A Case Study of University Students' Learning Performance  in Finance Education Through Blended Learning in Chongqing, China

Yuqiao Zheng

Authors

Yuqiao Zheng

Keywords:

Blended Learning, Learning Performance, Finance Education, China

Abstract

Purpose: This study aims to evaluate the factors influencing students' learning performance in finance education within a blended learning model at a public university in Chongqing. Research design, data and methodology: A quantitative research method was adopted, utilizing a questionnaire to collect sample data from the target population. Before distributing the questionnaire, the Item-Objective Congruence (IOC) and Cronbach's Alpha test were employed to assess its content validity and reliability. Results: The findings revealed that the conceptual research model successfully predicted and explained Cognitive Presence (CP), Teaching Presence (TP), Learning Motivation (LM), Interaction (INT), and Satisfaction (SAT), which were identified as significant predictors and antecedents of Students’ Learning Performance (SLP). Conclusions: The study recommends that finance course instructors and higher education administrators prioritize improving quality factors influencing learning performance to enhance students’ perceptions of the system's usefulness, thereby fostering positive attitudes toward blended learning.

Author Biography

Yuqiao Zheng

PhD.EAL Graduate School of Human Sciences, Assumption University

References

Adedoyin, O. B., & Soykan, E. (2020). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 1-13. https://doi.org/10.1080/10494820.2020.1813180

Akyol, Z., & Garrison, D. R. (2011). Understanding cognitive presence in an online and blended community of inquiry: Assessing outcomes and processes for deep approaches to learning. British Journal of Educational Technology, 42(2), 233-250. https://doi.org/10.1111/j.1467-8535.2009.01029.x

Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673-174686. https://doi.org/10.1109/ACCESS.2019.2957206

Alonso-García, S., Fernández-Gutiérrez, M., & Muñoz-Galiano, I. M. (2019). Blended learning in higher education: An analysis of models and the impact on student learning. Education Sciences, 9(3), 195. https://doi.org/10.3390/educsci9030195

Anderson, T., Rourke, L., Garrison, D. R., & Archer, W. (2001). Assessing teaching presence in a computer conferencing context. Journal of Asynchronous Learning Networks, 5(2), 1-17.

Bao, W. (2020). COVID-19 and online teaching in higher education: A case study of Peking University. Human Behavior and Emerging Technologies, 2(2), 113-115.

https://doi.org/10.1002/hbe2.191

Boelens, R., De Wever, B., & Voet, M. (2017). Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review, 22, 1-18.

https://doi.org/10.1016/j.edurev.2017.06.001

Cheng, Y. M. (2022). Can tasks and learning be balanced? A dual-pathway model of cloud-based e-learning continuance intention and performance outcomes. Kybernetes, 51(1), 210-240. https://doi.org/10.1108/K-03-2020-0153

Costin, G., Hu, H., & Schilling, R. (2018). Blended learning in finance education: A case study in China. Journal of Teaching in International Business, 29(2), 123-138.

https://doi.org/10.1080/08975930.2018.1487105

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.

Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268.

https://doi.org/10.1207/S15327965PLI1104_01

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

Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7-23. https://doi.org/10.1080/08923640109527071

Graham, C. R. (2013). Emerging practice and research in blended learning. In M. G. Moore (Ed.), Handbook of distance education (pp. 333-350). Routledge.

Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26(3), 499-510. https://doi.org/10.1207/s15327906mbr2603_7

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis (4th ed.). Prentice Hall.

Harandi, S. R. (2015). Effects of e-learning on students’ motivation. Procedia - Social and Behavioral Sciences, 181, 423-430. https://doi.org/10.1016/j.sbspro.2015.04.905

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

https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning

Hooper, D. (2012). Exploratory factor analysis. In H. Chen (Ed.), Approaches to quantitative research - Theory and its practical application: A guide to dissertation students (pp. 1-26). Oak Tree Press.

Hrastinski, S. (2019). What do we mean by blended learning? TechTrends, 63(5), 564-569.

https://doi.org/10.1007/s11528-019-00375-5

Li, C., & Phongsatha, T. (2022). Satisfaction and continuance intention of blended learning from perspective of junior high school students in the directly-entering-socialism ethnic communities of China. PLOS ONE, 17(12), e0270939. https://doi.org/10.1371/journal.pone.0270939

Mahande, R. D., Malago, J. D., Abdal, N. M., & Yasdin, Y. (2022). Factors affecting students’ performance in web-based learning during the COVID-19 pandemic. Quality Assurance in Education, 30(1), 150-165. https://doi.org/10.1108/QAE-08-2021-0130

Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1-47. https://doi.org/10.1177/016146811311500307

Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1-7.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Picciano, A. G. (2019). Blended learning: Research perspectives. Routledge.

Rienties, B. (2014). Understanding academics’ resistance towards (online) student evaluation. Assessment & Evaluation in Higher Education, 39(8), 987-1001. https://doi.org/10.1080/02602938.2014.880777

Sabry, K., & Baldwin, L. (2003). Web‐based learning interaction and learning styles. British Journal of Educational Technology, 34(4), 443-454. https://doi.org/10.1111/1467-8535.00341

Shea, P., & Bidjerano, T. (2009). Community of inquiry as a theoretical framework to foster “epistemic engagement” and “cognitive presence” in online education. Computers & Education, 52(3), 543-553. https://doi.org/10.1016/j.compedu.2008.10.007

Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202. https://doi.org/10.1016/j.compedu.2006.11.007

Trust, T., & Whalen, J. (2020). Emergency remote teaching during the COVID-19 pandemic: Teachers’ perspectives. Educational Technology Research and Development, 68(4), 2333-2363. https://doi.org/10.1007/s11423-020-09833-3

VanVoorhis, C. R. W., & Morgan, B. L. (2007). Understanding power and rules of thumb for determining sample sizes. Tutorials in Quantitative Methods for Psychology, 3(2), 43-50. https://doi.org/10.20982/tqmp.03.2.p043

Vaughan, N. (2014). Student engagement and blended learning: Making the assessment connection. Education Sciences, 4(4), 247-264. https://doi.org/10.3390/educsci4040247

Wang, J., Liu, R., Zou, Y., & Wen, Y. (2016). Self-determination theory and motivation in foreign language learning: A review of recent research. Frontiers in Psychology, 7, 502. https://doi.org/10.3389/fpsyg.2016.00502

Yin, B., & Yuan, C.-H. (2021). Precision teaching and learning performance in a blended learning environment. Frontiers in Psychology, 12, 631125. https://doi.org/10.3389/fpsyg.2021.631125

Zeqiri, J., Kareva, V., & Alija, S. (2021). Blended learning and student satisfaction: The moderating effect of student performance. Business Systems Research, 12(2), 79-94.

https://doi.org/10.2478/bsrj-2021-0018