Difference between revisions of "Course Grade and GPA Prediction"

Revision as of 17:36, 22 March 2022

Lee and Kizilcec (2020) [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)

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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-Hu and Rangwala (2020) [https://files.eric.ed.gov/fulltext/ED608050.pdf pdf]
-*Models predicting if a college student will fail in a course
-*Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against African-American students, while other models (particularly Logistic Regression and Rawlsian Fairness) performed far worse
-*The level of bias was inconsistent across courses, with MCCM prediction showing the least bias for Psychology and the greatest bias for Computer Science
-*Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against male students, performing particularly better for Psychology course.
-*Other models (Logistic Regression and Rawlsian Fairness) performed far worse for male students, performing particularly worse in Computer Science and Electrical Engineering.
 Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf pdf]]
 * Model predicting college course grade of median or above