Socioeconomic Status

From Penn Center for Learning Analytics Wiki
Jump to navigation Jump to search

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