The week's pick
Reseach Article
Predicting and Analysis of Students’ Academic Performance using Data Mining Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 32 |
| Year of Publication: 2018 |
| Authors: Reda M. Ahmed, Nahla F. Omran, Abdelmgeid A. Ali |
10.5120/ijca2018918250
|
Reda M. Ahmed, Nahla F. Omran, Abdelmgeid A. Ali . Predicting and Analysis of Students’ Academic Performance using Data Mining Techniques. International Journal of Computer Applications. 182, 32 ( Dec 2018), 1-6. DOI=10.5120/ijca2018918250
Abstract
The educational database holds on the massive amount of data and it is increasing rapidly. Data mining provides effective techniques for discovering useful knowledge and pattern from students’ data. The discovered patterns can be used to understand many problems in the educational field. This paper proposes a framework to predict the achievement of first-year bachelor’s students in computer science course. Decision Tree, Na¨ive Bayes, and Multi-Layer Perceptron classification methods are applied to the students’ data using the WEKA Data Mining tool to produce the best prediction model of the students’ academic performance. Experiments conducted to detect the best model among the used techniques then the models’ accuracy is computed. The extracted knowledge from the prediction model will be utilized to recognize and profile the student to decide the students’ level of success in the first semester.
References
- Han, J. 2012. Micheline Kamber, & Jian Pei.”Data Mining: Concepts and Techniques”.
- Aziz, A. A., Ismail, N. H., & Ahmad, F. 2013.” Mining Students’ Academic Performance” inJournal of Theoretical & Applied Information Technology,53(3).
- Ahmad, F., Ismail, N. H., & Aziz, A. A. 2015. ”The Prediction of Students’ Academic Performance Using Classification Data Mining Techniques.” inApplied Mathematical Sciences, 9(129), 6415-6426.
- El-Halees, A. 2009. Mining Student’s Data to Analyze ELearning Behavior: A Case Study.
- Tair, M. M. A., & El-Halees, A. M. 2012.” Mining Educational Data to Improve Students’ Performance: A Case Study” in International Journal of Information,2(2), 140-146.
- Garc´ia, E. P. I., & Mora, P. M. 2011.” Model Prediction of Academic Performance for First Year Students”. InArtificial Intelligence (Micai), 2011 10th Mexican International Conference (pp. 169-174). IEEE.
- Bhardwaj, B. K., & Pal, S.2012.” Data Mining: A Prediction for Performance Improvement Using Classification”.Arxiv Preprint Arxiv:1201.3418.
- Al-Radaideh, Q. A., Al-Shawakfa, E. M., & Al-Najjar, M. I. 2006.” Mining Student Data Using Decision Trees.” In- International Arab Conference on Information Technology, Yarmouk University, Jordan.
- Lakshmi, D., Arundathi, S., & Jagadeesh, D. 2014.” Data Mining: A Prediction for Student’s Performance Using Decision Tree Id3 Method”.
- Ali, M. M. 2013. ”Role of Data Mining in Education Sector” in International Journal of Computer Science and Mobile Computing,2(4), 374-383.
- Sembiring, S. 2012.”An Application of Predicting Student Performance Using Kernel K-Means and Smooth Support Vector Machine” in(Doctoral Dissertation, Ump).
- Bhullar, M. S., & Kaur, A. 2012.” Use of Data Mining in Education Sector.” InProceedings of The World Congress on Engineering and Computer Science(Vol. 1, pp. 24-26).
- Sumitha, R.,& Vinothkumar, E. S. 2016.” Prediction of Students Outcome Using Data Mining Techniques.” inInternational Journal of Scientific Engineering and Applied Science, 2(6)8.
- Saa, A. A. 2016.” Educational Data Mining & Students’ Performance Prediction.” inInternational Journal of Advanced Computer Science and Applications,7(5), 212-220.
- Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. 2017. ”Analyzing Undergraduate Students’ Performance Using Educational Data Mining.” inComputers & Education,113, 177-194.
- Hussain, S., Dahan, N. A., Ba-Alwib, F. M., & Ribata, N. 2018.” Educational Data Mining and Analysis of Students’ Academic Performance Using Weka.” inIndonesian Journal of Electrical Engineering and Computer Science,9(2).
- Chen, T. Y., Kuo, F. C., & Merkel, R. 2004.” On the Statistical Properties of The F-Measure.” InQuality Software, 2004. Qsic 2004. Proceedings. Fourth International Conference on (pp. 146-153). IEEE.
- Arora, R. 2012.” Comparative Analysis of Classification Algorithms on Different Datasets Using Weka.” in International Journal of Computer Applications,54(13).
Index Terms
Keywords