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Type :article
Subject :LB2300 Higher Education
Main Author :Ismail @ Ismail Yusuf Panessai
Additional Authors :Muhammad Modi bin Lakulu
Mohd Hishamuddin Bin Abdul Rahman
Noor Anida Zaria Binti Mohd Noor
Nor Syazwani Binti Mat Salleh
Title :PSAP: improving accuracy of students' final grade prediction using ID3 and C4.5
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2019
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Guest :Click to view PDF file

Abstract : Universiti Pendidikan Sultan Idris
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

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[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. 

 


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