Difference between revisions of "Course Grade and GPA Prediction"

Revision as of 20:26, 22 March 2022

Lee and Kizilcec (2020) [pdf]

Models predicting college success (or median grade or above)
Random forest algorithms performed significantly worse for male students than female students
The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values

Yu et al. (2020) [pdf]

Students who are international, first-generation, or from low-income households were inaccurately predicted to get lower course grade and average GPA than their peers
Fairness of models improved with the inclusion of clickstream and survey data

Riazy et al. (2020) [pdf]

Models predicting course outcome of students in a virtual learning environment (VLE)
Students with self-declared disability were predicted to pass the course with 16-23 percentage points in favor from the training and test set

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 Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf pdf]]
-* Model predicting college course grade of median or above
-* Out-of-the-box random forest model violates demographic parity and equality of opportunity for URM (underrepresented minority: American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than for non-URM students (White and Asian)
+* Models predicting college success (or median grade or above)
+* Random forest algorithms performed significantly worse for male students than female students
+* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values
 Yu et al. (2020) [[https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]]