At-risk/Dropout/Stopout/Graduation Prediction
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Kai et al. (2017) pdf
- Models predicting student retention in an online college program
- J48 decision trees achieved much lower Kappa and AUC for Black students than White students
- J48 decision trees achieved significantly lower Kappa but higher AUC for male students than female students
- JRip decision rules achieved almost identical Kappa and AUC for Black students and White students
- JRip decision trees achieved much lower Kappa and AUC for male students than female students
Hu and Rangwala (2020) pdf
- Models predicting if a college student will fail in a course
- Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against African-American students, while other models (particularly Logistic Regression and Rawlsian Fairness) performed far worse
- The level of bias was inconsistent across courses, with MCCM prediction showing the least bias for Psychology and the greatest bias for Computer Science
- Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against male students, performing particularly better for Psychology course.
- Other models (Logistic Regression and Rawlsian Fairness) performed far worse for male students, performing particularly worse in Computer Science and Electrical Engineering.
Anderson et al. (2019) pdf
- Models predicting six-year college graduation
- False negatives rates were greater for Latino students when Decision Tree and Random Forest yielded was used
- White students had higher false positive rates across all models, Decision Tree, SVM, Logistic Regression, Random Forest, and SGD
- False negatives rates were greater for male students than female students when SVM, Logistic Regression, and SGD were used
Christie et al. (2019) pdf
- Models predicting student's high school dropout
- The decision trees showed little difference in AUC among White, Black, Hispanic, Asian, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.
- The decision trees showed very minor differences in AUC between female and male students
Gardner, Brooks and Baker (2019) [pdf]
- Model predicting MOOC dropout, specifically through slicing analysis
- Some algorithms performed worse for female students than male students, particularly in courses with 45% or less male presence
Baker et al. (2020) [pdf]
- Model predicting student graduation and SAT scores for military-connected students
- For prediction of graduation, algorithms applying across population resulted an AUC of 0.60, degrading from their original performance of 70% or 71% to chance.
- For prediction of SAT scores, algorithms applying across population resulted in a Spearman's ρ of 0.42 and 0.44, degrading a third from their original performance to chance.
Kai et al. (2017) pdf
- Models predicting student retention in an online college program
- J-48 decision trees achieved much higher Kappa and AUC for students whose parents did not attend college than those whose parents did
- J-Rip decision rules achieved much higher Kappa and AUC for students whose parents did not attended college than those whose parents did
Yu et al. (2021) pdf
- Models predicting college dropout for students in residential and fully online program
- The model showed better recall for students who are under-represented minority (URM; not White or Asian), male, first-generation, or with greater financial needs
- Whether the socio-demographic information was included or not, the model showed worse accuracy and true negative rates for residential students who are under-represented minority (URM; not White or Asian), male, first-generation, or with greater financial needs
- Both accuracy and true negative rates were better for students who are first-generation, or with greater financial needs
Verdugo et al. (2022) pdf
- An algorithm predicting dropout from university after the first year
- Several algorithms achieved better AUC and F1 for students who attended public high schools than for students who attended private high schools.
- Several algorithms predicted better AUC for male students than female students; F1 scores were more balanced.
Sha et al. (2022) [1]
- Predicting dropout in XuetangX platform using neural network
- A range of over-sampling methods tested
- Regardless of over-sampling method used, dropout performance was slightly better for males.
Queiroga et al. (2022) pdf
- Models predicting secondary school students at risk of failure or dropping out
- Model was unable to make prediction of student success (F1 score = 0.0) for students not in a social welfare program (higher socioeconomic status)
- Model had slightly lower AUC ROC (0.52 instead of 0.56) for students not in a social welfare program (higher socioeconomic status)
Permodo et al.(2023) pdf
- Paper discusses system that predicts probabilities of on-time graduation
- Prediction is less accurate for White students than other students
- Prediction is more accurate for students with Disabilities than students without Disabilities
- Prediction is more accurate for low-income students than for non-low-income students
- Prediction is comparable for Males and Females
Cock et al.(2023) [pdf]
- Paper investigates biases in models designed to early identify middle school students at risk of failing in flipped-classroom course and open-ended exploration environment (TugLet)
- Model performs worse for students from school with higher socio-economic status in open-ended environment (FNR 0.73 for higher SES and 0.57 for medium SES).
- Model performs worse for males in open-ended environment (higher FNR for males than females)
- Model performs worse for students with diploma from foreign country in flipped classroom
- Model performs worse for females in flipped classrooms