Utilizing Data Mining Techniques to Predict Student Academic Performance

Dr. G.K. Sharma; Ashutosh Sharma; Dr. Sourabh Sharma

doi:10.36676/urr.v11.i3.1377

Authors

Dr. G.K. Sharma Assistant Professor, Gwalior
Ashutosh Sharma Research Scholar, Guwahati.
Dr. Sourabh Sharma Independent Researcher, India

DOI:

https://doi.org/10.36676/urr.v11.i3.1377

Keywords:

Prediction for Students, Academic Performance, Categorization

Abstract

During this epidemic, a problem in fundamental education affecting all globe is occurring, and we note that education and learning were online and conducted in students. Academic performance of students must be forecast, so that the instructor may better identify the missing pupils and offer teachers a proactive opportunity to develop additional resources for the student to maximize their chances of graduation. Students' academic achievement in higher learning (EH) has been extensively studied in addressing academic inadequacies, rising drop-out rates, graduation delays, and other difficult questions. Simply said, the performance of students refers to the amount to which short and long-term educational objectives are met. Academics nonetheless judge student achievement from different viewpoints, from grades, average grade points (GPAs) to prospective jobs. The literature encompasses numerous computing attempts to improve student performance in schools and colleges, primarily through data mining and analysis learning. However, the efficiency of current smart techniques and models is still unanimous.

References

Ajoodha, R., Jadhav, A., & Dukhan, S. (2020). Forecasting learner attrition for student success at a south african university. In Conference of the South African Institute of Computer Scientists and Information Technologists, 19-28.

Ajoodha, R., Klein, R., & Rosman, B. (2015). Single-labelled music genre classification using content-based features. In Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-Rob Mech), 66-71.

Ajoodha, R., & Rosman, B. (2018). Learning the influence structure between partially observed stochastic processes using iot sensor data. In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence.

Asif, R., Merceron, A., Ali, S.A., & Haider, N.G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers & Education, 113, 177-194.

Burman, I., & Som, S. (2019). Predicting students academic performance using support vector machine. In Amity International Conference on Artificial Intelligence (AICAI), 756-759.

Deepika, K., & Sathyanarayana, N. (2019). Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction. International Journal of Intelligent Engineering and Systems, 12(1), 30-39.

Dixson, M.D. (2015). Measuring student engagement in the online course: The Online Student Engagement scale (OSE). Online Learning, 19(4), n4.

Domínguez, A., Saenz-de-Navarrete, J., De-Marcos, L., Fernández-Sanz, L., Pagés, C., & Martínez-Herráiz, J.J. (2013). Gamifying learning experiences: Practical implications and outcomes. Computers & Education, 63, 380-392.

Gerhana, Y.A., Fallah, I., Zulfikar, W.B., Maylawati, D.S., & Ramdhani, M.A. (2019). Comparison of naive Bayes classifier and C4. 5 algorithms in predicting student study period. In Journal of Physics: Conference Series, 1280(2), 022022.

Hodges, C.B., Moore, S., Lockee, B.B., Trust, T., & Bond, M.A. (2020). The difference between emergency remote teaching and online learning. http://hdl.handle.net/10919/104648

Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers & Education, 61, 133-145.

Hussain, M., Zhu, W., Zhang, W., Abidi, S.M.R., & Ali, S. (2019). Using machine learning to predict student difficulties from learning session data. Artificial Intelligence Review, 52(1), 381-407.