Examining Techniques to Solving Imbalanced Datasets in Educational Data Mining Systems

Al-Ashoor, Ahmed; Abdullah, Shubair A.

doi:10.47839/ijc.21.2.2589

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…Consequently, it usually leads classifiers to become biased and produce high erroneous. Due to this, many empirical studies are interested in exploring various methods to enhance student grade prediction performance [14]- [17]. However, the methods and algorithms used in dealing with various class imbalanced distributions to predict student grades are not being highlighted and are not comprehensive enough.…”

Section: Imbalanced Classification In Student Grade Predictionmentioning

confidence: 99%

Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review

et al. 2023

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Student success is essential for improving the higher education system student outcome. One way to measure student success is by predicting students' performance based on their prior academic grades. Concerning the significance of this area, various predictive models are widely developed and applied to help the institution identify students at risk of failure. However, building a high-accuracy of the predictive model is a challenge due to the imbalanced nature of the dataset, which caused to bias results. Therefore, this study aims to review the existing research article by providing a state-of-the-art approach for handling imbalanced classification in higher education, including the best practices of dataset characteristics, methods, and comparative analysis of the proposed algorithms, focusing on student grade prediction context problems. The study also presents the most common balancing methods published from 2015 to 2021 and highlights their impact on resolving imbalanced classification in three approaches: data-level, algorithm-level, and hybridlevel. The survey results reveal that the data-level approach using SMOTE oversampling is broadly applied in determining imbalanced problems for student grade prediction. However, the application of hybrid and feature selection methods supporting the generalization of the predictive model to boost student grade prediction performance is generally lacking. Other than that, some of the strengths and weaknesses of the proposed methods are discussed and summarized for the direction of future research. The outcomes of this review will guide the professionals, practitioners, and academic researchers in dealing with imbalanced classification, mainly in the higher education field.

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Section: Imbalanced Classification In Student Grade Predictionmentioning

confidence: 99%

Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review

et al. 2023

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“…A successful approach to predicting performance can enable educators to allocate resources and tailor instruction more accurately. Furthermore, early prediction enables decision-makers to take appropriate action and implement appropriate learning to improve student success rates [18], [19]. This research identifies the interrelated as well as the most influential features [20], [21].…”

Section: Introductionmentioning

confidence: 99%

A Regression Model and a Combination of Academic and Non-Academic Features to Predict Student Academic Performance

et al. 2023

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Predicting academic performance provides an effective way for students and faculties to monitor their academic progress. The identification of the most significant features was a key outcome of this research, and the college/university databases from online learning platforms are the main academic data sets used to ascertain performance. However, previous research emphasized the addition of other significant features in the prediction of academic performance. Universities’ organizational features include non-academic essential data used in determining student success, but no research has utilized this data to predict student academic performance. Generally, to evaluate binary classification, the relationship between the predicted classifications and the true classifications is analyzed, this approach can lead to the loss of important information from the data. Furthermore, to avoid such loss, this research proposes a regression model by comparing six regression algorithms, and combining academic and non-academic features for prediction student academic performance. Among the various models examined, the gradient-boosted trees regression model demonstrated the lowest error rate. The proposed features have been observed to have a significant impact on academic performance, making them suitable for use in predictions.

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Academic Prediction in Multi-modal Learning Environments Using Data Fusion

Chango,

Logroño,

Salguero

et al. 2024

Lecture Notes in Networks and Systems

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Examining Techniques to Solving Imbalanced Datasets in Educational Data Mining Systems

Cited by 8 publications

References 19 publications

Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review

Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review

A Regression Model and a Combination of Academic and Non-Academic Features to Predict Student Academic Performance

Academic Prediction in Multi-modal Learning Environments Using Data Fusion

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