Difference between revisions of "Course Grade and GPA Prediction"
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* Models predicting college success (or median grade or above) | * Models predicting college success (or median grade or above) | ||
* Random forest algorithms performed significantly worse for male students than female students | *Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian), 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 | * The fairness of the model for URM and male students, 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]] | Yu et al. (2020) [[https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]] | ||
Revision as of 19:30, 22 March 2022
Lee and Kizilcec (2020) [pdf]
- Models predicting college success (or median grade or above)
- Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian), for male students than female students
- The fairness of the model for URM and male students, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values
Yu et al. (2020) [pdf]
- Models predicting undergraduate course grades and average GPA
- 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