A Machine Learning Approach to Predicting On-Time Graduation in Indonesian Higher Education

Mahendra Astu Sanggha Pawitra, National Central University, Taiwan, Province of China
Hui-Chun Hung, National Central University, Taiwan, Province of China
Handaru Jati, Universitas Negeri Yogyakarta, Indonesia

 10.21831/elinvo.v9i2.77052

Abstract


Graduating on schedule is a critical milestone for students in higher education, serving as a key indicator of both institutional effectiveness and student success. This study uses machine learning techniques to predict on-time graduation in Indonesian higher education. A dataset comprising 133 students from an engineering department over four academic years (2019–2023) was analyzed using the CRISP-DM framework. The research employed nine machine learning models, including Random Forest, Logistic Regression, Neural Networks, etc., to identify key predictors of on-time graduation. The result showed that Random Forest outperformed other models by achieving an accuracy of 85% and an AUC of 0.875. Additionally, the study developed a learning analytics dashboard to visualize predictive insights, offering actionable data for educators and administrators. The system’s performance was evaluated based on functionality, usability, efficiency, and reliability as the key intersecting factors from ISO/IEC 25010 and WebQEM frameworks, validating its quality and relevance for practical educational use. The result demonstrated high functionality, efficiency, and reliability, and positive usability feedback was received from both students and educators. The findings highlight the top ten important factors, such as cumulative GPA (CGPA), extracurricular involvement, programming, and social science courses, that predict on-time graduation, providing valuable insights for enhancing student outcomes in Indonesian higher education.

Keywords


On-time graduation; educational data mining; institutional research; learning analytics dashboard; machine learning

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References


D. Bañeres, M. E. Rodríguez, A. E. Guerrero-Roldán, and A. Karadeniz, “An Early Warning System to Detect At-Risk Students in Online Higher Education,” Applied Sciences, vol. 10, no. 13, p. 4427, Jun. 2020, doi: 10.3390/app10134427.

T.-C. Yang, Y.-L. Liu, and L.-C. Wang, “Using an Institutional Research Perspective to Predict Undergraduate Students’ Career Decisions in the Practice of Precision Education,” Educational Technology & Society, vol. 24, no. 1, pp. 280–296, 2021.

R. Maqsood, P. Ceravolo, M. Ahmad, and M. S. Sarfraz, “Examining students’ course trajectories using data mining and visualization approaches,” Int J Educ Technol High Educ, vol. 20, no. 1, p. 55, Oct. 2023, doi: 10.1186/s41239-023-00423-4.

S.-C. Tsai, C.-H. Chen, Y.-T. Shiao, J.-S. Ciou, and T.-N. Wu, “Precision education with statistical learning and deep learning: a case study in Taiwan,” Int J Educ Technol High Educ, vol. 17, no. 1, p. 12, Dec. 2020, doi: 10.1186/s41239-020-00186-2.

K. E. Arnold and M. D. Pistilli, “Course signals at Purdue: using learning analytics to increase student success,” in Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver British Columbia Canada: ACM, Apr. 2012, pp. 267–270. doi: 10.1145/2330601.2330666.

G. Akçapınar, A. Altun, and P. Aşkar, “Using learning analytics to develop early-warning system for at-risk students,” Int J Educ Technol High Educ, vol. 16, no. 1, p. 40, Dec. 2019, doi: 10.1186/s41239-019-0172-z.

C.-H. Chen, S. J. H. Yang, J.-X. Weng, H. Ogata, and C.-Y. Su, “Predicting at-risk university students based on their e-book reading behaviours by using machine learning classifiers,” AJET, pp. 130–144, Jun. 2021, doi: 10.14742/ajet.6116.

Y. F. Martak and C. Chotib, “Rate of Return on Education in Indonesia: The Privilege of A High Economic Group and Urban Areas,” JEP, vol. 22, no. 1, pp. 54–59, Jul. 2021, doi: 10.23917/jep.v22i1.13006.

A. T. Wibowo and D. Fitrianah, “A K-NEAREST ALGORITHM BASED APPLICATION TO PREDICT SNMPTN ACCEPTANCE FOR HIGH SCHOOL STUDENTS IN INDONESIA,” International Research Journal of Computer Science, vol. 5, no. 01, 2018.

K. S. Diasti and C. L. Mbato, “Exploring Undergraduate Students’ Motivation-regulation Strategies in Thesis Writing,” LC, vol. 14, no. 2, pp. 176–183, Apr. 2020, doi: 10.15294/lc.v14i2.23450.

Kementrian Pendidikan dan Kebudayaan, “Analitik PTNBH - Direktorat Kelembagaan Kemdikbud.” Accessed: Jul. 21, 2024. [Online]. Available: https://sinta.kemdikbud.go.id/ptnbhanalytics/v2/affiliations/detail/430

Direktorat Jenderal Pendidikan Tinggi Kementerian Pendidikan dan Kebudayaan, Buku Panduan Merdeka Belajar - Kampus Merdeka. Jakarta: Direktorat Jenderal Pendidikan Tinggi Kemendikbud RI, 2020.

D. Defrizal, A. P. Redaputri, V. T. Narundana, N. Nurdiawansyah, and Y. Y. Dharmawan, “The Merdeka Belajar Kampus Merdeka Program: An Analysis of the Success Factors,” njpi, vol. 2, no. 1, pp. 123–140, Jan. 2022, doi: 10.14421/njpi.2022.v2i1-8.

H.-C. Hung, I.-F. Liu, C.-T. Liang, and Y.-S. Su, “Applying Educational Data Mining to Explore Students’ Learning Patterns in the Flipped Learning Approach for Coding Education,” Symmetry, vol. 12, no. 2, p. 213, Feb. 2020, doi: 10.3390/sym12020213.

J. Munir, M. Faiza, B. Jamal, S. Daud, and K. Iqbal, “The Impact of Socio-economic Status on Academic Achievement,” JSSR, vol. 3, no. 2, pp. 695–705, Jun. 2023, doi: 10.54183/jssr.v3i2.308.

T. Purwoningsih, H. B. Santoso, and Z. A. Hasibuan, “Data Analytics of Students’ Profiles and Activities in a Full Online Learning Context,” in 2020 Fifth International Conference on Informatics and Computing (ICIC), Gorontalo, Indonesia: IEEE, Nov. 2020, pp. 1–8. doi: 10.1109/ICIC50835.2020.9288540.

F. Marbouti, J. Ulas, and C.-H. Wang, “Academic and Demographic Cluster Analysis of Engineering Student Success,” IEEE Trans. Educ., vol. 64, no. 3, pp. 261–266, Aug. 2021, doi: 10.1109/TE.2020.3036824.

C. Herodotou, M. Hlosta, A. Boroowa, B. Rienties, Z. Zdrahal, and C. Mangafa, “Empowering online teachers through predictive learning analytics,” Brit J Educational Tech, vol. 50, no. 6, pp. 3064–3079, Nov. 2019, doi: 10.1111/bjet.12853.

S. R. Rahman, Md. A. Islam, P. P. Akash, M. Parvin, N. N. Moon, and F. N. Nur, “Effects of co-curricular activities on student’s academic performance by machine learning,” Current Research in Behavioral Sciences, vol. 2, p. 100057, Nov. 2021, doi: 10.1016/j.crbeha.2021.100057.

M. Yağcı, “Educational data mining: prediction of students’ academic performance using machine learning algorithms,” Smart Learn. Environ., vol. 9, no. 1, p. 11, Dec. 2022, doi: 10.1186/s40561-022-00192-z.

C. Romero and S. Ventura, “Educational data mining and learning analytics: An updated survey,” WIREs Data Min & Knowl, vol. 10, no. 3, p. e1355, May 2020, doi: 10.1002/widm.1355.

Y.-T. Shiao, C.-H. Chen, K.-F. Wu, B.-L. Chen, Y.-H. Chou, and T.-N. Wu, “Reducing dropout rate through a deep learning model for sustainable education: long-term tracking of learning outcomes of an undergraduate cohort from 2018 to 2021,” Smart Learn. Environ., vol. 10, no. 1, p. 55, Oct. 2023, doi: 10.1186/s40561-023-00274-6.

R. Wirth and J. Hipp, “CRISP-DM: Towards a Standard Process Model for Data Mining,” 2000.

L. Olsina and G. Rossi, “Measuring Web application quality with WebQEM,” IEEE Multimedia, vol. 9, no. 4, pp. 20–29, Oct. 2002, doi: 10.1109/MMUL.2002.1041945.

S. Ariyani, M. Sudarma, and P. A. Wicaksana, “Analysis of Functional Suitability and Usability in Sales Order Procedure to Determine Management Information System Quality,” intensif, vol. 5, no. 2, pp. 234–248, Aug. 2021, doi: 10.29407/intensif.v5i2.15537.

A. M. Lund, “Measuring Usability with the USE Questionnaire,” 2001.

T. Wongvorachan, S. He, and O. Bulut, “A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining,” Information, vol. 14, no. 1, p. 54, Jan. 2023, doi: 10.3390/info14010054.

Jin Huang and C. X. Ling, “Using AUC and accuracy in evaluating learning algorithms,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 3, pp. 299–310, Mar. 2005, doi: 10.1109/TKDE.2005.50.

C. X. Ling, J. Huang, and H. Zhang, “AUC: a Statistically Consistent and more Discriminating Measure than Accuracy,” 2003.

I. A. Abu Amra and A. Y. A. Maghari, “Students performance prediction using KNN and Naïve Bayesian,” in 2017 8th International Conference on Information Technology (ICIT), Amman: IEEE, May 2017, pp. 909–913. doi: 10.1109/ICITECH.2017.8079967.

D. A. Solichin, “Comparison of Decision Tree, Naïve Bayes and K-Nearest Neighbors for Predicting Thesis Graduation,” 2019.

A. Asthana and J. Olivieri, “Quantifying software reliability and readiness,” in 2009 IEEE International Workshop Technical Committee on Communications Quality and Reliability, Naples, FL, USA: IEEE, May 2009, pp. 1–6. doi: 10.1109/CQR.2009.5137352.

E. Budiman, N. Puspitasari, M. Taruk, and E. Maria, “Webqual 4.0 and ISO/IEC 9126 Method for website quality evaluation of higher education,” 2019.

Q. Aini, E. Fetrina, and N. C. Epriani, “WebQual 4.0 Plus: An Approach to Measure Customer Satisfaction toward Website Quality,” in 2023 11th International Conference on Cyber and IT Service Management (CITSM), Makassar, Indonesia: IEEE, Nov. 2023, pp. 1–6. doi: 10.1109/CITSM60085.2023.10455371.

A. Morales-Vargas, R. Pedraza-Jimenez, and L. Codina, “Website quality evaluation: a model for developing comprehensive assessment instruments based on key quality factors,” JD, vol. 79, no. 7, pp. 95–114, Dec. 2023, doi: 10.1108/JD-11-2022-0246.

J. Nielsen, Usability Engineering. Morgan Kaufmann Publishers Inc., 1994.




DOI: https://doi.org/10.21831/elinvo.v9i2.77052

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