UMP Institutional Repository

Adaboost-multilayer perceptron to predict the student’s performance in software engineering

Ahmad Firdaus, Zainal Abidin and Mohd Faaizie, Darmawan and Mohd Zamri, Osman and Shahid, Anwar and Shahreen, Kasim and Yunianta, Arda and Sutikno, Tole (2019) Adaboost-multilayer perceptron to predict the student’s performance in software engineering. Bulletin of Electrical Engineering and Informatics, 8 (4). pp. 1556-1562. ISSN 2089-3191 (Print); 2302-9285 (Online)

[img]
Preview
Pdf
Adaboost-multilayer perceptron to predict the student’s performance .pdf

Download (272kB) | Preview

Abstract

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Adaboost; Education; Machine learning; Multilayer perceptron; Software engineering
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 28 Feb 2020 09:02
Last Modified: 28 Feb 2020 09:02
URI: http://umpir.ump.edu.my/id/eprint/26805
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item