Difference between revisions of "Asian/Asian-American Learners in North America"

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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, American Indian and Alaska Native, and  Native Hawaiian and Pacific Islander.
* The decision trees showed little difference in AUC among Asian, White, Black, Hispanic, American Indian and Alaska Native, and  Native Hawaiian and Pacific Islander.




Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]
Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]
* Automated scoring models for evaluating English essays, or e-rater  
* Automated scoring models for evaluating English essays, or e-rater  
* E-rater gave significantly higher score for 11th grade essays written by Asian American and Hispanic students, particularly, Hispanic female students
* E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students
* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students
* E-rater gave slightly lower score for GRE essays (argument and issue) written by Black test-takers while e-rated scores were higher for Asian test-takers in the U.S




Lee and Kizilcec (2020) [[https://arxiv.org/pdf/2007.00088.pdf](https://arxiv.org/pdf/2007.00088.pdf) pdf]
 
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 better for non-URM students (Asian and White) than for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural)
* 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
* 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
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
* 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 overpredict Asian students' performance
* Several fairness correction methods equalize false positive and false negative rates across groups.

Latest revision as of 15:03, 4 August 2022

Christie et al. (2019) pdf

  • Models predicting student's high school dropout
  • The decision trees showed little difference in AUC among Asian, White, Black, Hispanic, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.


Bridgeman et al. (2009) page

  • Automated scoring models for evaluating English essays, or e-rater
  • E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students


Lee and Kizilcec (2020) pdf

  • Models predicting college success (or median grade or above)
  • Random forest algorithms performed significantly better for non-URM students (Asian and White) than for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural)
  • 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


Jiang & Pardos (2021) pdf

  • Predicting university course grades using LSTM
  • Roughly equal accuracy across racial groups
  • 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 overpredict Asian students' performance
  • Several fairness correction methods equalize false positive and false negative rates across groups.
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