Difference between revisions of "Course Grade and GPA Prediction"
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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 | |||
Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf pdf]] | Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf pdf]] | ||
* Model predicting college course grade of median or above | * 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) | * 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) [[https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]] | Yu et al. (2020) [[https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]] | ||
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* 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 | * 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 | * Fairness of models improved with the inclusion of clickstream and survey data | ||
Riazy et al. (2020) [[https://www.scitepress.org/Papers/2020/93241/93241.pdf pdf]] | Riazy et al. (2020) [[https://www.scitepress.org/Papers/2020/93241/93241.pdf pdf]] | ||
* Models predicting course outcome of students in a virtual learning environment (VLE) | * 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 | * Students with self-declared disability were predicted to pass the course with 16-23 percentage points in favor from the training and test set | ||
Revision as of 17:27, 22 March 2022
Hu and Rangwala (2020) 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
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]
- 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