Decision-Based Grading Model System and Student Performance Analysis Using Rule-Based Algorithm
Authors
Criselle J. Centeno
Information Technology Department, Pamantasan ng Lungsod ng Maynila, Intramuros, Manila, Philippines (PH)
Angela L. Arago
Graduate School Department, La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines (PH)
Mamerto C. Mendoza
Graduate School Department, La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines (PH)
Isagani Mirador Tano
Graduate School Department, Quezon City University, Novaliches San Bartolome, Quezon City, Philippines (PH)
Keno Piad
College of Information and Communications Technology Bulacan State University, Malolos, Bulacan, Philippines (PH)
Jovy Jay D. Cabrera
Immaculate Conception I College of Arts and Technology Santa Maria Bulacan Philippines (PH)
Jonilo Mababa
Graduate School Department, La Consolacion University, Bulihan, City of Malolos, Bulacan, Philippines (PH)
Jayson Victoriano
Information Technology Department, Bulacan State University, Malolos, Bulacan, Philippines (PH)
Article Information
DOI: 10.51583/IJLTEMAS.2025.1412000132
Subject Category: Educational Management, Information Technology , Algorithm Analysis
Volume/Issue: 14/12 | Page No: 1507-1527
Publication Timeline
Submitted: 2026-01-17
Published: 2026-01-16
Abstract
The continuous advancement of educational technologies has led to the development of innovative academic tools aimed at enhancing assessment methods and student performance analysis. This study introduces the Decision-Based Grading Model System and Student Performance Analysis Using Rule-Based Algorithm, a system designed to modernize the grading process and provide tailored academic support. The system features a flexible grading simulator that allows educators to set minimum passing scores based on predefined parameters such as course requirements, learning outcomes, and institutional policies. It also integrates a rule-based recommendation system that suggests appropriate learning materials and assessments for students who require remediation. The study utilized both qualitative and quantitative approaches, involving expert validation, user feedback, and system evaluation through the ISO/IEC 25010 Software Quality Model. Results show high levels of effectiveness in functionality, performance efficiency, usability, reliability, security, maintainability, and portability. Additionally, accuracy metrics revealed 80% precision, 89% recall, and an F1-score of 84% for the recommendation system, confirming its capacity to deliver relevant interventions. The system promotes academic transparency, reduces manual workload, and aligns grading and assessment strategies with actual student needs. Overall, the study contributes to the evolving landscape of educational technology by offering a dynamic, data-driven approach to academic management.
Keywords
Automated Assessment, Decision-Based Grading, Educational Technology, Rule-Based Algorithm and Student Performance Analysis
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References
1. Akhtar, B. (2021). An automated grading and feedback system for a computer literacy course. https://libres.uncg.edu/ir/asu/f/Akhtar%20Thesis.pdf [Google Scholar] [Crossref]
2. Binti Yahaya, A. (2022). Assessment Management System For Centralized Grade Reporting. [Google Scholar] [Crossref]
3. Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 6. [Google Scholar] [Crossref]
4. Centeno, C. J., De Guzman, E. S., Bauat, R. V., Espino, J., & Victoriano, J. M. (2023). Utilization and pre-processing of Marilao, Meycauayan, and Obando River System dataset using Excel and Power Business Intelligence for descriptive analytics and visualization. Cosmos: An International Journal of Management, 12(2), January–June. ISSN: 2278-1218. [Google Scholar] [Crossref]
5. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2023, June 30). An introduction to statistical learning. SpringerLink. https://link.springer.com/book/10.1007/978-1-0716-1418-1 [Google Scholar] [Crossref]
6. Koren, Y., Rendle, S., & Bell, R. (2021). Advances in Collaborative Filtering. In Recommender Systems Handbook (pp. 99–142). Springer Link. https://link.springer.com/chapter/10.1007/978-1-0716-2197-4_3 [Google Scholar] [Crossref]
7. Matthew, K., Janicki, T., He, L., & Patterson, L. (2012). Implementation of an Automated Grading System with an Adaptive Learning Component to Affect Student Feedback and Response Time. Journal of Information Systems Education, 23(1). https://jise.org/Volume23/n1/JISEv23n1p71.pdf [Google Scholar] [Crossref]
8. McAffee, A., & Brynjolfsson, E. (2024, February 29). Big Data: The management revolution. Harvard Business Review. https://hbr.org/2012/10/big-data-the-management-revolution [Google Scholar] [Crossref]
9. Messing, A., & Hutchinson, S. (2021). Forward chaining Hierarchical Partial-Order planning. In Springer proceedings in advanced robotics (pp. 364–380). https://doi.org/10.1007/978-3-030-66723-8_22 [Google Scholar] [Crossref]
10. Mohammadpour, T., Bidgoli, A. M., Enayatifar, R., & Javadi, H. H. S. (2019). Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm. Genomics, 111(6), 1902–1912. [Google Scholar] [Crossref]
11. https://doi.org/10.1016/j.ygeno.2019.01.001 [Google Scholar] [Crossref]
12. Muklason, A., Irianti, R. G., & Marom, A. (2019). Automated course timetabling optimization using Tabu-Variable Neighborhood Search based Hyper-Heuristic Algorithm. Procedia Computer Science, 161, 656–664. https://doi.org/10.1016/j.procs.2019.11.169 [Google Scholar] [Crossref]
13. Musa, A. B., & Sa’ad, S. M. (2023). Impact of Heuristic Approach on Students’ Academic Achievement and Retention in Map Reading and Interpretation Among Secondary Schools, Municipal Zones, Kano-Nigeria. https://journals.e-palli.com/home/index.php/ajet/article/view/2328 [Google Scholar] [Crossref]
14. Mustapa, M. A., Salahuddin, L., & Hashim, U. R. (2022). Automated Study Plan Generator using Rule-based and Knapsack Problem. https://thesai.org/Downloads/Volume13No8/Paper_47 Automated_Study_Plan_Generator.pdf [Google Scholar] [Crossref]
15. Padhma, M. (2024, June 13). A Comprehensive Introduction to Evaluating Regression Models. https://www.analyticsvidhya.com/blog/2021/10/evaluation-metric-for-regression-models/ [Google Scholar] [Crossref]
16. Provost, F., & Fawcett, T. (2023). Data Science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508 [Google Scholar] [Crossref]
17. Rosas, P., Ríos-Solís, Y. Á., & Romeo Sánchez Nigenda. (2023). Scheduling personalized study plans considering the stress factor. Interactive Learning Environments, 1–20. https://doi.org/10.1080/10494820.2023.2191260 [Google Scholar] [Crossref]
18. Rotty, A. K., Dewayana, T. S., & Habyba, A. N. (2022). Cross-Industry Standard Process for Data Mining (CRISP-DM) Approach in Determining the Most Significant Employee Engagement Drivers to Sales at X Car Dealership. Proceedings of the 3rd Asia Pacific International Conference on Industrial Engineering and Operations Management. http://ieomsociety.org/proceedings/2022malaysia/552.pdf [Google Scholar] [Crossref]
19. Rudiarto, S., Dwiasnati, S., & Irawan, A. S. Y. (2022). Design and build expert system applications using forward chaining methods to manage web-based space management. Global Journal of Engineering and Technology Advances, 10(2), 009–017. https://doi.org/10.30574/gjeta.2022.10.2.0115 [Google Scholar] [Crossref]
20. Sandra, L., Lumbangaol, F., & Matsuo, T. (2021). Machine Learning Algorithm to Predict Student’s Performance: A Systematic Literature Review. [Google Scholar] [Crossref]
21. https://www.temjournal.com/content/104/TEMJournalNovember2021_1919_1927.html [Google Scholar] [Crossref]
22. Sarmiento, L. I. M., Tesoro, S. K. M., Gagno, P. A. M., Batista, R. T. B., Quiwa, E. P., & Naval Jr, P. C. (2025). CSS: A Course Scheduling System using Genetic Multiobjective Optimization. [Google Scholar] [Crossref]
23. Sayed, B. T. (2021). Application of Expert Systems or Decision-making Systems in the Field of Education, 9(1), 1396–1405. https://doi.org/10.17762/itii.v9i1.283 [Google Scholar] [Crossref]
24. Şekeroğlu, B., Dimililer, K., & Tuncal, K. (2019). Student Performance Prediction and Classification Using Machine Learning Algorithms. Association for Computing Machinery. https://doi.org/10.1145/3318396.3318419 [Google Scholar] [Crossref]
25. Servaz, B. C. O., Pradas, J. J. N., Sergio, J. E. A., De Goma, J. C., & Zara, S. I. (2025). Examination Scheduling System for Mapua University. [Google Scholar] [Crossref]
26. Stephen Few. (2020). Now you see it: Simple visualization techniques for quantitative analysis. https://www.amazon.com/Now-You-See-Visualization-Quantitative/dp/0970601980 [Google Scholar] [Crossref]
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