Application of machine learning algorithms in predicting the performance of students in mathematics in the modern world
Journal of Educational Research and Technology Management
PDF

Keywords

Academic performance
J48 algorithm
Mathematics in the modern world
Multiple linear regression
Naïve Bayes

How to Cite

Morilla, R., Omabe, R., Tolibas, C. J., Cornillez Jr., E. E., & Treceñe, J. K. (2020). Application of machine learning algorithms in predicting the performance of students in mathematics in the modern world. TARAN-AWAN Journal of Educational Research and Technology Management, 1(1), 49-57. Retrieved from https://journal.evsu.edu.ph/index.php/tjertm/article/view/215

Abstract

The purpose of this study is to predict the performance of students in mathematics in the modern world using different machine learning algorithms. In this study, multiple linear regression, J48 decision tree algorithm, and Naïve Bayes classification algorithm were implemented to predict the performance of the students in mathematics in the modern world (MMW). This study employed a correlational and predictive research design where secondary data from the records of the teachers in MMW during the academic year 2019-2020 was used. The data are records of 144 education students which are composed of their ratings in attendance, quizzes, recitation, midterm exam, final exam, and their final grade. Results revealed that the majority of the students performed well in the subject having a very good rating. Also, the analysis revealed that ratings in attendance, quizzes, recitation, midterm exam, and final exam have a significant positive relationship with the final grades. Among the five variables, the most influential component to the overall performance of the students is the midterm exam rating. Using the 10-fold cross-validation, prediction models were also generated using Naïve Bayes and J48. Further, the Naïve Bayes algorithm provides better performance for predicting the students’ academic performance in MMW which provided a 73.61% accuracy, followed by the J48 (72.22%), and Multiple Linear Regression with 70.2% accuracy. Lastly, these machine learning models should be employed to improve the learning outcomes of students.

PDF

References

Alcantara, E. C., Veriña, R. U., & Niem, M. M. (2020). Teaching and Learning with Technology: Ramification of ICT Integration in Mathematics Education. Teaching and Learning, 10(1).

Badugu, S., & Rachakatla, B. (2020). Students’ performance prediction using machine learning approach. In Data Engineering and Communication Technology (pp. 333-340). Springer, Singapore.

Belachew, E. B., & Gobena, F. A. (2017). Student performance prediction model using machine learning approach: the case of Wolkite university. International Journal if Advanced Research in Computer Science and Software Engineering, 7(2), 46-50.

Bray, A., & Tangney, B. (2017). Technology usage in mathematics education research–A systematic review of recent trends. Computers & Education, 114, 255-273.

CHED. (2015). CHED RESPECTS SC DECISION, TO RESPOND IN 10 DAYS. https://ched.gov.ph/ched-respects-sc-decision-respond-10-days/

Cornillez Jr, E. E., Treceñe, J. K., & de los Santos, J. R. (2020). Mining Educational Data in Predicting the Influence of Mathematics on the Programming Performance of University Students. Indian Journal of Science and Technology, 13(26), 2668-2677. DOI: 10.17485/IJST/v13i26.719

De Guzman, A. B. (2003). The dynamics of educational reforms in the Philippine basic and higher education sectors. Asia Pacific Education Review, 4(1), 39-50.

Dhilipan, J., Vijayalakshmi, N., Suriya, S., & Christopher, A. (2021, February). Prediction of Students Performance using Machine learning. In IOP Conference Series: Materials Science and Engineering (Vol. 1055, No. 1, p. 012122). IOP Publishing.

Díaz, L. M. B., & Cano, E. V. (2019). Effects on academic performance in secondary students according to the use of ICT. IJERI: International Journal of Educational Research and Innovation, (12), 90-108.

DLSU. (2015). Teaching of Mathematics in the Modern World.Retrieved from http://dlsuppp2015.weebly.com/uploads/5/9/9/7/59976313/dlsuppp2015_te

Enrique, W. D. C., & Cusipag, M. N. (2020). MathDali Program and Its Effectiveness on the Mathematics Performance of Grade 4 Students. Harvest, 16(1), 129-152.

Farhad, A., & Sanjay, P. (2017). Comparative Study of J48, Naive Bayes and One-R Classification Technique for Credit Card Fraud Detection using WEKA [J]. Advances in computational sciences and technology, 10(62), 1731-1743.

Gbollie, C., & Keamu, H. P. (2017). Student academic performance: The role of motivation, strategies, and perceived factors hindering Liberian junior and senior high school students learning. Education Research International, 2017.

Gómez-García, M., Hossein-Mohand, H., Trujillo-Torres, J. M., Hossein-Mohand, H., & Aznar-Díaz, I. (2020). Technological factors that influence the mathematics performance of secondary school students. Mathematics, 8(11), 1935.

Gunst, R. F., & Mason, R. L. (2018). Regression analysis and its application: a data-oriented approach. CRC Press.

Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., ... & Liao, S. N. (2018, July). Predicting academic performance: a systematic literature review. In Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education (pp. 175-199).

Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., & Reiss, K. M. (2020). The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education, 153, 103897.

Hu, X., Gong, Y., Lai, C., & Leung, F. K. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1-13.

Jayaprakash, S., Balamurugan E. & Chandar, V. (2018). Predicting Students Academic Performance using Naive Bayes Algorithm, BlueCrest College Accra, Ghana.

Kumar, N., Mitra, S., Bhattacharjee, M., & Mandal, L. (2019). Comparison of different classification techniques using different datasets. In Proceedings of International Ethical Hacking Conference 2018 (pp. 261-272). Springer, Singapore.

Larrabee Sønderlund, A., Hughes, E., & Smith, J. (2019). The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology, 50(5), 2594-2618.

Las Johansen, B. C., & Trecene, J. K. D. (2018). Predicting Academic Performance of Information Technology Students using C4. 5 Classification Algorithm: A Model Development. 10(1), 7-21. http://www.ripublication.com/irph/ijisa18/ijisav10n1_02.pdf

Leonard, J. (2018). Culturally specific pedagogy in the mathematics classroom: Strategies for teachers and students. Routledge.

Mathematics in the Modern World Preliminaries [CHED]. (2013). KWF Mathematics in the Modern World. https://ched.gov.ph/wp-content/uploads/2017/10/KWF-Mathematics-in-the-Modern-World.pdf

Maulud, D., & Abdulazeez, A. M. (2020). A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1(4), 140-147.

Ofori, F., Maina, E., & Gitonga, R. (2020). Using Machine Learning Algorithms to Predict Students’ Performance and Improve Learning Outcome: A Literature Based Review. Journal of Information and Technology, 4(1), 33-55.

Remo, L. M. (2019). Prediction And Assessment Of Students Performance In Mathematics In The Modern World (MMW). INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH. 8(4), 219-224.

Roman, A. G., & Villanueva, R. U. (2020). Competency Acquisition, Difficulty and Performance of First Year College Students in Mathematics in the Modern World (MITMW). International Journal on Emerging Mathematics Education, 3(2).

Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422.

Sharp, L. A., & Hamil, M. (2018). Impact of a Web-Based Adaptive Supplemental Digital Resource on Student Mathematics Performance. Online Learning, 22(1), 81-92.

Sidhu, G., & Srinivasan, S. (2018). An intervention-based active-learning strategy to enhance student performance in mathematics. International Journal of Pedagogy and Teacher Education, 2(1), 85-96.

Sun, Z., Xie, K., & Anderman, L. H. (2018). The role of self-regulated learning in students' success in flipped undergraduate math courses. The internet and higher education, 36, 41-53.

Valencia, G. R. (2015, October). CHED panelist seeks release of new college GE syllabus. Retrieved from National Research Council of the Philippines: http://nrcp.dost.gov.ph/previous-issues/118-ched-panelist-seeks-release-of-new-college-ge-syllabus.

Vamshidharreddy, V. S., Saketh, A. S., & Gnanajeyaraman, R. (2020). Student’s Academic Performance Prediction Using Machine Learning Approach. International Journal of Advanced Science and Technology. 29(9), 6731 – 6737.

Verdeflor, R. N., & Pacadaljen, L. M. (2021). Outcomes of the course mathematics in the modern world: A phenomenological study. Annals of the Romanian Society for Cell Biology, 2586-2599.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.