Eduardo R. Yu II; Elmerito D. Pineda; Isagani M. Tano; Ace C. Lagman; Jayson M. Victoriano; Jonilo C. Mababa; Jaime P. Pulumbarit

Development and Integration of a Classification Model for Student Programming Anxiety Levels

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Author(s)

Eduardo R. Yu II ^1,2,3,4,* Elmerito D. Pineda ¹ Isagani M. Tano ¹ Ace C. Lagman ¹ Jayson M. Victoriano ¹ Jonilo C. Mababa ¹ Jaime P. Pulumbarit ¹

1. Graduate Studies Department, La Consolacion University Philippines, City of Malolos, Bulacan 3000, Philippines

2. College of Information and Computing Sciences, University of Santo Tomas, City of Manila, NCR 1008, Philippines

3. School of Computer Studies, Arellano University, City of Manila, NCR 1008, Philippines

4. Computer Studies Department, University of Caloocan City, City of Caloocan, NCR 1400, Philippines

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2026.04.04

Received: 6 Jan. 2026 / Revised: 15 Mar. 2026 / Accepted: 21 Jun. 2026 / Published: 8 Aug. 2026

Index Terms

Computer Programming Anxiety, Machine Learning in Education, Mental Health, Student Mental Health Detection, Classification Algorithms

Abstract

Reducing student anxiety is a critical factor in enhancing academic performance, with timely identification of mental health concerns serving as a key component of effective educational support strategies. This study presents the development and integration of a classification model designed to detect students’ current programming anxiety levels based on academic, demographic, and behavioral attributes. The model was trained and evaluated using student data collected from enrolled in computing-related programs, and its performance was benchmarked against Support Vector Machine, Logistic Regression, Random Forest, J48 Decision Tree, and Naive Bayes classifiers. The Support Vector Machine achieved excellent performance, with an F-measure of 95.97%, an accuracy of 97.11%, precision of 94.20%, recall of 97.86%, and a Cohen’s kappa of 0.937, indicating strong agreement between predicted and actual classifications. Error-based metrics further supported the model’s reliability, with a Mean Absolute Error (MAE) of 0.0289, Root Mean Squared Error (RMSE) of 0.1641, Relative Absolute Error (RAE) of 0.04%, and Root Relative Squared Error (RRSE) of 34.40%. To enhance practical utility, the model was integrated into a web-based student information system that generates real-time classifications and visualizations, supporting educators in recognizing students who may require additional support. While the model provides valuable insights for identifying students exhibiting current programming anxiety, future work incorporating longitudinal data is needed to enable true predictive capabilities for early intervention.

Cite This Paper

Eduardo R. Yu II, Elmerito D. Pineda, Isagani M. Tano, Ace C. Lagman, Jayson M. Victoriano, Jonilo C. Mababa, Jaime P. Pulumbarit, "Development and Integration of a Classification Model for Student Programming Anxiety Levels", International Journal of Intelligent Systems and Applications(IJISA), Vol.18, No.4, pp.57-72, 2026. DOI:10.5815/ijisa.2026.04.04

Reference

[1]R. Yazdanian, R. L. Davis, X. Guo, F. Lim, P. Dillenbourg, and M.-Y. Kan, "On the radar: Predicting near-future surges in skills’ hiring demand to provide early warning to educators," Computers and Education: Artificial Intelligence, vol. 3, p. 100043, Jan. 2022, doi: 10.1016/j.caeai.2021.100043.
[2]K. M. N. Rebuta, I. M. P. Cabaron, R. J. C. Pucong, J. M. C. Bisquera, R. T. Llerado, and M. V. M. Buladaco, “Relationship of programming skills and perceived value of learning programming among Information Technology education students in Davao Del Sur”, Int. J. Res. Innov. Soc. Sci., vol. 6, no. 6, pp. 882–887, 2022, doi: 10.47772/ijriss.2022.6633.
[3]Philippine Commission on Higher Education, CMO No. 25, Series of 2015: Policies, standards, and guidelines for the Bachelor of Science in Information Technology program, 2015. [Online]. Available: https://ched.gov.ph/wp-content/uploads/2017/10/CMO-no.-25-s.-2015.pdf.
[4]Commission on Higher Education, "CHED Memorandum Order No. 24, series of 2015: Policies, Standards and Guidelines for the Bachelor of Library and Information Science (BLIS) Program," Oct. 12, 2015. [Online]. Available: https://ched.gov.ph/wp-content/uploads/2017/10/CMO-no.-24-s.-2015.pdf
[5]L. Hu, “Programming and 21st century skill development in K‐12 schools: A multidimensional meta‐analysis,” Journal of Computer Assisted Learning, vol. 40, no. 2, pp. 610–636, Nov. 2023, doi: 10.1111/jcal.12904.
[6]J. Zheng, M. Duffy, and G. Zhu, “Predictors of university students’ intentions to enroll in computer programming courses: a mixed-method investigation,” Discover Education, vol. 3, no. 1, Sep. 2024, doi: 10.1007/s44217-024-00232-5.
[7]C. N. P. Olipas, R. F. Leona, A. C. A. Villegas, A. I. Cunanan Jr., and C. L. P. Javate, “The academic performance and the computer programming anxiety of BSIT students: A basis for instructional strategy improvement”, Int. J. Adv. Eng. Manag. Sci., vol. 7, no. 6, pp. 125–129, 2021.
[8]Ahmed. (2024). Student performance prediction using machine learning algorithms. Applied Computational Intelligence and Soft Computing, 2024(1). https://doi.org/10.1155/2024/4067721
[9]C. Connolly, E. Murphy, and S. Moore, “Programming Anxiety Amongst Computing Students—A key in the retention debate?,” IEEE Transactions on Education, vol. 52, no. 1, pp. 52–56, Aug. 2008, doi: 10.1109/te.2008.917193.
[10]Yildirim, O. G., & Özdener, N. (2022). Development and validation of the Programming Anxiety Scale. International Journal of Computer Science Education in Schools, 5(3), 17–34. https://doi.org/10.21585/ijcses.v5i3.140
[11]P. Kumar, S. Garg, and A. Garg, "Assessment of anxiety, depression and stress using machine learning models," Procedia Computer Science, vol. 171, pp. 1989–1998, 2020, doi: 10.1016/j.procs.2020.04.213.
[12]A. Sau and I. Bhakta, "Screening of anxiety and depression among seafarers using machine learning technology," Informatics in Medicine Unlocked, vol. 16, p. 100228, 2019, doi: 10.1016/j.imu.2019.100228.
[13]A. Sau and I. Bhakta, "Predicting anxiety and depression in elderly patients using machine learning technology," Healthcare Technology Letters, vol. 4, no. 6, pp. 238–243, Nov. 2017, doi: 10.1049/htl.2016.0096.
[14]A. Priya, S. Garg, and N. P. Tigga, "Predicting anxiety, depression and stress in modern life using machine learning algorithms," Procedia Computer Science, vol. 167, pp. 1258–1267, 2020, doi: 10.1016/j.procs.2020.03.442.
[15]J. D. Elhai, H. Yang, D. McKay, G. J. G. Asmundson, and C. Montag, "Modeling anxiety and fear of COVID-19 using machine learning in a sample of Chinese adults: associations with psychopathology, sociodemographic, and exposure variables," Anxiety, Stress, & Coping, vol. 34, no. 2, pp. 130–144, 2021, doi: 10.1080/10615806.2021.1878158.
[16]Albagmi, F. M., Alansari, A., Shawan, D. S. A., AlNujaidi, H., & Olatunji, S. O. (2022). Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach. Informatics in Medicine Unlocked, 28, 100854. https://doi.org/10.1016/j.imu.2022.100854
[17]S. A. Farooq, O. Konda, A. Kunwar, and N. Rajeev, "Anxiety prediction and analysis - A machine learning based approach," in 2023 4th International Conference for Emerging Technology (INCET), 2023, doi: 10.1109/incet57972.2023.10170115.
[18]S. Mutalib, "Mental health prediction models using machine learning in higher education institutions," Turkish Journal of Computer and Mathematics Education, vol. 12, no. 5, pp. 1782–1792, 2021, doi: 10.17762/turcomat.v12i5.2181.
[19]M. D. Nemesure, M. V. Heinz, R. Huang, and N. C. Jacobson, "Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence," Scientific Reports, vol. 11, no. 1, 2021, doi: 10.1038/s41598-021-81368-4.
[20]R. Qasrawi, S. VicunaPolo, D. A. Al-Halawa, S. Hallaq, and Z. Abdeen, "Assessment and prediction of depression and anxiety risk factors in schoolchildren: Machine learning techniques performance analysis," JMIR Formative Research, vol. 6, no. 8, e32736, 2022, doi: 10.2196/32736.
[21]A. D. Vergaray, N. S. Ríos, J. I. Necochea-Chamorro, K. Z. Ramos, and Y. Del Rosario Vásquez Valencia, "Systematic review of machine learning techniques to predict anxiety and stress in college students," Informatics in Medicine Unlocked, vol. 43, p. 101391, 2023, doi: 10.1016/j.imu.2023.101391.
[22]Geronimo, S. M., Hernandez, A. A., Abisado, M. B., Rodriguez, R. L., Nova, A. C., Caluya, S. S., & Blancaflor, E. B. (2023). Understanding Perceived Academic Stress among Filipino Students during COVID-19 using Machine Learning. SIGITE ’23: Proceedings of the 24th Annual Conference on Information Technology Education, 4, 54–59. https://doi.org/10.1145/3585059.3611412
[23]N. B. Mendoza, R. B. King, and J. Y. Haw, "The mental health and well-being of students and teachers during the COVID-19 pandemic: combining classical statistics and machine learning approaches," Educational Psychology, vol. 43, no. 5, pp. 430–451, 2023, doi: 10.1080/01443410.2023.2226846.
[24]Ibrahim, A. (2016). Definition, purpose, and procedure of developmental research: An analytical review. Asian Research Journal of Arts & Social Sciences, 1(6), 1–6. https://doi.org/10.9734/arjass/2016/30478
[25]M. Vale, "Descriptive research design and its myriad uses," Elsevier Author Services - Articles, Dec. 27, 2023. [Online]. Available: https://scientific-publishing.webshop.elsevier.com/research-process/descriptive-research-design-and-its-myriad-uses/. [Accessed: Apr. 12, 2025].
[26]Studer, S., Bui, T. B., Drescher, C., Hanuschkin, A., Winkler, L., Peters, S., & Müller, K. (2021). Towards CRISP-ML(Q): A machine learning process model with quality assurance methodology. Machine Learning and Knowledge Extraction, 3(2), 392–413. https://doi.org/10.3390/make3020020
[27]Alija, S., Beqiri, E., Gaafar, A. S., & Hamoud, A. K. (2023). Predicting students' performance using supervised machine learning based on imbalanced dataset and wrapper feature selection. Informatica, 47(1). https://doi.org/10.31449/inf.v47i1.4519
[28]W. L. Ku and H. Min, "Evaluating machine learning stability in predicting depression and anxiety amidst subjective response errors," Healthcare, vol. 12, no. 6, p. 625, 2024, doi: 10.3390/healthcare12060625.
[29]International Organization for Standardization & International Electrotechnical Commission. (2023). Systems and software quality requirements and evaluation (SQuaRE) - Product quality model (ISO/IEC 25010:2023). https://www.iso.org/obp/ui/en/#iso:std:iso-iec:25010:ed-2:v1:en

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