Analysis of Summative Pre-Calculus Assessment for Computer Science Students

In foundational courses like Pre-Calculus, summative assessment is a common method to evaluate student learning in mathematics. Many students were seen struggling to pass the Pre-Calculus course, especially students with an inadequate SPM-level mathematics. Although educators are aware of the challenges faced by these students, few studies have investigated their performance patterns in the summative assessment. This study aims to analyse students’ performance in the Pre-Calculus summative assessment by examining the distribution of marks and the questions students choose to attempt. The summative assessment is an individual written final examination comprising 25 questions on various topics in this course. Data from the answer scripts of 32 repeat students were collected and analysed using descriptive analysis. Results show that all students attempted questions on inequalities, complex numbers, and systems of linear equations, with the median scores being higher than the average scores. Conversely, students performed poorly on questions on trigonometry, suggesting that the topic is challenging. Despite the limited sample size and scope, this study lays the groundwork for curriculum assessment in the Pre-calculus course. More broadly, this move helps to strengthen mathematical learning and contribute to the overall improvement of STEM education.

Mathematics education, Pre-calculus course

1. Abdulah, N. N., Bahari, S. S. F., & Mahmud, M. S. (2025). Perspectives and challenges in distance learning of mathematics: A survey among mathematics students in public tertiary education. Eurasia Journal of Mathematics, Science and Technology Education, 21(8), em2683. [Google Scholar] [Crossref]

2. Adnan, N. I. M., Mohamed, A. S. T., Azami, M. F. A. M., & Razali, F. A. (2021, May). Multiple linear regression of Asia Pacific University Malaysia students’ performance in statistics and mathematics course using R software. In AIP Conference Proceedings (Vol. 2355, No. 1, p. 060006). AIP Publishing LLC. [Google Scholar] [Crossref]

3. Buratta, L., Piccirilli, M., Lanfaloni, G. A., Ilicini, S., Bedetti, C., & Elisei, S. (2019). Mathematics anxiety and cognitive performance in adolescent students. Psychiatria Danubina, 31(suppl 3), 479-485. [Google Scholar] [Crossref]

4. Dogan, M. (2011). Student teachers' views about assessment and evaluation methods in mathematics. Educational Research and Reviews, 6(5), 417. [Google Scholar] [Crossref]

5. Iannone, P., & Simpson, A. (2022). How we assess mathematics degrees: the summative assessment diet a decade on. Teaching Mathematics and its Applications: An International Journal of the IMA, 41(1), 22-31. [Google Scholar] [Crossref]

6. Idris, N. (2006). Exploring the effects of TI-84 plus on achievement and anxiety in mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 2(3), 66-78. [Google Scholar] [Crossref]

7. Ismail, N. A., & Awang, H. (2012). Student factors and mathematics achievement: Evidence from TIMSS 2007. Eurasia Journal of Mathematics, Science and Technology Education, 8(4), 249-255. [Google Scholar] [Crossref]

8. Kamal, N., Arsad, N., Husain, H., & Nopiah, Z. M. (2015). The relationships between pre-university education and mathematics achievement with performance in engineering subject. Journal of Engineering Science and Technology, 10, 10-17. [Google Scholar] [Crossref]

9. Kuznetsova, E. (2019). Evaluation and interpretation of student satisfaction with the quality of the university educational program in applied mathematics. Teaching Mathematics and its Applications: An International Journal of the IMA, 38(2), 107-119. [Google Scholar] [Crossref]

10. Khoo, N.A., Jamaluddin, N.Y., Osman, S., & Buchori, A. (2024). Exploring the interaction between learning styles and mathematics anxiety among secondary school students: A correlational study in southern Malaysia. Journal of Technology and Science Education, 14(3), 683-700. https://doi.org/10.3926/jotse.2224 [Google Scholar] [Crossref]

11. Ling, L. S., & Krishnasamy, S. (2023). Information technology capability (ITC) framework to improve learning experience and academic achievement of mathematics in Malaysia. Electronic Journal of e-Learning, 21(1), 36-51. [Google Scholar] [Crossref]

12. Lishchynska, M., Palmer, C., Lacey, S., & O’Connor, D. (2023). Is motivation the key? Factors impacting performance in first year service mathematics modules. European Journal of Science and Mathematics Education, 11(1), 146-166. [Google Scholar] [Crossref]

13. Mukuka, A., & Tatira, B. (2025). Analysis of preservice teachers’ understanding of solving trigonometric equations: A perspective through actions, processes, objects, and schemas theory. Pythagoras-Journal of the Association for Mathematics Education of South Africa, 46(1), 830. [Google Scholar] [Crossref]

14. Nortvedt, G. A., & Buchholtz, N. (2018). Assessment in mathematics education: responding to issues regarding methodology, policy, and equity. ZDM Mathematics Education, 50(4), 555-570. [Google Scholar] [Crossref]

15. 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. [Google Scholar] [Crossref]

16. Rohimah, S. M., & Prabawanto, S. (2020). Students’ difficulties in solving trigonometric equations and identities. In Journal of Physics: Conference Series (Vol. 1521, No. 3, p. 032002). IOP Publishing. [Google Scholar] [Crossref]

17. Sergejeva, N., & Zeidmane, A. (2023). Factors influencing student initiative in effective mathematics studies at universities. In S. Treija (Ed.), Research for Rural Development 2023: Annual 29th International Scientific Conference Proceedings (Vol. 38, pp. 291–298). Latvia University of Life Sciences and Technologies. [Google Scholar] [Crossref]

18. Usman, M. H., & Hussaini, M. M. (2017). Analysis of students’ error in learning of trigonometry among senior secondary school students in Zaria Metropolis, Nigeria. IOSR Journal of Mathematics, 13(2), 1–4. [Google Scholar] [Crossref]

19. Wei, L. Y., Gabda, D., Ping, N. P. T., & Mun, H. C. (2025). Challenges and Opportunities: A Comparison of PISA 2022 Mathematics Performance in Malaysia and Other ASEAN Countries. Mathematics Teaching Research Journal, 17(3), 201-222. [Google Scholar] [Crossref]

20. Zakaria, E., Zain, N. M., Ahmad, N. A., & Erlina, A. (2012). Mathematics anxiety and achievement among secondary school students. American Journal of Applied Sciences, 9(11), 1828. [Google Scholar] [Crossref]

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