Analysis of Students' Academic Performance in the Department of Mathematics Based on Semester GPA Dynamics: A Case Study of the 2017–2024 Cohorts

Shafa Khadijah Rahmat; Sarini Abdullah

doi:10.31764/jtam.v9i3.30287

Analysis of Students' Academic Performance in the Department of Mathematics Based on Semester GPA Dynamics: A Case Study of the 2017–2024 Cohorts

Shafa Khadijah Rahmat, Sarini Abdullah

Abstract

This quantitative exploratory study investigates changes in students' Semester Grade Point Average (GPA) and their relationship with graduation status and study duration. It uses academic records from the Department of Mathematics at a public university in Indonesia for cohorts from 2017 to 2024. The study addresses concerns raised after the COVID-19 pandemic, which may have disrupted academic progression and altered the predictive power of initial GPA on graduation outcomes a gap not sufficiently explored in existing literature. Data were collected directly from the university's academic database, ensuring accuracy and consistency without relying on self-reported surveys. Descriptive statistical methods and visual analytics (e.g., line charts, boxplots, and scatter plots) were applied to uncover trends and patterns. Results show that earlier cohorts (2017–2020) have high graduation rates (82.7%–94.4%), while the 2019 cohort recorded the highest dropout rate (11.1%). Newer cohorts (2021–2024) predominantly consist of students still enrolled, though some early graduations and dropouts occurred. A positive correlation was found between first-semester GPA and graduation success, yet the pandemic likely introduced new variables that affect academic outcomes. These findings provide actionable insights for academic policy and support the development of early detection systems to identify students at academic risk.

Keywords

Academic Success; Data Exploration; Data Visualization; GPA.

Full Text:

DOWNLOAD [PDF]

References

Alghamdi, A. A., Alyousif, G. F., AlQarni, A. M., Amer, F. H., Alfadhel, T. O., Almutairi, R. N., Almutairi, S. M., Almutairi, A. D., Hakami, N. A., & Al Ghamdi, K. (2024). Factors affecting Saudi medical students’ engagement during synchronous and asynchronous eLearning and their impacts on the students’ academic achievement: a national survey. BMC Medical Education, 24(1), 1–13. https://doi.org/10.1186/s12909-024-05323-3

Amalita, N., Fitria, D., & Oktriatama, R. (2020). Analysis of Student’s Performance Index Using Confirmatory Analysis. Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019. https://doi.org/10.4108/eai.12-10-2019.2296279

Atti, A., Kleden, M. A., & Lobo, M. (2021). Prediksi Lama Masa Studi Mahasiswa Program Studi Matematika Berdasarkan IPK. Jurnal Penelitian Pembelajaran Matematika, 14(1), 114–120.

Binelli, C., Comi, S., Meschi, E., & Pagani, L. (2024). Every cloud has a silver lining: The role of study time and class recordings on university students’ performance during COVID-19. Journal of Economic Behavior and Organization, 225(2024), 305–328. https://doi.org/10.1016/j.jebo.2024.07.012

Bravo-Agapito, J., Romero, S. J., & Pamplona, S. (2021). Early prediction of undergraduate Student’s academic performance in completely online learning: A five-year study. Computers in Human Behavior, 115(October 2020), 106595. https://doi.org/10.1016/j.chb.2020.106595

Cheung, K., Ng, J., Tsang, H., & Pang, K. W. (2021). Factors predicting vertical transfer students’ GPA and degree attainment in an Asian educational context. Studies in Educational Evaluation, 70(August 2020), 101041. https://doi.org/10.1016/j.stueduc.2021.101041

Delogu, M., Lagravinese, R., Paolini, D., & Resce, G. (2024). Predicting dropout from higher education: Evidence from Italy. Economic Modelling, 130(October 2023). https://doi.org/10.1016/j.econmod.2023.106583

Di Pietro, G. (2023). The Impact of Covid-19 on Student Achievement: Evidence From a Recent Meta-Analysis. Educational Research Review, 39(March), 100530. https://doi.org/10.1016/j.edurev.2023.100530

Jannah, M. (2023). Prediksi Indeks Prestasi Semester (IPS) Mahasiswa Pendidikan Teknik Informatika dan Komputer Universitas Negeri Jakarta Selama Pembelajaran Daring Menggunakan Algoritma Naive Bayes [Universitas Negeri Jakarta]. http://repository.unj.ac.id/id/eprint/39819

Kapoor, H., Patston, T., Cropley, D. H., Rezaei, S., & Kaufman, J. C. (2025). Creativity predicts standardized educational outcomes beyond GPA and personality. Thinking Skills and Creativity, 56(December 2024). https://doi.org/10.1016/j.tsc.2024.101751

Khaled, A., Almaghaslah, D., Siddiqua, A., Kandasamy, G., & Orayj, K. (2025). Impact of sleep quality on academic achievements of undergraduate medical students: a cross-sectional study from Saudi Arabia. BMC Medical Education, 25(1), 59. https://doi.org/10.1186/s12909-025-06664-3

Lalin, S. A. A., Ahmed, M. N. Q., & Haq, S. M. A. (2024). The Effects of The COVID-19 Pandemic On Students’ Academic Performance and Mental Health: An Overview. Regional Science Policy and Practice, 16(7), 100046. https://doi.org/10.1016/j.rspp.2024.100046

Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systemic literature review of empirical evidence. Educational Technology and Society, 17(4), 49–64. https://www.jstor.org/stable/jeductechsoci.17.4.49

Pöysä, S., Poikkeus, A. M., Muotka, J., Vasalampi, K., & Lerkkanen, M. K. (2020). Adolescents’ engagement profiles and their association with academic performance and situational engagement. Learning and Individual Differences, 82(May), 101922. https://doi.org/10.1016/j.lindif.2020.101922

Ren, Z., & Du, Q. (2024). The Influence of COVID-19 Pandemic on College Students’ Academic Performance and The Construction of a Learning Ability Warning System. Frontiers in Public Health, 12(December), 1–13. https://doi.org/10.3389/fpubh.2024.1454406

Romero, C., & Ventura, S. (2020). Educational Data Mining and Learning Analytics: An Updated Survey. WIRES DATA MINING AND KNOWLEDGE DISCOVERY, 10(3). https://doi.org/https://doi.org/10.1002/widm.1355

Santos, R. M., & Henriques, R. (2023). Accurate, timely, and portable: Course-agnostic early prediction of student performance from LMS logs. Computers and Education: Artificial Intelligence, 5(October), 100175. https://doi.org/10.1016/j.caeai.2023.100175

Schriek, J., Carstensen, B., Soellner, R., & Klusmann, U. (2024). Pandemic rollercoaster: University students’ trajectories of emotional exhaustion, satisfaction, enthusiasm, and dropout intentions pre-, during, and post-COVID-19. Teaching and Teacher Education, 148(July), 104709. https://doi.org/10.1016/j.tate.2024.104709

Shukor, N. A., Tasir, Z., & Van der Meijden, H. (2015). An Examination of Online Learning Effectiveness Using Data Mining. Procedia - Social and Behavioral Sciences, 172(January), 555–562. https://doi.org/10.1016/j.sbspro.2015.01.402

Sinval, J., Oliveira, P., Novais, F., Almeida, C. M., & Telles-Correia, D. (2024). Exploring the impact of depression, anxiety, stress, academic engagement, and dropout intention on medical students’ academic performance: A prospective study. Journal of Affective Disorders, 368(September 2024), 665–673. https://doi.org/10.1016/j.jad.2024.09.116

Soto-Ferrari, M., Layman, A., & Young, J. A. (2024). Academic performance in the wake of COVID-19: A comparative study of first-year college business students. International Journal of Management Education, 22(3), 101078. https://doi.org/10.1016/j.ijme.2024.101078

Tran, V. D., Nguyen, K. S. T., & Le, D. N. (2025). Dataset of factors affecting learning outcomes of students at the University of Education, Vietnam National University, Hanoi. Data in Brief, 59, 111438. https://doi.org/10.1016/j.dib.2025.111438

Wahyuning, C. S., Ramadhani, F. T., Rahmani, D. S., & Moerti, A. T. (2024). Study on Sleep Quality of Students in Regards with Cognitive Capabilities and Academic Achievement Post COVID-19 Pandemic. E3S Web of Conferences, 484, 1–8. https://doi.org/10.1051/e3sconf/202448401009

Yelfera, S. R., Yozza, H., & HG, I. R. (2022). Faktor-Faktor Yang Mempengaruhi Masa Studi Dan Indeks Prestasi Kumulatif Mahasiswa Dengan Model Regresi Logistik Biner Bivariat. Jurnal Matematika UNAND, 11(1), 82. https://doi.org/10.25077/jmu.11.1.82-94.2022

Zhao, S., & Akhter, S. (2023). Negative psychological and educational impacts of Corona Virus Anxiety on Chinese university students: Exploring university students’ perceptions. Heliyon, 9(10), e20373. https://doi.org/10.1016/j.heliyon.2023.e20373

DOI: https://doi.org/10.31764/jtam.v9i3.30287