@misc{10481/70207,
year = {2021},
month = {6},
url = {http://hdl.handle.net/10481/70207},
abstract = {Today, predictive analytics applications became an urgent desire in higher educational institutions.
Predictive analytics used advanced analytics that encompasses machine learning implementation
to derive high-quality performance and meaningful information for all education levels. Mostly know that
student grade is one of the key performance indicators that can help educators monitor their academic performance.
During the past decade, researchers have proposed many variants of machine learning techniques
in education domains. However, there are severe challenges in handling imbalanced datasets for enhancing
the performance of predicting student grades. Therefore, this paper presents a comprehensive analysis of
machine learning techniques to predict the  nal student grades in the  rst semester courses by improving
the performance of predictive accuracy. Two modules will be highlighted in this paper. First, we compare the
accuracy performance of six well-known machine learning techniques namely Decision Tree (J48), Support
Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (kNN), Logistic Regression (LR) and
Random Forest (RF) using 1282 real student's course grade dataset. Second, we proposed a multiclass prediction
model to reduce the over tting and misclassi cation results caused by imbalanced multi-classi cation
based on oversampling Synthetic Minority Oversampling Technique (SMOTE) with two features selection
methods. The obtained results showthat the proposed model integrates with RF give signi cant improvement
with the highest f-measure of 99.5%. This proposed model indicates the comparable and promising results
that can enhance the prediction performance model for imbalanced multi-classi cation for student grade
prediction.},
organization = {Science and Technology Development Fund (STDF)},
organization = {Ministry of Higher Education & Scientific Research (MHESR)	FRGS/1/2018/ICT04/UTM/01/1},
organization = {Specific Research Project (SPEV) at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic	2102-2021},
organization = {Universiti Teknologi Malaysia (UTM)	Vot-20H04},
organization = {Malaysia Research University Network (MRUN)	4L876},
publisher = {IEEE},
keywords = {Machine learning},
keywords = {Predictive model},
keywords = {Imbalanced problem},
keywords = {Student grade prediction},
keywords = {Multi-class classification},
title = {Multiclass Prediction Model for Student Grade Prediction Using Machine Learning},
doi = {10.1109/ACCESS.2021.3093563},
author = {Bujang, Siti Dianah Abdul and Herrera Viedma, Enrique},
}