Socioeconomic Status

Yudelson et al. (2014) pdf

• Models discovering generalizable sub-populations of students across different schools to predict students' learning with Carnegie Learning’s Cognitive Tutor (CLCT)

• Models trained on schools with a high proportion of low-SES student performed worse than those trained with medium or low proportion
• Models trained on schools with low, medium proportion of SES students performed similarly well for schools with high proportions of low-SES students

Yu et al. (2020) pdf

• Models predicting undergraduate course grades and average GPA

• Students from low-income households were inaccurately predicted to perform worse for both short-term (final course grade) and long-term (GPA)
• Fairness of model improved if it included only clickstream and survey data

Yu et al. (2021) pdf

• Models predicting college dropout for students in residential and fully online program
• Whether the socio-demographic information was included or not, the model showed worse accuracy and true negative rates for residential students with greater financial needs
• The model showed better recall for students with greater financial needs, especially for those studying in person

Kung & Yu (2020) pdf

• Predicting course grades and later GPA at public U.S. university
• Equal performance for low-income and upper-income students in course grade prediction for several algorithms and metrics
• Worse performance on independence for low-income students than high-income students in later GPA prediction for four of five algorithms; one algorithm had worse separation and two algorithms had worse sufficiency

Litman et al. (2021) html

• Automated essay scoring models inferring text evidence usage
• All algorithms studied have less than 1% of error explained by whether student receives free/reduced price lunch

Queiroga et al. (2022) pdf

• Models predicting secondary school students at risk of failure or dropping out
• Models achieved high performances with an AUC higher than 0.90 and F1 higher than 0.88
• Equal performance for both genders students who participated in the educational system and completed their studies
• Students engaged in social welfare programs results in fewer problems in education
• The F1 score for social welfare program is 0.80, while for no social welfare program is 0, indicating bias toward social welfare program students
• The two most important features to predict students at risk in secondary school in Uruguay early are First-year primary school zones (rural or urban) and sixth-year assessment-based grouping