Difference between revisions of "White Learners in North America"
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* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups. | * For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups. | ||
* No racial/ethnic group consistently had best-performing detectors | * No racial/ethnic group consistently had best-performing detectors | ||
Li, Xing, & Leite (2022) [https://dl.acm.org/doi/pdf/10.1145/3506860.3506869?casa_token=OZmlaKB9XacAAAAA:2Bm5XYi8wh4riSmEigbHW_1bWJg0zeYqcGHkvfXyrrx_h1YUdnsLE2qOoj4aQRRBrE4VZjPrGw pdf] | |||
* Models predicting whether two students will communicate on an online discussion forum | |||
* Compared members of overrepresented racial groups to members of underrepresented racial groups (overrepresented group approximately 90% White) | |||
* Multiple fairness approaches lead to ABROCA of under 0.01 for overrepresented versus underrepresented students | |||
Revision as of 08:20, 4 July 2022
Bridgeman et al. (2009) pdf
- Automated scoring models for evaluating English essays, or e-rater
- The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students than other groups
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
Zhang et al. (in press) [1]
- Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process
- For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.
- No racial/ethnic group consistently had best-performing detectors
Li, Xing, & Leite (2022) pdf
- Models predicting whether two students will communicate on an online discussion forum
- Compared members of overrepresented racial groups to members of underrepresented racial groups (overrepresented group approximately 90% White)
- Multiple fairness approaches lead to ABROCA of under 0.01 for overrepresented versus underrepresented students