The week's pick
Reseach Article
Student Academic Performance Prediction using Artificial Neural Networks: A Case Study
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 48 |
| Year of Publication: 2019 |
| Authors: Mubarak Albarka Umar |
10.5120/ijca2019919387
|
Mubarak Albarka Umar . Student Academic Performance Prediction using Artificial Neural Networks: A Case Study. International Journal of Computer Applications. 178, 48 ( Sep 2019), 24-29. DOI=10.5120/ijca2019919387
Abstract
Students dropout and delay in graduation are significant problems at Katsina State Institute of Technology and Management (KSITM). There are various reasons for that, students’ performances during first year is one of the major contributing factors. This study aims at predicting poor students’ performances that might lead to dropout or delay in graduation so as to allow the institution to develop strategic programs that will help improve student performance and enable the student to graduate in time without any problem. This study presents a neural network model capable of predicting student’s GPA using students’ personal information, academic information, and place of residence. A sample of 61 Computer Networking students’ dataset was used to train and test the model in WEKA software tool. The accuracy of the model was measured using well-known evaluation criteria. The model correctly predicts 73.68% of students’ performance and, specifically, 66.67% of students that are likely to dropout or experience delay before graduating.
References
- A. S. Hoffait and M. Schyns, ‘Early detection of university students with potential difficulties’, Decis. Support Syst., vol. 101, pp. 1–11, 2017.
- E. Osmanbegović and M. Suljić, ‘Data Mining Approach for Predicting students performances’, J. Econ. Bus., vol. X, no. 1, pp. 3–12, 2012.
- C. Romero and S. Ventura, ‘Educational Data Mininig: A reviwe of the state of the art’, IEEE Trans. Syst. Man. Cybern., vol. 40, no. X, pp. 601–619, 2010.
- ‘educationaldatamining.org’. [Online]. Available: http://educationaldatamining.org/. [Accessed: 09-May-2019].
- M. M. Abu Tair and A. M. El-Halees, ‘Mining Educational Data to Improve Students’ Performance: A Case Study’, Int. J. Inf. Commun. Technol. Res., vol. 2, no. 2, pp. 140–146, 2012.
- H. Hamsa, S. Indiradevi, and J. J. Kizhakkethottam, ‘Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm’, Procedia Technol., vol. 25, pp. 326–332, 2016.
- P. Cortez and A. Silva, ‘Using data mining to predict secondary school student performance’, Proc. 5th Futur. Bus. Technol. Conf., vol. 2003, no. 2000, pp. 5–12, 2008.
- S. Huang and N. Fang, ‘Work in progress: Early prediction of students’ academic performance in an introductory engineering course through different mathematical modeling techniques’, Proc. - Front. Educ. Conf. FIE, vol. 1, pp. 3–4, 2012.
- A. M. Flitman, ‘Towards analysing student failures: Neural networks compared with regression analysis and multiple discriminant analysis’, Comput. Oper. Res., vol. 24, no. 4, pp. 367–377, 1997.
- M. Al-Saleem, N. Al-Kathiry, S. Al-Osimi, and G. Badr, ‘Mining Educational Data to Predict Students’ Academic Performance’, Mach. Learn. Data Min. Pattern Recognition, Mldm 2015, vol. 9166, no. 6, pp. 403–414, 2015.
- C. E. Lopez Guarin, E. L. Guzman, and F. A. Gonzalez, ‘A Model to Predict Low Academic Performance at a Specific Enrollment Using Data Mining’, Rev. Iberoam. Tecnol. del Aprendiz., vol. 10, no. 3, pp. 119–125, 2015.
- D. Delen, ‘A comparative analysis of machine learning techniques for student retention management’, Decis. Support Syst., vol. 49, no. 4, pp. 498–506, 2010.
- C. El Moucary, M. Khair, and W. Zakhem, ‘Improving Student’s Performance Using Data Clustering and Neural Networks in Foreign-Language Based Higher Education’, Res. Bull. Jordan ACM, vol. 2, no. 3, pp. 27–34, 2011.
- N. Z. Zacharis, ‘Predicting Student Academic Performance in Blended Learning Using Artificial Neural Networks’, Int. J. Artif. Intell. Appl., vol. 7, no. 5, pp. 17–29, 2016.
- B. Oancea, R. Dragoescu, and S. Ciucu, ‘Predicting students’ results in higher education using a neural network’, no. 72041, 2013.
- T. Wongkhamdi and P. Seresangtakul, ‘A Comparison of Classical Discriminant Analysis and Artificial Neural Networks in Predicting Student Graduation Outcomes’, Proc. Second Int. Conf. Knowl. Smart Technol., pp. 29–34, 2010.
- A. M. Shahiri, W. Husain, and N. A. Rashid, ‘A Review on Predicting Student’s Performance Using Data Mining Techniques’, Procedia Comput. Sci., vol. 72, pp. 414–422, 2015.
- J. Han, M. Kamber, and J. Pei, Data Mining – Concepts & Techniques, 3rd Editio. USA: Morgan Kaufmann Publishers, Elsevier Inc, 2012.
- ‘What is the difference between classification and prediction?’ [Online]. Available: https://www.kdnuggets.com/faq/classification-vs-prediction.html. [Accessed: 09-Jun-2019].
- D. Kabakchieva, ‘Student Performance Prediction by Using Data Mining Classification Algorithms’, Int. J. Comput. Sci. Manag. Res., vol. 1, no. 4, pp. 686–690, 2012.
- B. Guo, R. Zhang, G. Xu, C. Shi, and L. Yang, ‘Predicting Students Performance in Educational Data Mining’, Proc. - 2015 Int. Symp. Educ. Technol. ISET 2015, pp. 125–128, 2016.
- I. H. Witten, E. Frank, and H. Mark A., Data Mining Practical Machine Learning Tools and Techniques Third Edition, 3rd Editio., vol. 1, no. 5. USA: Morgan Kaufmann Publishers, Elsevier Inc, 2011.
- J. Li, ‘Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what?’, PLoS One, vol. 12, no. 8, pp. 1–16, 2017.
Index Terms
Keywords