Prediction of the graduation rate of engineering education students using Artificial Neural Network Algorithms

Anwar, Muhammad

doi:10.24036/00411za0002

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…Consequently, the study program manager is required to keep track of student progress to reduce the number of dropouts. The development of prediction models for students' study periods using machine learning models has been done using several parameters Gender [2], GPA [3] [4], Number of semester credit units (credits), parents' job, high school major, high school city [2] [5]. In this study, a prediction model for the study period was constructed utilizing semester 1 to 4 GPA values based on XYZ University's educational program, which highlights concentration selection in semester four lectures, ages, and Gender.…”

Section: A Backgroundmentioning

confidence: 99%

Accuracy Enhancement of Prediction Method using SMOTE for Early Prediction Student's Graduation in XYZ University

Yaqin¹,

Rahardi²,

Abdulloh³

2022

IJACSA

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According to the Minister of Education and Culture of the Republic of Indonesia's regulations from 2014, one of the essential elements in implementing higher education is the student's study duration. Higher education institutions will use early graduation prediction as a guide when developing policy. According to XYZ University data, the student study period is Grade Point Average (GPA), Gender, and Age are all aspects to consider. Using a dataset of 8491 data, the Prediction of Early Graduation of Students based on XYZ University data was examined by this study, particularly in the information systems and informatics study program. The aim is to find significant features and compare three prediction models: Artificial Neural Networks (ANN), K-Nearest Neighbor (K-NN) method, and Support Vector Machines (SVM). The Challenge in the development of a prediction model is imbalanced data. The Synthetic Minority Oversampling Technique (SMOTE) handles the class imbalance problem. Next, the machine learning models are trained and then compared. Prediction results increase. The best test accuracy value is on ANN with a data Imbalance of 62.5% to 70.5% after using SMOTE, compared to the accuracy test on the K-NN method with SMOTE 69.3%, while the SVM method increased to 69.8%. The most significant increase in recall value to 71.3% occurred in the ANN.

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Section: A Backgroundmentioning

confidence: 99%

Accuracy Enhancement of Prediction Method using SMOTE for Early Prediction Student's Graduation in XYZ University

Yaqin¹,

Rahardi²,

Abdulloh³

2022

IJACSA

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Section: Introductionmentioning

confidence: 99%

“…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%

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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