Journal Screenshot

International Journal of Academic Research in Business and Social Sciences

Open Access Journal

ISSN: 2222-6990

Online Learning on Quantitative Subjects during COVID-19: Identifying Factor Analysis for Teaching Effectiveness

Cheam Chai Li

http://dx.doi.org/10.6007/IJARBSS/v11-i5/9865

Open access

Online teaching and learning have witnessed tremendous growth in recent years. Thanks to progressive technological developments, researchers and educators are facilitated in conducting classes online to ensure teaching and learning processes remain on schedule. Such rapid growth in online learning renders it crucial for researchers to seek for understanding the manner in which an online classroom impacts learners. Previous studies have shown various e-learning and online learning tools that are effective for teaching and learning purposes in the health and dentistry professions. The current study was thus aimed at validating some effective online teaching and learning instruments for quantitative subjects, as well as organising and summarising the findings into a few core factors. Data were collected from undergraduates that had signed up for quantitative subjects using a Web-based instrument during the Coronavirus Disease 2019 COVID-19 pandemic, ranging from Monte Carlo and parallel analyses to principal components with varimax rotations. Subsequently, 20 items were reduced to three factors in which 16 of them were maintained, while the total variance explained was able to retain 75.382% of the original 100%. Out of the 16 items, eight items were correlated with the attitude factor, while six and two items were correlated with educator and flexibility factors, respectively. It is hoped that these factors can be henceforth utilised as the basic approach for supporting online teaching and learning effectiveness in the education industry at present and for the future.

Bao, W. (2020). COVID-19 and online teaching in higher education: A case study of Peking University. Hum Behav & Emerg Tech. 2, 113–115. https://doi.org/10.1002/hbe2.191
Berge, Z. L. (1999). Interaction in post-secondary, Web-based learning and teaching. Educational Technology 39 (1), 5–11.
Bertea, P. (2009). Measuring Students’ Attitude towards Online learning. A Case Study. A paper presented in the 5th International Scientific conference on online learning and software of Education, Bucharist.
Bolliger D. U., and Wasilik, O. (2009). Factors Influencing Faculty Satisfaction with Online Teaching and Learning in Higher Education, Distance Education, 30(1), 103-116.
Douglas, A., Miller, B., Kwansa, F., and Cummings, P. (2008). Students’ perceptions of the usefulness of a virtual simulation in post- secondary hospitality education. Journal of Teaching in Travel and Tourism. 7(3), 1-19.
Filius, R. M., Kleijn, R. A. M., Uijl, S. G., Prins, F. J., Rijen, H. V. M., & Grobbee, D. E. (2019). Audio peer feedback to promote deep learning in online education. Journal of Computer Assisted Learning, 35(5), 607-619. https://doi.org/10.1111/jcal.12363
Gooden, D., Preziosi, R., and Barnes, F. (2009). An examination of Kolb’s learning style inventory. American Journal of Business Education. 2(3), 57-62.
Govindasamy, T. (2001). Successful Implementation of e-learning: Pedagogical Considerations. The Internet and Higher Education. 4(3-4), 287-299.
Hara, N., and Kling, R. (2000). Students’ distress with a Web-based distance education course. Information, Communication and Society. 3(4), 557–579.
Henson, R. K., and Roberts, J. K. (2006). Use of Exploratory Factor Analysis in Published Research: Common Errors and Some Comment on Improved Practice. Educational and Psychological Measurement. 66(3), 393-416.
Jackson, J., Weiss, M. A., Andres, B. S., Rebecca, M. N. (2020) Global Economics Effects of Covid19. Congressional Research Service. https://fas.org/sgp/crs/row/R46270.pdf
Keeton, M. T. (2004). Best online instructional practices: Report of phase I of an ongoing study. Journal of Asynchronous Learning Networks. 8(2), 75–100.
Kieffer, K. M. (1999). An introductory primer on the appropriate use of exploratory and confirmatory factor analysis. Research in the Schools. 6, 75-92.
Kim, M., and Hannafin, M. (2011). Scaffolding 6th graders’ problem-solving in technology-enhanced science classrooms: a qualitative case study. Instructional Science: An International Journal of the Learning Sciences. 39(3), 255-282.
Martin, F., Budhrani, K., Kumar, S., & Ritzhaupt, A. (2019). Award-winning faculty online teaching practices: Roles and competencies. Online Learning, 23(1), 184-205. doi:10.24059/olj.v23i1.1329
Miles, S., Leinster, S. J. (2007). Medical students’ perceptions of their educational environment: Expected versus actual perceptions. Med Educ. 41, 265–272.
Moazami, F., Bahrampour, E., Azar, M. R., Jahedi, F., Moattari, M. (2014) Comparing two methods of education (virtual versus traditional) on learning of Iranian dental students: a post-test only design study. BMC Med Educ. 14(45).https://doi.org/10.1186/1472-6920-14-45.
Naidu, S. (2017) How flexible is flexible learning, who is to decide and what are its implications? Distance Education. 38(3), 269-272.
Northrup, P. T. (2002). Online learners’ preferences for interaction. The Quarterly Review of Distance Education. 3(2), 219–226.
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
Parry, J., Mathers, J., Al-Fares, A., Mohammad, M., Nandakumar, M., Tsivos, D. (2002). Hostile teaching hospitals and friendly district general hospitals: Final year students’ views on clinical attachment locations. Med Edu. 36, 1131–1141.
Pedhazur, E. J., & Schmelkin, L. P. (1991). Measurement, design, and analysis: An integrated approach. Hillsdale, NJ: Lawrence Erlbaum.
Preziosi, R., and Alexakis, G. (2011). A comparison of traditional instructional methods and accelerated learning methods in leadership education. International Leadership Journal. 3(1), 79-89.
Robinson, C., and Taylor, C. (2007). Theorizing student voice: values and perspectives. Improving Schools. 10(5), 5-17.
Rosenberg, M. J. (2001). Online learning, strategies for delivering knowledge in the digital age. New York: McGraw-Hill.
Saunders, S. (2019). Flexible and accessible: How to make online learning effective, Studiosity. https://www.studiosity.com/blog/flexible-and-accessible-how-to-make-online-learning-effective
Selim, H. M. (2007). Critical success factors for online learning acceptance: Confirmatory factor models. Computers and Education. 49, 396–413.
Shivangi Dhawan (2020). Online Learning: A Panacea in the Time of COVID-19 Crisis. Journal of Educational Technology Systems. 49(1), 5–22.
Smith, B., Caputi, P., & Rawstorne, P. (2000). Differentiating computer experience and attitudes toward computers: an empirical investigation. Computer in Human Behavior. 16, 59-81.
Young, S. (2006). Student Views of Effective Online Teaching in Higher Education. The American Journal of Distances education. 20(2), 65-77.
UNESCO. (2020). Global Education Coalition-290-million students out school due-COVID-19. In Unesco. https://en.unesco.org/news/290-million-students-out-school-due-covid-19-unesco-releases-first-globalnumbers-and-mobilizes.
Varthis, S., Anderson, O. R. (2016) Students’ perceptions of a blended learning experience in dental education. Eur J Dent Educ. 1–7. https://doi.org/10.1111/eje.12253

In-Text Citation: (Li, 2021)
To Cite this Article: Li, C. C. (2021). Online Learning on Quantitative Subjects during COVID-19: Identifying Factor Analysis for Teaching Effectiveness. International Journal of Academic Research in Business and Social Sciences, 11(5), 127–138.