Citation

Hussain, Muhammad Mubashar and Akbar, Shahzad and Hassan, Syed Ale and Aziz, Muhammad Waqas and Urooj, Farwa (2024) Prediction of Student’s Academic Performance through Data Mining Approach. Journal of Informatics and Web Engineering, 3 (1). pp. 241-251. ISSN 2821-370X

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Abstract

The universities and institutes produce a large amount of student data that can be used in a disciplinary way and useful information can be extracted by using an automated approach. Educational Data Mining (EDM) is an emerging discipline used in the educational environment to deal with big student data and extract useful information. The data mining of students’ data can help the At-risk students as well as the stakeholders by the early warning. This study aims to predict the performance of the students based on student-related data to increase the overall performance. In existing studies, insufficient attributes and complexity of network models is a problem. The student’s current records and grades need to be analyzed. In this approach, the Levenberg Marquardt Algorithm (MLA) deep learning algorithm is used. The data consists of the class test, attendance, assignment and midterm scores. The neural network model consists of four input variables, three hidden and one output layer. The performance of the deep neural network is evaluated by accuracy, precision, recall and F1 score. The proposed model gained a higher accuracy of 88.6% than existing studies. The study successfully predicts the student's final grades using current academic records. This research will be beneficial to the students, educators and educational authorities as a whole.

Item Type:	Article
Uncontrolled Keywords:	Data mining, Levenberg marquardt algorithm, Educational data mining, Deep learning, Neural network
Subjects:	L Education > LB Theory and practice of education L Education > LB Theory and practice of education > LB1025-1050.75 Teaching (Principles and practice)
Divisions:	Others
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	02 Apr 2024 07:28
Last Modified:	02 Apr 2024 07:28
URII:	http://shdl.mmu.edu.my/id/eprint/12254

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