Abstract
The presented work is a student marks and grade prediction system using supervised machine learning techniques, the system is developed on the historic performance of students. The data used in this research is collected from Federal Board of Intermediate and Secondary Education Islamabad Pakistan, there are 7 regions in FBISE i.e. Punjab, Sindh, Khyber Pakhtunkhwa, Balochistan, Azad Jammu and Kashmir and overseas. The aims of this work is to analyze the education quality which is closely tightened with the sustainable development goals. The implementation of the system has produced an excess of data which must be processed suitably to gain more valuable information that can be more useful for future development and planning. Student marks and grade prediction from their historic academic data is a popular and useful application in educational data mining, so it is becoming a valuable source of information which can be used in different manners to improve the education quality in the country. Related work shows that several method for academic grade prediction are developed for the betterment of teaching and administrative staff of an educational organizational system. In our proposed methodology, the obtained data is preprocessed to improve the quality of data, the labeled student historic data (29 optimal attributes) is used to train decision tree classifier and regression model. The classification system will predict the grade while the regression model will predict the marks, finally the results obtained from both the model are analyzed. The obtain results show the effectiveness and importance of machine learning technology in predicating the students performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akhil, M., Deekshatulu, B. L., & Chandra, P. (2013). Classification of heart disease using K- nearest neighbor and genetic algorithm. Procedia Technology, 10, 85–94.
Baker, R. S. J. (n.d.). “Data Mining for Education Data Mining for Education Advantages Relative to Traditional Educational Research Paradigms.”
Baker, R. S. J. D. and Yacef, K. (2009). “The State of Educational Data Mining in 2009 : A Review and Future Visions,” vol. 1, no. 1, pp. 3–16.
Brooks, C., Thompson, C., Ri, H. D., Hgxfdwlrqdo, D., and Prghoolqj, S. (2010). “Chapter 5 : Predictive Modelling in Teaching and Learning,” pp. 61–68.
Buenaño-Fern, D. and Gil, D. (2019). “Application of Machine Learning in Predicting Performance for Computer Engineering Students : A Case Study,” pp. 1–18.
Buenaño-Fernandez, D. (2019). “The use of tools of data mining to decision making in engineering education — A systematic mapping study,” no. December 2018.
Bydžovská, H. (2015). “Are Collaborative Filtering Methods Suitable for Student Performance Prediction ?,” pp. 425–430.
Casanova, D., Moreira, A., Costa, N. (2011). “Procedia Social and Technology Enhanced Learning in Higher Education : results from the design of a quality evaluation framework,” vol. 29.
Castro, F., Vellido, A., Nebot, À., and Mugica, F. (2007). “Applying Data Mining Techniques to e-Learning Problems,” 221, 183–221.
Dahdouh, K., Dakkak, A., Oughdir, L., and Ibriz, A. (2019). “Large - scale e - learning recommender system based on spark and Hadoop,” Journal of Big Data.
Daniela, L. (2017). “An Overview on Effectiveness of Technology Enhanced Learning (TEL),” 8(1), 79–91.
F. Authors. (2018). “Understand , develop and enhance the learning process with big data”.
Gil, D., Fernández-Alemán, J. L., Trujillo, J., García-Mateos, G., Luján-Mora, S., and Toval, A. (2018). “The Effect of Green Software : A Study of Impact Factors on the Correctness of Software,” pp. 1–19.
Hong, S. J. and Weiss, S. M. (2001). “Advances in predictive models for data mining,” vol. 22, pp. 55–61.
Kami, B. and Jakubczyk, M. (2017). “A framework for sensitivity analysis of decision trees”.
Khalifa, S., Elshater, Y., Sundaravarathan, K., and Bhat, A. (2016). “The Six Pillars for Building Big Data Analytics Ecosystems,” vol. 49, no. 2, pp. 1–36.
Kuncheva, L. (1993). “Genetic algorithm for feature selection for parallel classifiers,” vol. 16, pp. 163–168.
Lee, J., and Choi, H. (2017). “What affects learner’s higher-order thinking in technology-enhanced learning environments? The effects of learner factors,” Computers & Education.
Lu, O. H. T., Huang, A. Y. Q., Huang, J. C. H., Lin, A. J. Q., and Yang, S. J. H. (n.d.). “Applying Learning Analytics for the Early Prediction of Students ’ Academic Performance in Blended Learning.”
Luj, S. (2018). “Big Data , the Next Step in the Evolution of Educational Data Analysis,” vol. 1, no. Icits.
Márquez-Vera, C. et al. (2016). “A rticle,” vol. 33, no. 1, pp. 107–124.
Paura, L., & Arhipova, I. (2014). Cause analysis of students’ dropout rate in higher education study program. Procedia - Social and Behavioral Sciences, 109, 1282–1286.
Ph, D., Daud, M., and Ph, D. (2015). “Final Grade Prediction of Secondary School Student using Decision Tree,” vol. 115, no. 21, pp. 32–36.
Polyzou, A., & Karypis, G. (2016). Grade prediction with models specific to students and courses. International Journal of Data Science and Analytics, 2(3), 159–171.
Provost, F. and Fawcett, T. (2013). “Data science and its relationship data-driven decision making,” vol. 1, no. 1, pp. 51–59.
Publishing, E. G., E. Group, Limited, P., Reserved, A. R., Url, O., & Uri, E. (2017). Catalysing Change in Higher Education for Sustainable Development : A Review of Professional Development Initiatives for University Educators. International Journal of Sustainability in Higher Education, 18(5), 798 Development : A Review of Professional Development.
Ren, Z. and Sweeney, M. (2016). “Predicting Student Performance Using Personalized Analytics,” no. April, pp. 61–69.
S. Member. (2010). “Educational Data Mining : A Review of the State of the Art,” vol. 40, no. 6, pp. 601–618.
Shields, L. D., Satz, N. A. 2017. “Equality of Educational Opportunity,” Stanford Encyclopedia of Philosophy, California USA.
Sin, K. and Muthu, L. (2015). “Application of big data in education data mining and learning analytics – A literature review,” vol. 6956, no. July, pp. 1035–1049.
Thai-nghe, N., Drumond, L., Krohn-grimberghe, A., & Schmidt-thieme, L. (2010). Recommender system for predicting student performance. Procedia Computer Science, 1(2), 2811–2819.
U. Nations. 2019). “Sustainable development goals”.
Visvizi, A., Lytras, M. D., and Daniela, L. 2018. “The Future of Innovation and Technology in Education : Policies and Practices for Teaching and Learning Education, Innovation and the Prospect of Sustainable Growth and Development”.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yousafzai, B.K., Hayat, M. & Afzal, S. Application of machine learning and data mining in predicting the performance of intermediate and secondary education level student. Educ Inf Technol 25, 4677–4697 (2020). https://doi.org/10.1007/s10639-020-10189-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-020-10189-1