Difference between revisions of "Indigenous Learners in North America"
Jump to navigation
Jump to search
(Added Jiang & Pardos) |
(Added Jeong et al (2022)) |
||
| (One intermediate revision by the same user not shown) | |||
| Line 7: | Line 7: | ||
Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf] | Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf] | ||
*Models predicting student's high school dropout | *Models predicting student's high school dropout | ||
*The decision trees showed little difference in AUC among White, Black, Hispanic, Asian | *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 | *The decision trees showed very minor differences in AUC between female and male students | ||
| Line 15: | Line 15: | ||
* Roughly equal accuracy across racial groups (including Native American and Pacific Islander students) | * 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 | * 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 16:05, 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.