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Predicting Student Dropout and Academic Success

https://doi.org/10.3390/data7110146
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Roughly balanced bagging for imbalanced data
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Mining Student Information System Records to Predict Students’ Academic Performance
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Early Prediction of student’s Performance in Higher Education: A Case Study
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Comparison of feature importance measures as explanations for classification models
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The FAIR Guiding Principles for scientific data management and stewardship
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Diversity analysis on imbalanced data sets by using ensemble models
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A Review on Handling Imbalanced Data
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ADASYN: Adaptive synthetic sampling approach for imbalanced learning
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Exploratory Undersampling for Class-Imbalance Learning
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Motives for dropping out from higher education—An analysis of bachelor's degree students in Germany
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XGBoost
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Predicting Student Performance using Advanced Learning Analytics
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Imbalance class problems in data mining: a review
inferredcites →https://doi.org/10.1186/s41239-019-0172-zopenalex-snapshot
Using learning analytics to develop early-warning system for at-risk students
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Student dropout from universities in Europe: A review of empirical literature
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SMOTE: Synthetic Minority Over-sampling Technique
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Comparison of course completion and student performance through online and traditional courses
inferredcites →https://doi.org/10.3390/app11010237openalex-snapshot
Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review

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Prescriptive Learning Analytics for Student Dropout: Integrating Temporal Velocity and Counterfactual Explanations in Longitudinal Data
inferred← citeshttps://doi.org/10.69693/jeca.v1i2.13openalex-snapshot
Predict Students' Dropout and Academic Success with XGBoost
inferred← citeshttps://doi.org/10.70577/asce.v5i1.665openalex-snapshot
Determinantes de la deserción en la educación superior desde una perspectiva micro: un análisis de revisión bibliográfica aplicando el método PRISMA
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