Difference between revisions of "Indigenous Learners in North America"
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Lee and Kizilcec (2020) | Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf] | ||
*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 underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian) | *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) | ||
*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, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values | ||
Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf] | |||
*Models predicting student's high school dropout | |||
*The decision trees showed little difference in AUC among American Indian and Alaska Native, White, Black, Hispanic, Asian, and Native Hawaiian and Pacific Islander. | |||
*The decision trees showed very minor differences in AUC between female and male students | |||
Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf] | |||
* Predicting university course grades using LSTM | |||
* Roughly equal accuracy across racial groups (including Native American and Pacific Islander students) | |||
* Slightly better accuracy (~1%) across racial groups when including race in model | |||
Jeong et al. (2022) [https://fated2022.github.io/assets/pdf/FATED-2022_paper_Jeong_Racial_Bias_ML_Algs.pdf] | |||
* Predicting 9th grade math score from academic performance, surveys, and demographic information | |||
* Despite comparable accuracy, model tends to underpredict Native American students' performance | |||
* Several fairness correction methods equalize false positive and false negative rates across groups. | |||
Latest revision as of 15:05, 4 August 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)
- The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values
Christie et al. (2019) pdf
- Models predicting student's high school dropout
- The decision trees showed little difference in AUC among American Indian and Alaska Native, White, Black, Hispanic, Asian, and Native Hawaiian and Pacific Islander.
- The decision trees showed very minor differences in AUC between female and male students
Jiang & Pardos (2021) pdf
- Predicting university course grades using LSTM
- Roughly equal accuracy across racial groups (including Native American and Pacific Islander students)
- Slightly better accuracy (~1%) across racial groups when including race in model
Jeong et al. (2022) [1]
- Predicting 9th grade math score from academic performance, surveys, and demographic information
- Despite comparable accuracy, model tends to underpredict Native American students' performance
- Several fairness correction methods equalize false positive and false negative rates across groups.