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Alias, Muhamad Aqif Hadi
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Computer Science & IT Electrical & Electronics Engineering

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Student performance classification: a comparison of feature selection methods based on online learning activities Alias, Muhamad Aqif Hadi; Abdul Aziz, Mohd Azri; Hambali, Najidah; Taib, Mohd Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4675-4685

Abstract

The classification of student performance involves categorizing students' performance using input data such as demographic information and examination results. However, our study introduces a novel approach by emphasizing students' online learning activities as a rich data source. To avoid misinterpretation during the classification, we therefore presented a study comparing several feature selection (FS) methods combined with artificial neural network (ANN), for classifying students’ performance based on their online learning activities. At first, we focused on tackling the issue of missing values by implementing data cleaning using variance threshold. Feature selection techniques were implemented which encompass both filter-based (information gain, chi-square, Pearson correlation) and wrapper-based, sequential selection (forward and backward) techniques. In the classification stage, multi-layer perceptron (MLP) was used with the default hyperparameters and 5-fold cross-validation along with synthetic minority oversampling technique (SMOTE) were also applied to each method. We evaluated each feature selection method's performance using key metrics: accuracy, precision, recall, and F1-score. The outcomes highlighted information gain and sequential selection (forward and backward) as the top-performing methods, all achieving 100% accuracy. This research underscores the potential of leveraging online learning activities for robust student performance classification within the specified constraints.
Development of machine learning algorithms in student performance classification based on online learning activities Alias, Muhamad Aqif Hadi; Aziz, Mohd Azri Abdul; Hambali, Najidah; Taib, Mohd Nasir
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7126-7136

Abstract

The field of educational data mining has gained significant traction for its pivotal role in assessing students' academic achievements. However, to ensure the compatibility of algorithms with the selected dataset, it is imperative for a comprehensive analysis of the algorithms to be done. This study delved into the development of machine learning algorithms utilizing students' online learning activities to effectively classify their academic performance. In the data cleaning stage, we employed VarianceThreshold for discarding features that have all zeros. Feature selection and oversampling techniques were integrated into the data preprocessing, using information gain to facilitate efficient feature selection and synthetic minority oversampling technique (SMOTE) to address class imbalance. In the classification phase, three supervised machine learning algorithms: k-nearest neighbors (KNN), multi-layer perceptron (MLP), and logistic regression (LR) were implemented, with 3-fold cross-validation to enhance robustness. Classifiers’ performance underwent refinement through hyperparameter tuning via GridSearchCV. Evaluation metrics, encompassing accuracy, precision, recall, and F1-score, were meticulously measured for each classifier. Notably, the study revealed that both MLP and LR achieved impeccable scores of 100% across all metrics, while KNN exhibited a noticeable performance boost after using hyperparameter tuning.
Feature selection techniques and classification algorithms for student performance classification: a review Alias, Muhamad Aqif Hadi; Hambali, Najidah; Abdul Aziz, Mohd Azri; Taib, Mohd Nasir; Jailani, Rozita
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3230-3243

Abstract

The process of categorizing students’ performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students’ performance. However, this study entails the need to analyze the diverse attributes of students’ information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students’ online learning activities, utilizing a variety of classification algorithms.
Co-Authors Abdul Aziz, Mohd Azri Aziz, Mohd Azri Abdul Hambali, Najidah Mohd Nasir Taib Rozita Jailani, Rozita