Leveraging artificial neural networks to predict graduation rates in diploma computer science programs
List of Authors
  • Adeline Engkamat , Yii Ming Leong

Keyword
  • Graduate-on-Time, Artificial Neural Network Classification, Prediction, Self-Organizing Map

Abstract
  • Ensuring that students graduate on time (GOT) is a major hurdle for many universities, with over 80% GOT rates remaining elusive. Numerous research efforts, using statistical and machine learning approaches, have aimed to identify factors impacting timely graduation. Despite this, a comprehensive visualization of students' GOT trajectories has been a missing piece. This study addresses this gap by developing a predictive model that visually maps students' paths towards GOT. By projecting multifaceted academic data onto 2D planes, the model achieves impressive prediction accuracy ranging from 78.32% to 97.06%. This new tool empowers lecturers and the university's management to quickly identify students at risk of falling off track so that more precise preventive measures could be implemented to help these students achieve GOT.

Reference
  • 1. Ahmad Saleh, A. K. (2014). Time Management and Its Relation to Students' Stress, Gender and Academic Achievement among Sample of Students at Al Ain University of Science and Technology, UAE. International Journal of Business and Social Research (IJBSR), 4(5). 47-58. https://doi.org/10.18533/ijbsr.v4i5.498

    2. Ajibade, S. S., & Adediran, A. (2016). An overview of big data visualization techniques in data mining. International Journal of Computer Science and Information Technology Research, 4(3), 105- 113.

    3. Almgren, K. (2014). Using Business Intelligence to Mitigate Graduation Delay Issues. International Journal of Applied Science and Technology, 4(3). 94-100. doi:10.30845/ijast

    4. Ariani, N. K. D., Sumarjaya, I. W., & Oka. T. B. (2013). Analisis Faktor-Faktor Yang Mempengaruhi Waktu Kelulusan Mahasiswa Dengan Menggunakan Metode Gompit (Studi Kasus: Mahasiswa Fakultas MIPA Universitas Udayana). E-Jurnal Matematika, 2(3), 40-45. https://doi.org/10.24843/MTK.2013.v02.i03.p047

    5. Center, I. I. (2013). Big Data Visualization: Turning Big Data Into Big Insights. White Paper, 1-14.

    6. Deepika, K., & Prema, N. (2017). Peer Pressure in Relation to Academic Achievement of Deviant Students.

    7. International Journal of Environmental & Science Education, 12(8), 1931-1943. E-ISSN: 1306-3065

    8. Gurbuz, O., & Serkan, B. (2016). Examination of the Relation between Academic Procrastination and Time Management Skills of Undergraduate Students in Terms of Some Variables. Journal of Education and Training Studies, 4(5).76-84. doi:10.11114/jets.v4i5.1313

    9. Hervianti, S.P. & Haryatmi, E. (2021). C4.5 Algorithm Application for Student Graduation Data Analysis of Darma Persada University, International Research Journal of Advanced Engineering and Science, 6(1), 169-171.

    10. Jumadi, A., Yusop, H., Mokhtar, M., Abu, B. N. F., & Abu, B. N. (2020). Graduate on Time: Factors of Failure in UiTM Cawangan Johor. The Asian Journal of Professional and Business Studies, 1(1). E-ISSN: 2716-666X

    11. Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464-1480.

    12. Kristanto, S. P., Bahtiar, R. S., Sembiring, M., Himawan, H., Samboteng, L., Hariyadi, & Suparya, K. (2021). Implementation of ML Rough Set in Determining Cases of Timely Graduation of Students. Journal of Physics: Conference Series, 1933(2021), 1-8. doi:10.1088/1742-6596/1933/1/012031

    13. Letkiewicz, J., Lim, H., Heckman, S., Bartholomae, S., Fox, J., & Montalto, C. P. (2014). The Path to Graduation: Factors Predicting On-Time Graduation Rates. Journal of College Student Retention: Research, Theory, & Practice, 16(3), 351-371. doi:10.2190/CS.16.3.c

    14. Liu, S., Maljovec, D., Wang, B., Bremer, P. T., & Pascucci, V. (2016). Visualizing high-dimensional data: Advances in the past decade. IEEE transactions on visualization and computer graphics, 23(3), 1249- 1268.

    15. Malaysian Qualification Agency(MQA), (2017). Malaysian Qualifications Framework (MQF), 2nd Edition. https://www.mqa.gov.my/pv4/document/mqf/2019/oct/updated%20mqf%20ed%202%2024102019.p df

    16. Marliana, B. W., Umi, N., Suwinarno, N., Hendar, R., & Syahrul, M. (2020). Algorithm Design to Determine Possibility of Student Graduate Time in Student Grade Recapitulation Application. International Journal of Advanced Computer Science and Applications, 11(12), 409-414. doi:10.14569/IJACSA.2020.0111250

    17. Maulana, A. (2021). Prediction of student graduation accuracy using decision tree with application of genetic algorithms. In IOP Conference Series: Materials Science and Engineering, 1073(1). 012055. IOP Publishing.

    18. Merkl, D., & Rauber, A. (1997a). Alternative ways for cluster visualization in self-organizing maps. In Proceedings of the Workshop on Self-Organizing Maps (WSOM97) (pp. 4-6).

    19. Miqdadi, F. Z., ALMomani, A. F., Mohammad, T., Nabil, M., & Elmousel, N. M. (2014). The Relationship Between Time Management and the Academic Performance of Students from the Petroleum Institute in Abu Dhabi, the UAE, ASEE 2014 Zone I Conference (pp. 1-5). University of Bridgeport, Bridgeport, CT, USA.

    20. Mosha, M. A. (2017). The Influence of Peer Group on Academic Performance of Adolescent Students in Secondary Schools in Tanzania. Research Journal of Educational Studies and Review, 3(1). 18-26. ISSN: 2449-1837

    21. Muliono, R., Lubis, J. H., & Khairina, N. (2020). Analysis K-Nearest Neighbor Algorithm for Improving Prediction Student Graduation Time. Sinkron: jurnal dan penelitian teknik informatika, 4(2), 42-46.

    22. Noercholis, A., & Zainuddin, M. (2020). Comparative Analysis of 5 Algorithm Based Particle Swarm Optimization (PSO) for Prediction of Graduate Time Graduation. Jurnal MATICS Volume, 12(1).

    23. Purwinarko, A., Hardyanto, W., & Aryani, N. P. (202). Academic achievement analysis of Universitas Negeri Semarang students using the Naïve Bayes classifier algorithm. In Journal of Physics: Conference Series, 1918(4). 042130). IOP Publishing.

    24. Sudipa, I. G. I., Wijaya, I. N. S. W., Radhitya, M. L., Mahawan, I. M. A., & Arsana, I. N. A. (2020). An android- based application to predict student with extraordinary academic achievement. In Journal of Physics: Conference Series, 1469(1). 012043. IOP Publishing.

    25. Teh, C. S., Yii, M. L., & Chen, C. J. (2009, December). An Artificial Neural Network Model for Multi Dimension Reduction and Data Structure Exploration. In 2009 International Conference of Soft Computing and Pattern Recognition (pp. 254-258). IEEE.

    26. Wirawan, C., Khudzaeva, E., Hasibuan, T. H., & Lubis, Y. H. K. (201). Application of Data mining to Prediction of Timeliness Graduation of Students (A Case Study). In 2019 7th International Conference on Cyber and IT Service Management (CITSM), 7. 1-4. IEEE.

    27. Yanto, I. T. R., Sutoyo, E., Rahman, A., Hidayat, R., Ramli, A. A., & Fudzee, M. F. M. (2020). Classification of Student Academic Performance using Fuzzy Soft Set. In 2020 International Conference on Smart Technology and Applications (ICoSTA). 1-6. IEEE.

    28. Yii, M. L., & Teh, C. S. (2017). A Novel Triangulate Mapping Based on Self-Organized Anchor Points for Data Visualization. Advanced Science Letters, 23(11), 11083-11087.