At-risk/Dropout/Stopout/Graduation Prediction

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