Difference between revisions of "Socioeconomic Status"

Revision as of 11:35, 4 June 2023

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

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
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)

@@ Line 34: / Line 34: @@
-Queiroga et al. (2022) [https://doi.org/10.3390/info13090401 pdf]
+Queiroga et al. (2022) [https://www.mdpi.com/2078-2489/13/9/401 pdf]
 * Models predicting secondary school students at risk of failure or dropping out
-* Models achieved high performances with an AUC ROC higher than 0.90 and F1 higher than 0.88
+* Model was unable to make prediction of student success (F1 score = 0.0) for students not in a social welfare program (higher socioeconomic status)
-* Equal performance for both genders students who participated in the educational system and completed their studies
+* Model had slightly lower AUC ROC (0.52 instead of 0.56) for students not in a social welfare program (higher socioeconomic status)
-* 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