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Abstract

The abrupt migration of educational institutions into a more flexible mode of learning due to the COVID-19 pandemic has undoubtedly resulted in students' difficulties. Such difficulties specific to mathematics flexible learning are generalized in this quantitative study. Using Principal Component Analysis, seven (7) factors were identified as the emerging students’ difficulties. Further analyses reveal a moderate degree of seriousness for most of the components. However, the standard deviation suggests that the students' responses are spread across the measuring scale, indicating that the severity of difficulties experienced by other students is higher than moderate. Further comparison of such ratings shows that for students enrolled in advanced and major mathematics courses, difficulties emanating from inadequate learning materials and support, and difficulty in submitting requirements on time are more pronounced. These intertwined difficulties generally stem from the lack of planning and preparation, at the same time from the nature of mathematics as being complex, abstract, and notational. By considering these difficulties, adjustments may be prioritized by students and teachers in the hope of improving the current state of mathematics flexible learning. These improvements will eventually lead to sustainable and fully stable online academic programs that may be offered even after the pandemic.

Keywords

Flexible Learning Mathematics Principal Component Analysis Students’ Difficulties

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How to Cite
Sibaen, N. W., Buasen, J. A., & Alimondo, M. S. (2023). Principal components of students’ difficulties in mathematics in the purview of flexible learning. Journal on Mathematics Education, 14(2), 353–374. https://doi.org/10.22342/jme.v14i2.pp353-374
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References

  1. Aguilera-Hermida, A. P. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1. https://doi.org/10.1016/j.ijedro.2020.100011
  2. Akar, S. S., & Erden, M. K. (2021). Distance education experiences of secondary school math teachers during the pandemic: A narrative study. Turkish Online Journal of Distance Education, 22(3), 19–39.
  3. Alqurashi, E. (2016). Self-efficacy in online learning environments: A literature review. Contemporary Issues in Education Research-First Quarter, 9(1), 45–51. https://doi.org/10.19030/cier.v9i1.9549
  4. Apostol, T. (2000). Computer animated mathematics videotapes. In J. Borwein, M. H. Morales, K. P. Polthier, & J. F. Rodrigues (Eds.), Multimedia Tools for Communicating Mathematics: [presentations at an International Workshop MTCM2000, Organized at the Centro de Matemática E Aplicaçoes Fundamentais at the University of Lisbon, in November 2000] (p. 1). Springer Science & Business Media. https://bit.ly/3Ab2GTY
  5. Artigue, M. (2010). The future of teaching and learning mathematics with digital technologies. In C. Hoyles & J. B. Lagrange (Eds.), Mathematics education and technology-rethinking the terrain (pp. 463–475). Springer.
  6. Awang, Z. (2014). Research methodology and data analysis (2nd ed.). UiTM Press.
  7. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman and Company.
  8. 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
  9. Bates, R., & Khasawneh, S. (2007). Self-efficacy and college students’ perceptions and use of online learning systems. Computers in Human Behavior, 23(1), 175–191. https://doi.org/https://doi.org/10.1016/j.chb.2004.04.004
  10. Baticulon, R. E., Sy, J. J., Alberto, N. R. I., Baron, M. B. C., Mabulay, R. E. C., Rizada, L. G. T., Tiu, C. J. S., Clarion, C. A., & Reyes, J. C. B. (2021). Barriers to online learning in the time of COVID-19: A national survey of medical students in the Philippines. Medical Science Educator, 31(2), 615–626. https://doi.org/10.1007/s40670-021-01231-z
  11. Berge, Z. L., Collins, M., & Dougherty, K. (2000). Design guidelines for web-based courses. In B. Abbey (Ed.), Instructional and cognitive impacts of web-based education (pp. 32–40). IGI Global.
  12. Bringula, R., Batalla, Ma. Y., & Borebor, Ma. T. (2021). Modeling Computing Students’ Perceived Academic Performance in an Online Learning Environment. In Proceedings of the 22st Annual Conference on Information Technology Education, 99–104. https://doi.org/https://doi.org/10.1145/3450329.3476856
  13. Bro, R., & Smilde, A. K. (2014). Principal component analysis. In Analytical Methods (Vol. 6, Issue 9, pp. 2812–2831). Royal Society of Chemistry. https://doi.org/10.1039/c3ay41907j
  14. Cao, Y., Zhang, S., Chan, M. C. E., & Kang, Y. (2021). Post-pandemic reflections: Lessons from Chinese mathematics teachers about online mathematics instruction. Asia Pacific Education Review, 22(2), 157–168. https://doi.org/10.1007/s12564-021-09694-w
  15. Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. https://doi.org/https://doi.org/10.1207/s15327906mbr0102_10
  16. CHED. (2020a). CHED COVID-19 advisory no. 3. https://ched.gov.ph/wp-content/uploads/CHED-COVID-2019-Advisory-No.-3.pdf
  17. CHED. (2020b). CHED memorandum order no. 4. https://ched.gov.ph/wp-content/uploads/CMO-No.-4-s.-2020-Guidelines-on-the-Implementation-of-Flexible-Learning.pdf
  18. Cho, M.-H., & Heron, M. L. (2015). Self-regulated learning: The role of motivation, emotion, and use of learning strategies in students’ learning experiences in a self-paced online mathematics course. Distance Education, 36(1), 80–89. https://doi.org/https://doi.org/10.1080/01587919.2015.1019963
  19. Cho, Y., Avalos, J., Kawasoe, Y., Johnson, D., & Rodriguez, R. (2021). The impact of the COVID-19 pandemic on low-income households in the Philippines: Impending human capital crisis. https://data.humdata.org/dataset/global-school-closures-covid19
  20. Collis, B., Moonen, J., & Vingerhoets, J. (1997). Flexibility as a key construct in European training: Experiences from the TeleScopia project. British Journal of Educational Technology, 98, 199–217. https://doi.org/https://doi.org/10.1111/1467-8535.00026
  21. Cucinotta, D., & Vanelli, M. (2020). WHO declares COVID-19 a pandemic. Acta Biomedica, 91(1), 157–160. https://doi.org/10.23750/abm.v91i1.9397
  22. Department of Information and Communications Technology. (2019). National ICT Household Survey 2019. DICT. https://dict.gov.ph/ictstatistics/nicths2019/
  23. Duraku, Z. H., & Hoxha, L. (2021). The impact of COVID-19 on education and on the wellbeing of teachers, parents, and students: Challenges related to remote (online) learning and opportunities for advancing the quality of education. In M. Mala & L. Jemini-Gashi (Eds.), Impact of the COVID-19 Pandemic on Education and Wellbeing Implications for Practice and Lessons for the Future (pp. 17–39). University of Prishtina “Hasan Prishtina” Faculty of Philosophy, Department of Psychology.
  24. Dutta, S., & Smita, M. K. (2020). The impact of COVID-19 pandemic on tertiary education in Bangladesh: Students’ perspectives. Open Journal of Social Sciences, 08(09), 53–68. https://doi.org/10.4236/jss.2020.89004
  25. Fabito, B., Trillanes, A., & Sarmiento, J. (2021). Barriers and challenges of computing students in an online learning environment: Insights from one private university in the Philippines. International Journal of Computing Sciences Research, 5(1), 441–458. https://doi.org/10.25147/ijcsr.2017.001.1.51
  26. Ferla, J., Valcke, M., & Cai, Y. (2015). Academic self-efficacy and academic self-concept: Reconsidering structural relationships. Learning and Individual Differences, 19(4), 499–505. https://doi.org/https://doi.org/10.1016/j.lindif.2009.05.004
  27. Fhloinn, E. N., & Fitzmaurice, O. (2021). Challenges and opportunities: Experiences of mathematics lecturers engaged in emergency remote teaching during the covid-19 pandemic. Mathematics, 9(18). https://doi.org/10.3390/math9182303
  28. Freiman, V., Polotskaia, E., & Savard, A. (2017). Using a computer-based learning task to promote work on mathematical relationships in the context of word problems in early grades. ZDM: The International Journal on Mathematics Education, 49(6), 835–849. https://doi.org/https://doi.org/10.1007/s11858-017-0883-3
  29. Giatman, M., Siswati, S., & Basri, I. Y. (2020). Online learning quality control in the pandemic Covid-19 era in Indonesia. Journal of Nonformal Education, 6(2), 168–175. https://journal.unnes.ac.id/nju/index.php/jne/article/viewFile/25594/10736
  30. Glass, J., & Sue, V. (2008). Student preferences, satisfaction, and perceived learning in an online mathematics class. MERLOT Journal of Online Learning and Teaching, 4(3), 325–338. https://jolt.merlot.org/vol4no3/glass_0908.pdf
  31. Goode, S., Willis, R. A., Wolf, J. R., & Harris, A. L. (2007). Enhancing IS education with flexible teaching and learning. Journal of Information Systems Education, 18(3), 297–302. https://openresearch-repository.anu.edu.au/bitstream/1885/33727/2/01_Goode_Enhancing_IS_Education_with_2007.pdf
  32. Henaku, E. A. (2020). COVID-19 online learning experience of college students: The case of Ghana. International Journal of Multidisciplinary Sciences and Advanced Technology, 1(2), 54–62. https://www.researchgate.net/publication/342586709
  33. Hermanto, Rai, N. G. M., & Fahmi, A. (2021). Students’ opinions about studying from home during the COVID-19 pandemic in Indonesia. Cypriot Journal of Educational Sciences, 16(2), 499–510. https://doi.org/10.18844/CJES.V16I2.5627
  34. Herrington, J., Reeves, T. C., Oliver, R., & Woo, Y. (2004). Designing authentic activities in web-based courses. Journal of Computing in Higher Education, 16(1).
  35. Hodds, M. (2020). A report into the changes in Mathematics and Statistics support practices due to Acknowledgements. http://www.sigma-network.ac.uk/wp-content/uploads/2020/07/Report-into-the-changes-in-Maths-and-Stats-Support-practice-during-Covid-19.pdf
  36. Hodges, C. B., & Hunger, G. M. (2011). Communicating mathematics on the internet: Synchronous and asynchronous tools. TECHTRENDS TECH TRENDS, 55(5). https://doi.org/https://doi.org/10.1007/s11528-011-0526-4
  37. Hohenwarter, M., Jarvis, D., & Lavicza, Z. (2009). Discussion Paper Linking Geometry, Algebra, and Mathematics Teachers: GeoGebra Software and the Establishment of the International GeoGebra Institute. In International Journal for Technology in Mathematics Education (Vol. 16, Issue 2). http://www.geogebra.org/IGI/
  38. Huang, R., Tlili, A., Yang, J., & Chang, T.-W. (2020). Handbook on facilitating flexible learning during educational disruption: The Chinese experience in maintaining undisrupted learning in COVID-19 outbreak. Beijing: Smart Learning Institute of Beijing Normal University. https://www.researchgate.net/publication/339939064
  39. 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. https://doi.org/10.22460/infinity.v9i2.p147-158
  40. Jenkins, D. G., & Taber, T. D. (1977). A Monte Carlo study of factors affecting three indices of composite scale reliability. Journal of Applied Psychology, 62(4), 392–398. https://doi.org/10.1037/0021-9010.62.4.392
  41. Johns, C., & Mills, M. (2021). Online mathematics tutoring during the COVID-19 pandemic: Recommendations for best practices. PRIMUS, 31(1), 99–117. https://doi.org/10.1080/10511970.2020.1818336
  42. Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). Springer.
  43. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141–151. https://doi.org/https://doi.org/10.1177%2F001316446002000116
  44. Kim, K.-J., & Bonk, C. J. (2006). The future of online teaching and learning in higher education: The survey says... EDUCAUSE Quarterly, 29(4), 22–30. https://er.educause.edu/-/media/files/articles/2006/11/eqm0644.pdf?la=en&hash=CA40A2E5940EF94C7D580EAC7A32812194A994A2
  45. Lee, M. J. W., & McLoughlin, C. (2010). Beyond distance and time constraints: Applying social networking tools and Web 2.0 approaches to distance learning. In G. Veletsianos (Ed.), Emerging technologies in distance education (pp. 61–87). Athabasca University Press.
  46. Li, L., Liu, S., Peng, Y., & Sun, Z. (2016). Overview of principal component analysis algorithm. Optik, 127(9), 3934–3944. https://doi.org/https://doi.org/10.1016/j.ijleo.2016.01.033
  47. Lissitz, R. W., & Green, S. B. (1975). Effect of the number of scale points on reliability: A Monte Carlo approach. Journal of Applied Psychology, 60(1), 10–13. https://doi.org/10.1037/h0076268
  48. Mailizar, Almanthari, A., Maulina, S., & Bruce, S. (2020). Secondary school mathematics teachers’ views on e-learning implementation barriers during the COVID-19 pandemic: The case of Indonesia. Eurasia Journal of Mathematics, Science and Technology Education, 16(7). https://doi.org/10.29333/EJMSTE/8240
  49. McCoy, B. (2016). Digital distractions in the classroom phase II: Student classroom use of digital devices for non-class related purposes. Journal of Media Education, 7(1), 5–32. https://en.calameo.com/books/00009178915b8f5b352ba
  50. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97.
  51. Milrad, M., Wong, L.-H., Sharples, M., Hwang, G.-J., Looi, C.-K., & Ogata, H. (2013). Seamless learning: An international perspective on next generation technology enhanced learning. In Z. L. Berge & L. Y. Muilenburg (Eds.), Handbook of mobile learning (pp. 95–108). Routledge.
  52. Najdi, R. E. (2020). A Training Program on Mathematics Online Communication Skills to Overcome Barriers in Communicating Mathematics through Internet. Palest. J. Open Learn. e-Learning, 6, 20–35. https://platform.almanhal.com/Files/2/135137
  53. Naveed, Q. N., Muhammed, A., Sanober, S., Qureshi, M. R. N., & Shah, A. (2017). Barriers effecting successful implementation of E-learning in Saudi Arabian Universities. International Journal of Emerging Technologies in Learning, 12(6), 94–107. https://doi.org/10.3991/ijet.v12i06.7003
  54. Organization for Economic Cooperation and Development. (2001). Understanding the digital divide. http://www.oecd.org/dataoecd/38/57/1888451.pdf
  55. Pierce, R., & Ball, L. (2009). Perceptions that may affect teachers’ intention to use technology in secondary mathematics classes. Educational Studies in Mathematics, 71(3), 299–317.
  56. Pynos, R. R. (2016). Duquesne Scholarship Collection Student Engagement and Its Relationship to Mobile Device Ownership and the Role of Technology in Student Learning. https://dsc.duq.edu/etd/104
  57. Ramadhani, R., Sihotang, S. F., Bina, N. S., Rusmini, Harahap, F. S. W., & Fitri, Y. (2021). Undergraduate Students’ Difficulties in Following Distance Learning in Mathematics Based on E-Learning During the Covid-19 Pandemic. TEM Journal, 10(3), 1239–1247. https://doi.org/10.18421/TEM103-30
  58. Rotas, E. E., & Cahapay, M. B. (2020). Difficulties in remote learning: Voices of Philippine university students in the wake of COVID-19 crisis. Asian Journal of Distance Education, 15(2), 147–158. http://www.asianjde.com/ojs/index.php/AsianJDE/article/view/504
  59. Schumacker, R. E., & Lomax, R. G. (2010). A beginners guide to structural equation modeling. Routledge.
  60. Shahabadi, M. M., & Uplane, M. (2015). Synchronous and asynchronous e-learning styles and academic performance of e-learners. Procedia - Social and Behavioral Sciences, 176, 129–138. https://doi.org/10.1016/j.sbspro.2015.01.453
  61. Singh, A. S., & Masuku, M. B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of Economics, Commerce and Management, 2(11). http://ijecm.co.uk/
  62. Tasso, A. F., Hisli Sahin, N., & San Roman, G. J. (2021). COVID-19 disruption on college students: Academic and socioemotional implications. Psychological Trauma: Theory, Research, Practice, and Policy, 13(1), 9–15. https://doi.org/10.1037/tra0000996
  63. Verma, J. P. (2012). Application of Factor Analysis: To Study the Factor Structure Among Variables. In Data Analysis in Management with SPSS Software. Springer. https://doi.org/doi.org/10.1007/978-81-322-0786-3_11
  64. Voorst, V. (1999). Technology in mathematics teacher education. http://www.icte.org/T99_Library/T99_54.PDF
  65. Wadsworth, L. M., Husman, J., Duggan, M. A., & Pennington, M. N. (2007). Online mathematics achievement: Effects of learning strategies and self-efficacy. Journal of Developmental Education, 30(3), 6–14.
  66. World Bank. (2020). Philippines digital economy report 2020: A better normal under Covid-19: Digitalizing the Philippine economy now. https://bit.ly/3QMByA1
  67. World Bank. (2021). Impacts of COVID-19 on Communities in the Philippines Results from the Philippines High Frequency Social Monitoring of COVID-19 Impacts Round 2: April 8-14, 2021 About the survey. https://openknowledge.worldbank.org/handle/10986/36193
  68. Yohannes, Y., Juandi, D., Diana, N., & Sukma, Y. (2021). Mathematics teachers’ difficulties in implementing online learning during the COVID-19 pandemic. Journal of Hunan University (Natural Sciences), 48(5). http://www.jonuns.com/index.php/journal/article/view/581
  69. Zahara, Z., Kirilova, G. I., & Windarti, A. (2020). Impact of Corona Virus Outbreak Towards Teaching and Learning Activities in Indonesia. SALAM: Jurnal Sosial dan Baduya Syar-I, 7(3), 269–282. https://doi.org/https://doi.org/10.15408/sjsbs.v7i3.15104