Difference between revisions of "Course Grade and GPA Prediction"
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* Fairly marginal differences were found for prediction quality and in overall proportion of predicted pass between groups | * Fairly marginal differences were found for prediction quality and in overall proportion of predicted pass between groups | ||
* Inconsistent in direction between algorithms. | * Inconsistent in direction between algorithms. | ||
Li and colleagues (2021) [[https://arxiv.org/pdf/2103.15212.pdf pdf]] | |||
*Model predicting student achievement on the standardized examination PISA | |||
*Inaccuracy of the U.S.-trained model was greater for students from countries with lower scores of national development (e.g. Indonesia, Vietnam, Moldova) | |||
Revision as of 02:52, 24 January 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]
- Model predicting undergraduate course grades and average GPA
- students of several racial backgrounds were inaccurately predicted to perform worse than other students
Riazy et al. (2020) [pdf]
- Model predicting course outcome
- Fairly marginal differences were found for prediction quality and in overall proportion of predicted pass between groups
- Inconsistent in direction between algorithms.
Li and colleagues (2021) [pdf]
- Model predicting student achievement on the standardized examination PISA
- Inaccuracy of the U.S.-trained model was greater for students from countries with lower scores of national development (e.g. Indonesia, Vietnam, Moldova)