Prediction of Student’s Performance With Learning Coefficients Using Regression Based Machine Learning Models

Asthana, Pallavi; Mishra, Sumita; Gupta, Nishu; Derawi, Mohammad; Kumar, Anil

doi:10.1109/access.2023.3294700

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…The number of records assessed for eligibility was 709. After screening, highly relevant studies were identified (n = 50) (Adnan et al, 2021(Adnan et al, , 2022Alamri & Alharbi, 2021;Alhazmi & Sheneamer, 2023;Aljaloud et al, 2022;Alshanqiti & Namoun, 2020;Asthana et al, 2023;Bujang et al, 2021;Chan et al, 2023;Chui et al, 2020;Feng et al, 2022;Gao et al, 2019;Ghorbani & Ghousi, 2020;Hassan et al, 2022;Hussain et al, 2021;Kastrati et al, 2020;Kusumawardani & Alfarozi, 2023;Latif et al, 2023;Liu et al, 2020;Mengash, 2020;Motz et al, 2021;Munshi & Alhindi, 2021;Nabil et al, 2021;Nguyen-Huy et al, 2022;Okoye et al, 2023;Orlando et al, 2020;Pek et al, 2023;Prabowo et al, 2021;Rafique et al, 2021;Rahman et al, 2021.…”

Section: Search Strategymentioning

confidence: 99%

Exploring the role of machine learning in forecasting student performance in education: An in-depth review of literature

Pandian,

Aziz,

Subramaniam

et al. 2024

Multidiscip. Rev.

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The literature on the role of machine learning in forecasting student performance in education lacks comprehensive data quality, equitable and interpretable models, consideration of contextual and causal factors, and the integration of human expertise. This review will explore machine learning types, algorithms, predictive performance, and impact on student performance in education. A systematic literature review based on articles published in the past 2019–2023 period Methods: The IEEE Xplore database was searched by using keywords such as "Educational Data Mining," "Student Performance Prediction," "Evaluations of Students," "Performance Analysis of Students," and "Learning Curve Prediction." were employed and 50 papers were selected. Results: The analysis of the results highlighted prominent patterns. Half of the studies favored supervised learning methods, with decision trees leading (19 instances), followed by Long Short-Term Memory and Random Forest (16 each), and K-Nearest Neighbor and Naive Bayes (12 and 11 times). Support Vector Machine and Logistic Regression were noted 10 and 9 times, respectively. Noteworthy were ANN, CNN, and Xgboost. Positive impacts were evident in 36 cases; only one showed negative effects, while 13 indicated intricate relationships. This study helped in understanding the prevalent machine learning methods used for predicting student performance, provides a benchmark for assessing the effectiveness of new or alternative techniques. Conclusion: This review highlights the varied machine learning uses in predicting student performance in education, emphasizing supervised methods, diverse algorithms, and complex intervention impacts.

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