Navigating New Normal: Evaluating the Effects of Blended Learning Models on College Student Outcomes in Southwest China Post-Epidemic

Chen Hongxu; Zainudin Bin Mohd Isa

doi:10.56982/dream.v3i05.236

Authors

Chen Hongxu City University, Kuala Lumpur, Malaysia
Zainudin Bin Mohd Isa City University, Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.56982/dream.v3i05.236

Keywords:

blended learning, student outcomes, Southwest China post-epidemic

Abstract

This study investigates the impacts of blended learning models on college student outcomes in Southwest China post-COVID-19 epidemic, focusing on both academic performance and psychological well-being. Utilizing a quasi-experimental design, the research assesses differences in outcomes between students engaged in traditional learning methods and those participating in blended learning. Quantitative data collected through standardized tests and structured questionnaires highlight significant improvements in academic performance and increased student engagement in the blended learning group compared to the control group. Furthermore, psychological assessments indicate enhanced satisfaction and reduced stress levels among students exposed to blended learning, suggesting that these models can offer robust support in a post-pandemic educational landscape. However, the study also identifies several challenges, including the digital divide and infrastructural limitations, particularly affecting students in rural and less urbanized areas. The findings underscore the need for strategic enhancements in technological infrastructure and comprehensive faculty training to support the effective implementation of blended learning models. Recommendations include policy interventions to bridge access gaps and targeted professional development programs for educators. Future research directions proposed include longitudinal studies to examine the long-term effects of blended learning and comparative studies across different regions to tailor educational strategies to diverse student populations. These insights contribute to the broader discourse on optimizing blended learning in emerging educational paradigms post-epidemic, offering valuable guidelines for educators, administrators, and policymakers in regions similar to Southwest China.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4(2), Article 17. DOI: https://doi.org/10.20429/ijsotl.2010.040217

Babbie, E. (2015). The Practice of Social Research. Cengage Learning.

Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.

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

Chen, L. (2021). Long-term impacts of pandemic-driven online learning in Chinese higher education. Education and Information Technologies, 26, 445-461.

Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education. Routledge. DOI: https://doi.org/10.4324/9781315456539

Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Thousand Oaks, CA: SAGE Publications.

Cucinotta, D., & Vanelli, M. (2020). WHO declares COVID-19 a pandemic. Acta Biomedica, 91(1), 157-160.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. DOI: https://doi.org/10.2307/249008

Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95-105. DOI: https://doi.org/10.1016/j.iheduc.2004.02.001

Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk & C. R. Graham (Eds.), The handbook of blended learning: Global perspectives, local designs (pp. 3-21).

Horn, M. B., & Staker, H. (2015). Blended: Using Disruptive Innovation to Improve Schools. Jossey-Bass.

Huang, R. H., Liu, D. J., Tlili, A., Yang, J. F., & Wang, H. H. (2020). Handbook on Facilitating Flexible Learning During Educational Disruption: The Chinese Experience in Maintaining Undisrupted Learning in COVID-19 Outbreak. Smart Learning Institute of Beijing Normal University.

Johnson, N., Veletsianos, G., & Seaman, J. (2021). U.S. Faculty and Administrators' Experiences and Approaches in the Early Weeks of the COVID-19 Pandemic. Online Learning Journal, 25(1), 6-21. DOI: https://doi.org/10.24059/olj.v24i2.2285

Jonassen, D. (1999). Designing constructivist learning environments. In C. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory, Volume II (pp. 215-239). Lawrence Erlbaum Associates.

Lee, J. (2020). Learning from the past: Pandemic insights for educational practice. Journal of Educational Change, 21(2), 181-195.

Liu, H., & Zhang, S. (2019). Psychological wellbeing and blended learning: The importance of the teacher-student relationship. Educational Psychology Review, 31(3), 743-764.

Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. U.S. Department of Education.

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

Porter, W. W., & Graham, C. R. (2016). Institutional drivers and barriers to faculty adoption of blended learning in higher education. British Journal of Educational Technology, 47(4), 748-762. DOI: https://doi.org/10.1111/bjet.12269

Selwyn, N. (2020). Digital Technology and the Contemporary University: Degrees of Digitization. Routledge.

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204. DOI: https://doi.org/10.1287/mnsc.46.2.186.11926

Wang, Y., Wang, X., & Huang, R. (2019). Examining the role of feedback messages in undergraduate students' writing performance during an online peer assessment activity. Internet and Higher Education, 41, 20-30.

Wang, F., Wang, D., & Liu, M. (2019). Digital divide and its impact on academic performance in Southwest China. Chinese Education & Society, 52(1-2), 100-115.

Zhang, M., & Zuo, M. (2016). The effects of different types of trust in open online learning communities. Educational Technology Research and Development, 64(5), 953-975.

Zhang, X., & Wang, Q. (2018). Challenges to Education in Southwest China. China Educational Review, 5(2), 34-45.

Zhao, Y., Wang, L., Yen, C., & Liu, Q. (2020). Digital infrastructure in support of online learning for students with disabilities during the COVID-19 pandemic: A qualitative study in China. Journal of Inclusive Education.

Zhao, Y., Pugh, K., Sheldon, S., & Byers, J. L. (2002). Conditions for classroom technology innovations. Teachers College Record, 104(3), 482-515. DOI: https://doi.org/10.1111/1467-9620.00170

Zhao, Y., & Namasivayam, K. (2009). The impact of blended learning on student performance in a hospitality management course. Journal of Hospitality & Tourism Education, 21(1), 37-44.