Z., Shaukat, M. Z., Usman, A., & Wasim-ul-Rehman, A. (2014). The relationship between perceived fairness in performance appraisal and OCB: Mediating role of organizational commitment. International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(4), 17-27. https://doi.org/10.6007/IJARAFMS/v4-i4/1186
Alarabiat, A., Hujran, O., Soares, D., & Tarhini, A. (2021). Examining students' continuous use of online learning in the post-COVID-19 era: An application of the process virtualization theory. Information Technology & People, 36(1), 21-47. https://doi.org/10.1108/ITP-02-2021-0142
Al-Awidi, H. M., & Aldhafeeri, F. M. (2017). Teachers’ readiness to implement digital curriculum in Kuwaiti schools. Journal of Information Technology Education: Research, 16, 105–126. https://doi.org/10.28945/3685
Al Hassani, A. A., & Wilkins, S. (2022). Student retention in higher education: The influences of organizational identification and institution reputation on student satisfaction and behaviors. International Journal of Educational Management, 36(6), 1046-1064. https://doi.org/10.1108/IJEM-03-2022-0123
Ali, M., Puah, C.-H., Fatima, S., Hashmi, A., & Ashfaq, M. (2022). Student e-learning service quality, satisfaction, commitment and behaviour towards finance courses in COVID-19 pandemic. International Journal of Educational Management, 36(6), 892–907. https://doi.org/10.1108/IJEM-04-2021-0133
Anwar, G., & Surarchith, N. K. (2015). Factors affecting shoppers’ behavior in Erbil, Kurdistan – Iraq. International Journal of Social Sciences & Educational Studies, 1(4), 10–16. Retrieved from https://www.proquest.com/docview/2394989100/abstract/9C154393579D4C04PQ/1
Arifiati, N., Nurkhayati, E., Nurdiawati, E., Pamungkas, G., Adha, S., 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).
Athiyaman, A. (1997). Linking student satisfaction and service quality perceptions: The case of university education. European Journal of Marketing, 31(7), 528-540. https://doi.org/10.1108/03090569710176655
Axelson, R. D., & Flick, A. (2010). Defining student engagement. Change: The Magazine of Higher Learning, 43(1), 38-43. https://doi.org/10.1080/00091383.2011.533096
Badri, M., Al Rashedi, A., Yang, G., Mohaidat, J., & Al Hammadi, A. (2014). Technology readiness of school teachers: An empirical study of measurement and segmentation. Journal of Information Technology Education: Research, 13, 257-275. https://doi.org/10.28945/2082
Batista-Toledo, S., & Gavilan, D. (2023). Student experience, satisfaction and commitment in blended learning: A structural equation modelling approach. Mathematics, 11(3), 749. https://doi.org/10.3390/math11030749
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
https://doi.org/10.1037/0033-2909.107.2.238
Bishop, K., Etmanski, C., & Page, M. B. (2018). Engagement in online learning: It's not all about faculty! In A. Altmann, B. Ebersberger, C. Mössenlechner, & D. Wieser (Eds.), The disruptive power of online teaching (pp. 83-98). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78754-325-620181006
Briggs, B., Kalra, S., Masneri, D., & Husain, I. (2023). Impact of a focused online teaching module on airway intervention: Can an online teaching module enable knowledge acquisition and increased confidence in airway management?. Journal of Medical Education and Curricular Development, 10, 23821205231192335. https://doi.org/10.1177/23821205231192335
Brown, T. A., & Moore, M. T. (2012). Confirmatory factor analysis. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 361-379). The Guilford Press.
Bui, H. T. T., Bui, Q. T. T., Nguyen, T. T. P., Cao, Q. H., Phung, T. V., & Nguyen, H. T. (2023). Assessing the relationship between service quality, satisfaction and loyalty: The Vietnamese higher education experience. Quality Assurance in Education, 31(2), 197-214. https://doi.org/10.1108/QAE-01-2022-0015
Carini, R. M., Kuh, G. D., & Klein, S. P. (2006). Student engagement and student learning: Testing the linkages. Research in Higher Education, 47(1), 1-32. https://doi.org/10.1007/s11162-005-8150-9
Casteel, A., & Bridier, N. L. (2021). Describing populations and samples in doctoral student research. International Journal of Doctoral Studies, 16, 339-362.
Chen, H. H. (2017). Analysis of the influencing factors of college students' satisfaction with online course learning [Master's thesis]. Nanjing Normal University.
China Internet Network Information Center (CNNIC). (2021). The 47th statistical report on Internet development in China. https://www.cnnic.com.cn/IDR/ReportDownloads/202104/P020210420557302172744.pdf
Choi, H. J., & Yang, M. (2011). The effect of problem-based video instruction on student satisfaction, empathy, and learning achievement in the Korean teacher education context. Higher Education, 62(5), 551-561.
https://doi.org/10.1007/s10734-010-9403-x
Darawong, C., & Widayati, A. (2022). Improving student satisfaction and learning outcomes with service quality of online courses: Evidence from Thai and Indonesian higher education institutions. Journal of Applied Research in Higher Education, 14(4), 1245-1259. https://doi.org/10.1108/JARHE-02-2021-0074
Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19-45.
https://doi.org/10.1006/ijhc.1996.0040
Dikko, M. (2016). Establishing construct validity and reliability: Pilot testing of a qualitative interview for research in Takaful (Islamic insurance). The Qualitative Report, 21(3), 521-528. https://doi.org/10.46743/2160-3715/2016.2243
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/10.1177/002224378101800104
Geng, S., Law, K. M. Y., & Niu, B. (2019). Investigating self-directed learning and technology readiness in a blending learning environment. International Journal of Educational Technology in Higher Education, 16(1), 17.
https://doi.org/10.1186/s41239-019-0147-0
Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2007). Research methods for business. Education + Training, 49(4), 336-337. https://doi.org/10.1108/et.2007.49.4.336.2
Ikram, M., Kenayathulla, H. B., & Saleem, S. M. U. (2023). Unlocking the potential of technology usage in fostering education quality and students' satisfaction: A case of Pakistani higher education. Kybernetes, 54(3), 1938-1965.
https://doi.org/10.1108/K-03-2023-0452
Joseph, G. V., Thomas, K. A., & Nero, A. (2021). Impact of technology readiness and techno-stress on teacher engagement in higher secondary schools. Digital Education Review, 40, 51-65. https://doi.org/10.1344/der.2021.40.51-65
Kapuza, A., Kolygina, D., Khavenson, T., & Koroleva, D. (2022). A time to gather stones – Barriers to use technologies before the COVID-19 school closures. International Journal of Educational Management, 36(6), 923-936.
https://doi.org/10.1108/IJEM-02-2022-0069
Kavanagh, S. S., Metz, M., Hauser, M., Fogo, B., Taylor, M. W., & Carlson, J. (2020). Practicing responsiveness: Using approximations of teaching to develop teachers’ responsiveness to students’ ideas. Journal of Teacher Education, 71(1), 94-107. https://doi.org/10.1177/0022487119841884
Lotu Technology (RUNTO). (2023). 2023 China Digital Education Market Data Report. https://www.iviewxtech.com /news/2023-e-education-market-report-75964030.html
Lu, Q., & Mustafa, Z. (2021). Toward the impact of EFL teachers’ self-efficacy and collective efficacy on students’ engagement. Frontiers in Psychology, 12, 1-4. https://doi.org/10.3389/fpsyg.2021.744586
Maamari, B. E., & Majdalani, J. F. (2017). Emotional intelligence, leadership style, and organizational climate. International Journal of Organizational Analysis, 25(2), 327-345. https://doi.org/10.1108/IJOA-04-2016-1010
Magasi, C., Mashenene, R. G., & Ndengenesa, D. M. (2022). Service quality and students’ satisfaction in Tanzania’s higher education: A re-examination of SERVQUAL model. International Review of Management and Marketing, 12(3), 18-25. https://doi.org/10.32479/irmm.13040
Maini, R., Sehgal, S., & Agrawal, G. (2021). Todays' digital natives: An exploratory study on students' engagement and satisfaction towards virtual classes amid COVID-19 pandemic. The International Journal of Information and Learning Technology, 38(5), 454-472. https://doi.org/10.1108/IJILT-03-2021-0055
Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook (3rd ed.). SAGE Publications, Inc.
Miller, A. D., Ramirez, E. M., & Murdock, T. B. (2017). The influence of teachers’ self-efficacy on perceptions: Perceived teacher competence and respect and student effort and achievement. Teaching and Teacher Education, 64, 260-269. https://doi.org/10.1016/j.tate.2017.02.008
Naidoo, J., Pillay, D., & Naicker, I. (2023). Together a catalyst: Learning from our co-creative arts-based inquiry about our teacher selves during the transition to online teaching. International Journal of Qualitative Methods, 22. https://doi.org/10.1177/16094069231180167
Nikou, S., & Maslov, I. (2023). Finnish university students' satisfaction with e-learning outcomes during the COVID-19 pandemic. International Journal of Educational Management, 37(1), 1-21. https://doi.org/10.1108/IJEM-04-2022-0166
Overby, E. (2008). Process Virtualization Theory and the impact of information technology. Organization Science, 19(2), 277-291. https://doi.org/10.1287/orsc.1070.0316
Parahoo, S. K., Santally, M. I., Rajabalee, Y., & Harvey, H. L. (2016). Designing a predictive model of student satisfaction in online learning. Journal of Marketing for Higher Education, 26(1), 1-19. https://doi.org/10.1080/08841241.2015.1083511
Parasuraman, A. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320. https://doi.org/10.1177/109467050024001
Pedroso, B., Pilatti, L. A., Gutierrez, G. L., & Picinin, C. T. (2016). Measurement model for innovation management assessment in the Brazilian automotive industry. International Journal of Innovation, 4(2), 71-84. https://doi.org/10.5585/iji.v4i2.107
Poehlein, G. W. (1996). Universities and information technologies for instructional programs: Issues and potential impacts. Technology Analysis & Strategic Management, 8(3), 283-290. https://doi.org/10.1080/09537329608524251
Poulou, M. S., Reddy, L. A., & Dudek, C. M. (2019). Relation of teacher self-efficacy and classroom practices: A preliminary investigation. School Psychology International, 40(1), 25-48. https://doi.org/10.1177/0143034318798045
Raman, S., Mohd Suki, N., Heng Wei, L., & Chinniah, S. (2022). Exploring factors influencing service trade-offs in the higher education sector: Evidence from Malaysia. International Journal of Quality and Service Sciences, 14(4), 555-575. https://doi.org/10.1108/IJQSS-09-2021-0118
Rashidi, N., & Moghadam, M. (2014). The effect of teachers’ beliefs and sense of self-efficacy on Iranian EFL learners’ satisfaction and academic achievement. TESL-EJ, 18(2), 1-23. https://eric.ed.gov/?id=EJ1045203
Rogers, J., & Smith, M. (2011). Demonstrating genuine interest in students' needs and progress: Implications for student satisfaction with courses. Journal of Applied Research in Higher Education, 3(1), 6-14.
https://doi.org/10.1108/17581181111150865
Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. R. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58(7), 935-943.
https://doi.org/10.1016/j.jbusres.2003.10.007
Sia, J. K.-M., Chin, W. L., Voon, M. L., Adamu, A. A., & Tan, S. C. K. (2023). Transitioning from online teaching to blended teaching in the post-pandemic era: What has COVID-19 taught us?. Cogent Education, 10(2), 1-18.
https://doi.org/10.1080/2331186X.2023.2282313
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. Psychological Reports, 101(2), 521-532.
https://doi.org/10.2466/pr0.101.2.521-532
Skaalvik, E. M., & Skaalvik, S. (2007). Dimensions of teacher self-efficacy and relations with strain factors, perceived collective teacher efficacy, and teacher burnout. Journal of Educational Psychology, 99(3), 611-625.
https://doi.org/10.1037/0022-0663.99.3.611
Stapleton, C. D. (1997). Basic concepts and procedures of confirmatory factor analysis. https://eric.ed.gov/?id=ED407416
Trad, M., Alayoubi, M. O., Abdul Khalek, R., & Khaddage-Soboh, N. (2022). Assessing the influence of emotional intelligence on teachers' performance in Lebanese private education institutions. Higher Education, Skills and Work-Based Learning, 12(3), 556-573. https://doi.org/10.1108/HESWBL-12-2020-0268
Tschannen-Moran, M., Hoy, A. W., & Hoy, W. K. (1998). Teacher efficacy: Its meaning and measure. Review of Educational Research, 68(2), 202-248. https://doi.org/10.3102/00346543068002202
Ullman, J. B. (2006). Structural equation modeling: Reviewing the basics and moving forward. Journal of Personality Assessment, 87(1), 35-50. https://doi.org/10.1207/s15327752jpa8701_03
Vital-López, L., García-García, R., Rodríguez-Reséndíz, J., Paredes-García, W., Zamora-Antuñano, M., Oluyomi-Elufisan, T., Rodríguez Reséndiz, H., Álvarez Sánchez, A., & Cruz-Pérez, M. (2022). The impacts of COVID-19 on technological and polytechnic university teachers. Sustainability, 14(8), 4593. https://doi.org/10.3390/su14084593
Wang, W. (2016). Research on the development of online education in American universities [Master's thesis]. Liaoning Normal University.
Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84-136. https://doi.org/10.2307/270754
Wu, J.-H., & Wang, Y.-M. (2006). Measuring KMS success: A respecification of the DeLone and McLean's model. Information & Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002
Xu, S. J. (2021). Research on the construction of an influencing factor model for college students' online learning satisfaction [Master's thesis]. Shanxi Normal University.
Yousaf, H. Q., Rehman, S., Ahmed, M., & Munawar, S. (2023). Investigating students’ satisfaction in online learning: The role of students’ interaction and engagement in universities. Interactive Learning Environments, 31(10), 7104-7121.
https://doi.org/10.1080/10494820.2022.2061009
Zaki, J. (2014). Empathy: A motivated account. Psychological Bulletin, 140(6), 1608-1647. https://doi.org/10.1037/a0037679
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Luo Li

This work is licensed under a Creative Commons Attribution 4.0 International License.
A separate Copyright Form will be sent to authors whose paper is accepted for publication.