Improving SVM Classification Performance on Unbalanced Student Graduation Time Data Using SMOTE

Anggrawan, Anthony; Hairani, Hairani; Satria, Christofer

doi:10.18178/ijiet.2023.13.2.1806

Cited by 9 publications

(10 citation statements)

References 44 publications

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“…We cross-validations, also referred to as k-folds, as one of the validation techniques when performing model validation [9], [32]. At this stage, the determination test is also used, and the confusion matrix is used to compute accuracy, recall, and precision [1], [4]. The performance of the Confusion Matrix can be assessed using…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…The lack of research on the proper metrics to use in evaluating student performance and the incompatibility of the current models with institutional frameworks make this issue very important to study. Predicting student performance is one effort in anticipating student failures in taking courses [1], [2]. Numerous publicly available data in the area of education can be examined with the goal of improving student academic performance.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

Ismanto,

Ghani,

Aziz

et al. 2023

Advances in Social Science, Education and Humanities Research

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Accurate prediction of student performance is crucial in learning analytics to prevent course failures and improve academic outcomes. However, publicly accessible educational data often contains noise and imbalanced data distributions, requiring effective handling techniques. In this study, we propose a novel approach that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Long Short-Term Memory (LSTM) and Feed-Forward Neural Network (FFNN) models for performance prediction in virtual learning environments (VLEs). Our experimental results show that utilizing the SMOTE technique significantly improves the accuracy of predicting student withdrawals, with the LSTM model achieving the highest accuracy of 94.90% in the 25th week of data testing. These findings indicate the effectiveness of the SMOTE technique in addressing data imbalance issues in VLE datasets and the potential of our proposed deep learning models in accurately predicting student performance. The implications of our study are significant for learning analytics and educational institutions, as accurate prediction of student performance can inform early interventions and personalized support. Future research could explore the generalizability of our approach in diverse educational contexts and the integration of additional features for further improving prediction accuracy. Hence, our study contributes to the field of learning analytics by proposing a novel approach that combines SMOTE with deep learning models for student performance prediction in VLEs. Our findings highlight the potential of our approach in addressing data imbalance challenges and accurately predicting student performance, with implications for enhancing student success in educational settings.

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Section: Evaluation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

Ismanto,

Ghani,

Aziz

et al. 2023

Advances in Social Science, Education and Humanities Research

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“…Students who graduate on time (GOT) are those who completed their studies timely within the time frame specified by the university [1,2]. Its significance extends beyond individual achievement, serving as a metric for evaluating institutional quality and performance [1,3]. However, the journey towards GOT is often beset by challenges when the academic success is multifaceted [4], with students grappling to maintain academic momentum and overcome obstacles that may impede timely completion.…”

Section: Introductionmentioning

confidence: 99%

“…However, the journey towards GOT is often beset by challenges when the academic success is multifaceted [4], with students grappling to maintain academic momentum and overcome obstacles that may impede timely completion. These challenges manifest in various forms, including the accumulation of failed courses over semesters [3,5]. While certain measures such as adjusting passing thresholds or attendance tracking might seem to bolster graduation rates, concerns linger regarding their impact on the overall quality of graduates [6].…”

Section: Introductionmentioning

confidence: 99%

Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

Law,

Ting,

et al. 2024

JIWE

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In education, detecting students graduating on time is difficult due to high data complexity. Researchers have employed various approaches in identifying on-time graduation with Machine Learning, but it remains a challenging task due to the class imbalance in the dataset. This study has aimed to (i) compare various class imbalance treatment methods with different sampling ratios, (ii) propose an ensemble class imbalance treatment method in mitigating the problem of class imbalance, and (iii) develop and evaluate predictive models in identifying the likelihood of students graduating on time during their studies in university. The dataset is collected from 4007 graduates of a university from year 2021 and 2022 with 41 variables. After feature selection, various class imbalance treatment methods were compared with different sampling ratios ranging from 50% to 90%. Moreover, Ensemble-SMOTE is proposed to aggregate the dataset generated by Synthetic Minority Oversampling Technique variants in mitigating the problem of class imbalance effectively. The dataset generated by class imbalance treatment methods were used as the input of the predictive models in detecting on-time graduation. The predictive models were evaluated based on accuracy, precision, recall, F0.5-score, F1-score, F2-score, Area under the Curve, and Area Under the Precision-Recall Curve. Based on the findings, Logistic Regression with Ensemble-SMOTE outperformed other predictive models, and class imbalance treatment methods by achieving the highest average accuracy (87.24), recall (92.50%), F1-score (91.30%), and F2-score (92.02%) from 6th until 10th trimester. To assess the effectiveness of class imbalance treatment methods, Friedman test is performed to determine on significant difference between the models after applying Shapiro-Wilk test in normality test. Consequently, Ensemble-SMOTE is ranked as the top-performers by achieving the lowest value in the average rank based on the performance metrics. Additional research could incorporate and examine more complicated approaches in mitigating class imbalance when the dataset is highly imbalanced.

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“…One of them can use the Machine Learning Algorithm as a method of mining knowledge from a data set [1]. Several Machine Learning algorithms used in predicting student graduation, such as the Support Vector Machine (SVM) [2], C4.5 [3][4], K-Nearest Neighbor (KNN) [5], Correlated Naive Bayes (C-NBC) [6] and Nave Bayes algorithms [7][8] [9], can be used.…”

Section: Introductionmentioning

confidence: 99%

Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms

Tholib,

Fadli Hidayat,

Yono

et al. 2023

IJECSA

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Student graduation is a very important element for universities because it relates to college accreditation assessment. One of them is at the Faculty of Engineering Nurul Jadid University, which has problems completing the study period within a predetermined time. So that it can be detrimental because accreditation is less than optimal, and the number of active students makes it less ideal in teaching and learning activities. This study aimed to compare the level of accuracy using the C4.5 algorithm and Naïve Bayes method in predicting graduation on time. The C4.5 and Naïve Bayes algorithms are one of the methods in the algorithm for classifying. Tests were carried out using the C4.5 and Naïve Bayes algorithms using Google Colab with Python programming language, then validated using 10-fold cross-validation. The results of this study indicate that the Naïve Bayes method has a higher accuracy value with an accuracy rate of 96.12%, while the C4.5 algorithm method is 93.82%.

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Improving SVM Classification Performance on Unbalanced Student Graduation Time Data Using SMOTE

Cited by 9 publications

References 44 publications

Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

Enhancing Student Performance Prediction through LSTM-based Deep Learning Models with Unbalanced Data Handling using Oversampling Approach

Ensemble-SMOTE: Mitigating Class Imbalance in Graduate on Time Detection

Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms

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