Factor that Affects the Students’ Performance in Mathematics Subject using Logistic Regression
DOI:
https://doi.org/10.24191/jcrinn.v9i1.394Keywords:
Performance Prediction, Logistic Regression, prediction model, Student PerformanceAbstract
Predicting students' academic performance plays an important role in academics. Mathematics is a science concerned with the logic of shape, quantity, and order. This subject holds a crucial role in the school curriculum. Mathematics is a basic knowledge that students should have the expertise in order for them to score in the other subjects. However, most students find Mathematics a difficult subject, they will have difficulty scoring in this subject. Therefore, this paper aims to determine the factors that affect students’ performance in Mathematics subject which is the pre-calculus subject among students of Diploma in Computer Sciences (CS110) in UiTM Cawangan Melaka Kampus Jasin. During the analysis, gender, assessment marks, place of students, time spent studying pre-calculus subject per week, whether the student took additional mathematics in SPM, act as independent variables whereas grade for pre-calculus subject as the dependent variable, were examined. The assessments refer to test 1, test 2, lab assignment, quiz 1, quiz 2 and written assignment. The logistic regression model was applied, and the results showed that the gender, test 2 and quiz 2 are variables that are significant to the model with the p-value 0.031,0.014 and 0.03 respectively. When examining the variables influencing the academic performance of CS110 students at UiTM Cawangan Melaka Kampus Jasin, there are certain knowledge gaps and restrictions. It is advised that future studies should collect data from more respondents and keep the question simple but clear in order to get more accurate results.
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Adejumo, A. O., & Adetunji, A. A. (2013). Application of ordinal logistic regression in the study of students’ performance. Mathematical Theory and Modeling, 3(11), 10 - 20.
Arora, N., & Saini, J. R. (2014). Predicting student academic performance using fuzzy ARTMAP network. International Journal of Advances in Engineering Science and Technology, 187(September 2015).
Ashaari, N. S., Judi, H. M., Mohamed, H., & Tengku Wook, T. M. (2011). Student’s attitude towards statistics course. Procedia - Social and Behavioral Sciences, 18. https://doi.org/10.1016/j.sbspro.2011.05.041
Bakar, S. A., Ayub, A. F. M., Gopal, K., & Salim, N. R. (2019). The influence of students’ beliefs on mathematical problem solving towards mathematics achievement among Malaysian matriculation students. Universal Journal of Educational Research, 7(10). https://doi.org/10.13189/ujer.2019.071025
Chakraborty, S., Hoque, S. M. A., & Kabir, S. M. F. (2020). Predicting fashion trend using runway images: application of logistic regression in trend forecasting. International Journal of Fashion Design, Technology and Education, 13(3). https://doi.org/10.1080/17543266.2020.1829096
Díaz-Pérez, M., Carreño-Ortega, Á., Salinas-Andújar, J. A., & Callejón-Ferre, Á. J. (2019). Application of logistic regression models for the marketability of cucumber cultivars. Agronomy, 9(1). https://doi.org/10.3390/agronomy9010017
Farooq, M. S., Chaudhry, a H., Shafiq, M., & Berhanu, G. (2011). Factors affecting students’ quality of academic performance: A case of secondary school level. Journal of Quality and Technology Management, VII(II).
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley & Sons. http://search.lib.virginia.edu/catalog/ocn830163779
Iddrisu, A. B., Bornaa, C. S., Alagbela, A. A., Kwakye, D. O., Gariba, A., Ahusah, E. T.-W., & Badger, T. N. A. (2023). Students’ characteristics and academic performance in Mathematics. Journal of Education, Society and Behavioural Science. https://doi.org/10.9734/jesbs/2023/v36i31214
Jaafar, Z., Ali, R., Ahmad, S. N. D., Ruslan, S. Z. M., & Othman, N. (2021). Logistic regression in analyzing the determinants of university students’ mathematics performance. Mathematical Sciences and Informatics Journal. https://doi.org/10.24191/mij.v2i2.14977
Jin, R. (2015). Application of logistic regression model in an epidemiological study. Science Journal of Applied Mathematics and Statistics, 3(5). https://doi.org/10.11648/j.sjams.20150305.12
Kuppusamy, S., & Musa, M. (2021). Investigating international school secondary students ’ attitude towards Mathematics. Jurnal Pendidikan Sains Dan Matematik Malaysia, 11(2), 122–130.
Mahmud, N., Muhammat Pazil, N. S., & Azman, N. A. N. (2022). The significant factors affecting students’ academic performance in online class: Multiple linear regression approach. Jurnal Intelek, 17(2), 1–11. https://doi.org/10.24191/ji.v17i2.17896
Nofriyandi, & Andrian, D. (2022). Factors that affect students’ mathematics performance at higher education in Riau province during the Covid-19 pandemic. Infinity Journal. https://doi.org/10.22460/infinity.v11i2.p367-380
Omar, S. H., Aris, S. R. S., & Hoon, T. S. (2022). Mathematics anxiety and its relationship with mathematics achievement among secondary school students. Asian Journal of University Education, 18(4), 863–878. https://doi.org/10.24191/ajue.v18i4.19992
Patena, A. D. C., & Dinglasan, B. (2013). Students performance on departmental examination: Basis for math intervention program. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2276044
Porter, L., Zingaro, D., & Lister, R. (2014). Predicting student success using fine grain clicker data. ICER 10th Annual International Conference on International Computing Education Research (pp.51-58). ACM. https://doi.org/10.1145/2632320.2632354
Ramosacaj, M., Hasani, V., & Dumi, A. (2015). Application of logistic regression in the study of students’ performance level (case study of Vlora University). Journal of Educational and Social Research. https://doi.org/10.5901/jesr.2015.v5n3p239
Rodríguez, S., Regueiro, B., Piñeiro, I., Estévez, I., & Valle, A. (2020). Gender Differences in Mathematics Motivation: Differential Effects on Performance in Primary Education. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2019.03050
Shedriko. (2021). Binary logistic regression in determining affecting factors student graduation in a subject. Jurnal Teknologi & Open Source, 4(1), 114–120. https://doi.org/10.36378/jtos.v4i1.1401
Soule, P. (2017). Predicting student success : A logistic regression analysis of data from multiple SIU-C courses. Fall, 1–24.
Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia Medica, 24(1). https://doi.org/10.11613/BM.2014.003
Syuhada, N., Pazil, M., Mahmud, N., Baharom, N., & Hafawati, S. (2023). Logistic regression in determining affecting factors student success in an introductory statistics subject. Jurnal Intelek, 18(1), 9–16. https://doi.org/https://doi.org/10.24191/ji.v18i1.20133
Zou, X., Hu, Y., Tian, Z., & Shen, K. (2019). Logistic regression model optimization and case analysis. 7th IEEE 7th International Conference on Computer Science and Network Technology, ICCSNT 2019 (pp.135-139). IEEE Xplore. https://doi.org/10.1109/ICCSNT47585.2019.8962457
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Copyright (c) 2024 Norwaziah Mahmud, Nur Syuhada Muhammat Pazil, Nur Syakirah Rosli, Siti Hafawati Jamaluddin (Author)
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