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Predicting Student Dropout in an International Setting Using Explainable Machine Learning

https://doi.org/10.2139/ssrn.5420637

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Support-Vector Networks

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Student dropout at university: a phase-orientated view on quitting studies and changing majors

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International students’ experience in Australian higher education: can we do better?

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Measuring Determinants of Student Return VS. Dropout/Stopout VS. Transfer: A First-to-Second Year Analysis of New Freshmen

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Dropout prediction in e-learning courses through the combination of machine learning techniques

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Educational data mining: A survey from 1995 to 2005

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A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition

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Predicting who will drop out of nursing courses: A machine learning exercise

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Emotional intelligence and student retention: Predicting the successful transition from high school to university

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The Worldwide Governance Indicators: Methodology and Analytical Issues

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Random Forests

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Why do higher education students drop out? Evidence from Spain

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Predicting student dropout: A machine learning approach

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A Real-Life Machine Learning Experience for Predicting University Dropout at Different Stages Using Academic Data

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The Regression Analysis of Binary Sequences

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XGBoost

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Should College Dropout Prediction Models Include Protected Attributes?

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More Is More or More Is Less? Parental Financial Investments during College

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First-Year Students’ Adjustment to University Life as a Function of Relationships with Parents

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Predicting academic success in higher education: literature review and best practices

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Extracting topological features to identify at-risk students using machine learning and graph convolutional network models

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To Explain or to Predict?

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SMOTE: Synthetic Minority Over-sampling Technique

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Predicting Student Attrition with Data Mining Methods

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Limits of Theory and Practice in Student Attrition

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Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program

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Predicting Student Dropout and Academic Success

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