Analyzing the difficulty level in learning mathematics online using a quantile regression approach
DOI:
https://doi.org/10.37134/ejsmt.vol10.2.5.2023Keywords:
Mathematics learning, online education, difficulty level, causal determinants, college studentsAbstract
Online learning is a difficult type of educational process due to its limitations, particularly in college mathematics courses. The essential aim of this article is to elucidate the students' difficulty level in learning mathematics amid online learning and predict its statistically significant influencing factors. The data used in this study is secondary from a paper in the mathematics education literature on the form of cross-sectional. The data were summarized employing some descriptive measures and regression modeling as an inference. The result of the study showed that, on average, students are facing "difficulty" in learning their mathematics lessons during distance learning due to some problems. The quantile regression revealed that younger and female students are experiencing higher levels of difficulty. Plus, students who spent more money on the internet are facing higher difficulty in learning. In addition, a not conducive learning environment and social distractions are predictors of difficulty in learning. Conclusively, the difficulty in learning mathematics which adversely affects their performance is due to the distractions and problems in the learning environment, low coping mechanisms, and unprecedented educational process in the form of online setup. Hence, the study suggested that teachers must be flexible with students and provide digital simulations of mathematical problems, and use interactive models in their classes.
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Allison, P. D. (2012). Logistic regression using SAS: Theory and application. SAS Institute. Retrieved from https://mycourses.aalto.fi/pluginfile.php/889996/mod_resource/content/2/Paul%20D.%20Allison%20-%20Logistic%20Regression%20Using%20SAS%20-%20Ch%202.pdf
Babicka-Wirkus, A., Wirkus, L., Stasiak, K., & Kozłowski, P. (2021). University students’ strategies of coping with stress during the coronavirus pandemic: Data from Poland. PloS one, 16(7), e0255041. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255041
Baloran, E. T. (2020). Knowledge, attitudes, anxiety, and coping strategies of students during COVID-19 pandemic. Journal of loss and trauma, 25(8), 635-642. https://doi.org/10.1080/15325024.2020.1769300
Barrot, J. S., Llenares, I. I., & Del Rosario, L. S. (2021). Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines. Education and information technologies, 26(6), 7321-7338. https://link.springer.com/article/10.1007/s10639-021-10589-x
Bestiantono, D. S., Agustina, P. Z. R., & Cheng, T. H. (2020). How students’ perspectives about online learning amid the COVID-19 pandemic?. Studies in Learning and Teaching, 1(3), 133-139. https://doi.org/10.46627/silet.v1i3.46
Cardullo, V., Wang, C. H., Burton, M., & Dong, J. (2021). K-12 teachers’ remote teaching self-efficacy during the pandemic. Journal of research in innovative teaching & learning, 14(1), 32-45. https://www.emerald.com/insight/content/doi/10.1108/JRIT-10-2020-0055/full/html
Carius, A. C. (2020). Teaching Practices in Mathematics During COVID-19 Pandemic: Challenges for Technological Inclusion in a Rural Brazilian School. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 72(1), 35-43.
https://www.asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/6230/2232
Cassibba, R., Ferrarello, D., Mammana, M. F., Musso, P., Pennisi, M., & Taranto, E. (2021). Teaching mathematics at a distance: A challenge for universities. Education Sciences, 11(1), 1-20. https://doi.org/10.3390/educsci11010001
Casinillo, L. (2022). Is learning mathematics still creative and enjoyable during the COVID-19 pandemic? Indonesian Journal of Social Research (IJSR), 4(2), 124-138. https://doi.org/10.30997/ijsr.v4i2.208
Casinillo, L. F. (2022). Modeling Determinants of Challenge in Learning Statistics in Time of the COVID-19 Pandemic. Philippine Social Science Journal, 5(3), 131-139. https://doi.org/10.52006/main.v5i3.536
Casinillo, L. F., Casinillo, E. L., Valenzona, J. V., Almonite, M. R. C., & Valenzona, D. L. (2022). How Challenging It Is to Learn Mathematics Online. Philippine Social Science Journal, 5(1), 80-89. https://doi.org/10.52006/main.v5i1.447
Casinillo, L. F. (2023). Are students submitting their mathematics outputs on time during the covid-19 pandemic?: a statistical modeling. Canadian Journal of Family and Youth/Le Journal Canadien de Famille et de la Jeunesse, 15(3), 100-112.
Debowska, A., Horeczy, B., Boduszek, D., & Dolinski, D. (2020). A repeated cross-sectional survey assessing university students' stress, depression, anxiety, and suicidality in the early stages of the COVID-19 pandemic in Poland. Psychological Medicine, 1-4. https://doi.org/10.1017/S003329172000392X
Dontre, A. J. (2021). The influence of technology on academic distraction: A review. Human Behavior and Emerging Technologies, 3(3), 379-390. https://doi.org/10.1002/hbe2.229
Dubey, P., & Pandey, D. (2020). Distance learning in higher education during a pandemic: Challenges and opportunities. The International Journal of Indian Psychology, 8(2), 43-46. https://doi.org/10.25215/0802.204
Frid, S., Sumpter, L., & Nortvedt, G. A. (2020). Who is best in mathematics?. Proceedings of the International Groups for the Psychology of Mathematics Education, 152-161. https://www.duo.uio.no/handle/10852/82907
Hollis, R. B., & Was, C. A. (2016). Mind wandering, control failures, and social media distractions in online learning. Learning and Instruction, 42, 104-112. https://doi.org/10.1016/j.learninstruc.2016.01.007
Irfan, M., Kusumaningrum, B., Yulia, Y., & Widodo, S. A. (2020). Challenges during the pandemic: use of e-learning in mathematics learning in higher education. Infinity Journal, 9(2), 147-158.
http://ejournal.stkipsiliwangi.ac.id/index.php/infinity/article/view/1830
Islam, M. A., Barna, S. D., Raihan, H., Khan, M. N. A., & Hossain, M. T. (2020). Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional
survey. PloS one, 15(8), e0238162. https://doi.org/10.7910/DVN/N5BUJR
Kim, S. H., & Park, S. (2021). Influence of learning flow and distance e-learning satisfaction on learning outcomes and the moderated mediation effect of social-evaluative anxiety in nursing college students during the COVID-19 pandemic: A cross-sectional study. Nurse Education in Practice, 56, 103197. https://doi.org/10.1016/j.nepr.2021.103197
Koenker, R. (2005). Quantile regression (Vol. 38). Cambridge university press. http://mirrors.nic.cz/R/web/packages/quantreg/vignettes/rq.pdf
Kok, A. A., Pan, K. Y., Rius-Ottenheim, N., Jörg, F., Eikelenboom, M., Horsfall, M., ...& Penninx, B. W. (2022). Mental health and perceived impact during the first Covid-19 pandemic year: A longitudinal study in Dutch case-control cohorts of persons with and without depressive, anxiety, and obsessive-compulsive disorders. Journal of affective disorders, 305, 85-93. https://doi.org/10.1016/j.jad.2022.02.056
Lee, V. H., Hew, J. J., Leong, L. Y., Tan, G. W. H., & Ooi, K. B. (2022). The dark side of compulsory e-education: Are students really happy and learning during the COVID-19 pandemic?. International Journal of Human–Computer Interaction, 38(12), 1168-1181.
Mátyás, L., & Sevestre, P. (2013). The econometrics of panel data: Handbook of theory and applications (Vol. 28). Springer Science & Business Media. Retrieved from https://link.springer.com/book/10.1007/978-94-009-0375-3
Mulenga, E. M., & Marbán, J. M. (2020). Is COVID-19 the gateway for digital learning in mathematics education?. Contemporary Educational Technology, 12(2), ep269. https://doi.org/10.30935/cedtech/7949
Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in health sciences education, 15(5), 625-632. https://link.springer.com/article/10.1007/s10459-010-9222-y
Onyema, E. M., Eucheria, N. C., Obafemi, F. A., Sen, S., Atonye, F. G., Sharma, A., & Alsayed, A. O. (2020). Impact of Coronavirus pandemic on education. Journal of Education and Practice, 11(13), 108-121. https://doi.org/10.7176/JEP/11-13-12
Reed, G. F., Lynn, F., & Meade, B. D. (2002). Use of coefficient of variation in assessing variability of quantitative assays. Clinical and Vaccine Immunology, 9(6), 1235-1239. https://doi.org/10.1128/CDLI.9.6.1235-1239.2002
Seriño, M. N. V. (2017). Effects of affluence on rising household carbon emission in the Philippines: An application using quantile regression approach the Philippines. DLSU Business & Economics Review, 26(2), 147-157. https://www.dlsu.edu.ph/wp-content/uploads/2019/03/10serino-012517.pdf
Simorangkir, M. R. R., Manalu, R. U., & Masta, N. (2021). Prediction and Analysis of Mathematics Anxiety Disorders in Adolescents During the Pandemic. Solid State Technology, 64(2), 3042-3049. http://repository.uki.ac.id/3825/
Tannert, S., & Gröschner, A. (2021). Joy of distance learning? How student self-efficacy and emotions relate to social support and school environment. European Educational Research Journal, 20(4), 498-519. https://doi.org/10.1177/14749041211024784
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