Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review
Metadatos
Mostrar el registro completo del ítemEditorial
IEEE
Materia
Imbalanced classification Prediction model Machine learning Student grade prediction Education Systematic literature review
Fecha
2022-11-28Referencia bibliográfica
S. D. Abdul Bujang... [et al.], "Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review," in IEEE Access, vol. 11, pp. 1970-1989, 2023, doi: [10.1109/ACCESS.2022.3225404]
Patrocinador
Ministry of Education FRGS/1/2022/ICT08/UTM/01/1; Faculty of Informatics and Management, University of Hradec Kralove 2102/2022Resumen
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 signi cance 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 predictive model
is challenging due to the dataset's imbalanced nature, which caused biased results. Therefore, this study
aims to review the existing research article by providing a state-of-the-art approach for handling imbalanced
classi cation 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 classi cation in three approaches: data-level, algorithm-level, and
hybrid-level. 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
classi cation, mainly in the higher education eld.