PSAP: improving accuracy of students

Ismail @ Ismail Yusuf Panessai

Abstract

This study was aimed to increase the performance of the Predicting Student Academic Performance (PSAP) system, and the outcome is to develop a web application that can be used to analyze student performance during present semester. Development of the web-based application was based on the evolutionary prototyping model. The study also analyses the accuracy of the classifier that is constructed for the prediction features in the web application. Qualitative approaches by user evaluation questionnaire were used for this study. A number of few personnel expert users which are lecturers from Universiti Pendidikan Sultan Idris were chosen as respondents. Each respondent is instructed to answer a total of 27 questions regarding respondent’s background and web application design. The accuracy of the classifier for the prediction features is tested by using the confusion matrix by using the test set of 24 rows. The findings showed the views of respondents on the aspects of interface design, functionality, navigation, and reliability of the web-based application that is developed. The result also showed that accuracy for the classifier constructed by using ID3 classification model (C4.5) is 79.18% and the highest compared to Naïve Bayes and Generalized Linear classification model

References

[1] Cherif, A. H., Adams, G. E., Movahedzadeh, F., Martyn, M. A., & Jeremy, D. (2014). Why DoStudents Fail? Faculty's Perspective. 2014 Collection of Papers - Creating & Supporting Learning Environments.

[2] Nghe, N. T., Janecek, P., & Haddawy, P. (2007). A Comparative Analysis of Techniques for Predicting Academic Performance. 2007 37th Annual Frontiers in Education Conference - Global Engineering: Knowledge Without Borders, Opportunities Without Passports (pp. T2G-7- T2G-12). Milwaukee, WI: IEEE.

[3] Amirah, M., Wahidah, H., & Nur'aini, A. (2015). A Review on Predicting Student’s Performance using Data Mining Techniques. Procedia Computer Science, 72, 414-422.

[4] Osmanbegovi?, E., & Suljic, M. (2012). Data Mining Approach for Predicting Student Performance. Journal of Economics and Business/Economic Review, 10, 3-12.

[5] Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., & Honrao, V. (2013). Predicting Students’ Performance Using ID3 And C4.5 Classification Algorithms. International Journal of Data Mining & Knowledge Management Process (IJDKP), 3(5).

[6] Nagy, H. M., Aly, W. M., & Hegazy, O. F. (2013). An Educational Data Mining System for Advising Higher Education Students. International Journal of Computer, Control, Quantum and Information Engineering, 7(10), 1266-1270.

[7] Chen, J.-F., Hsieh, H.-N., & Do, Q. H. (2014). Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks. Algorithms, 7, 538-553.

[8] Natek, S., & Zwilling, M. (2014). Student data mining solution–knowledge management system related to higher education institutions. Expert Systems with Applications, 41, 6400-6407.

[9] Hamsa, H., Indiradevi, S., & Kizhakkethottam, J. J. (2016). Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm. Procedia Technology, 25, 326-332.

[10] Sweeney, M., Rangwala, H., Lester, J., & Johri, A. (2016). Next-Term Student Performance Prediction: A Recommender Systems Approach. JEDM | Journal of Educational Data Mining, 8(1), 22-51.

[11] Adejo, O. W., & Connolly, T. (2017). Predicting student academic performance using multimodel heterogeneous ensemble approach. Journal of Applied Research in Higher Education, 10(1), 61-75.

[12] Chiheb, F., Boumahdi, F., Bouarfa, H., & Boukraa, D. (2017). Predicting Students Performance Using Decision Trees: Case of an Algerian University. 2017 International Conference on Mathematics and Information Technology (ICMIT) (pp. 113-121). Adrar: IEEE.

[13] Livieris, I., Drakopoulou, K., Kotsilieris, T., Tampakas, V., & Pintelas, P. (2017). DSS-PSP - A Decision Support Software for Evaluating Students’ Performance. In G. Boracchi, L. Iliadis, C. Jayne, & A. Likas, Engineering Applications of Neural Networks. EANN 2017 (Vol. 744, pp. 63-74). Athen: Springer, Cham.