Year: 2016
Type: article
Abstract: The main objective of this study is to apply data mining techniques to predict and analyze students' academic performance based on their academic record and forum participation.Educational Data Mining (EDM) is an emerging tool for academic intervention.The educational institutions can use EDU for extensive analysis of students' characteristics.In this study, we have collected students' data from two undergraduate courses.Three different data mining classification algorithms (Naï ve Bayes, Neural Network, and Decision Tree) were used on the dataset.The prediction performance of three classifiers are measured and compared.It was observed that Naï ve Bayes classifier outperforms other two classifiers by achieving overall prediction accuracy of 86%.This study will help teachers to improve student academic performance.
Authors: Ahmed Mueen, Bassam Zafar, Umar Manzoor
Institution: King Abdulaziz University
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W2558171996