Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020056" target="_blank" >RIV/62690094:18450/23:50020056 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9965398/authors#authors" target="_blank" >https://ieeexplore.ieee.org/document/9965398/authors#authors</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2022.3225404" target="_blank" >10.1109/ACCESS.2022.3225404</a>
Alternative languages
Result language
angličtina
Original language name
Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review
Original language description
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 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 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 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 classification, mainly in the higher education field.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
11
Issue of the periodical within the volume
January
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
1970-1989
UT code for WoS article
000912476000001
EID of the result in the Scopus database
2-s2.0-85144071000