Difference between revisions of "White Learners in North America"
Jump to navigation
Jump to search
(Added Li, Xing, & Leite (2022)) |
(addition new paper) |
||
| (5 intermediate revisions by 2 users not shown) | |||
| Line 1: | Line 1: | ||
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 pdf] | ||
* Automated scoring models for evaluating English essays, or e-rater | * Automated scoring models for evaluating English essays, or e-rater | ||
| Line 9: | Line 10: | ||
* Slightly better accuracy (~1%) across racial groups when including race in model | * Slightly better accuracy (~1%) across racial groups when including race in model | ||
Zhang et al. ( | Zhang et al. (2022) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM22_paper_35.pdf] | ||
* Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process | * 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. | * For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups. | ||
| Line 19: | Line 20: | ||
* Compared members of overrepresented racial groups to members of underrepresented racial groups (overrepresented group approximately 90% White) | * 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 | * Multiple fairness approaches lead to ABROCA of under 0.01 for overrepresented versus underrepresented students | ||
Sulaiman & Roy (2022) [https://fated2022.github.io/assets/pdf/FATED-2022_paper_Sulaiman_Transformers.pdf] | |||
* Models predicting whether a law student will pass the bar exam (to practice law) | |||
* Compared White and non-White students | |||
* Models not applying fairness constraints performed significantly worse for White students in terms of ABROCA | |||
* Models applying fairness constraints performed equivalently for White and non-White students | |||
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 White students' performance | |||
* Several fairness correction methods equalize false positive and false negative rates across groups. | |||
Permodo et al. (2023) [https://www.researchgate.net/publication/370001437_Difficult_Lessons_on_Social_Prediction_from_Wisconsin_Public_Schools pdf] | |||
* Paper discusses system that predicts probabilities of on-time graduation | |||
* Prediction is less accurate for White students than other students | |||
Zhang et al.(2023) [https://learninganalytics.upenn.edu/ryanbaker/ISLS23_annotation%20detector_short_submit.pdf pdf] | |||
* Models developed to detect attributes of student feedback for other students’ mathematics solutions, reflecting the presence of three constructs:1) commenting on process, 2) commenting on the answer, and 3) relating to self. | |||
*Models have approximately equal performance for White, African American and Hispanic/Latinx students. | |||
Almoubayyed et al. (2023) [https://educationaldatamining.org/EDM2023/proceedings/2023.EDM-long-papers.18/2023.EDM-long-papers.18.pdf pdf] | |||
* Models discovering generalization of the performance for reading comprehension ability in the context of middle school students’ usage of Carnegie Learning’s ITS for mathematics instruction | |||
* Model trained on smaller dataset achieves greater fairness in prediction for white and non-white students | |||
* For model trained on larger dataset, prediction is more accurate for white students than for non-white students. | |||
Latest revision as of 12:05, 17 August 2023
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. (2022) [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
Sulaiman & Roy (2022) [2]
- Models predicting whether a law student will pass the bar exam (to practice law)
- Compared White and non-White students
- Models not applying fairness constraints performed significantly worse for White students in terms of ABROCA
- Models applying fairness constraints performed equivalently for White and non-White students
Jeong et al. (2022) [3]
- Predicting 9th grade math score from academic performance, surveys, and demographic information
- Despite comparable accuracy, model tends to overpredict White students' performance
- Several fairness correction methods equalize false positive and false negative rates across groups.
Permodo et al. (2023) pdf
- Paper discusses system that predicts probabilities of on-time graduation
- Prediction is less accurate for White students than other students
Zhang et al.(2023) pdf
- Models developed to detect attributes of student feedback for other students’ mathematics solutions, reflecting the presence of three constructs:1) commenting on process, 2) commenting on the answer, and 3) relating to self.
- Models have approximately equal performance for White, African American and Hispanic/Latinx students.
Almoubayyed et al. (2023) pdf
- Models discovering generalization of the performance for reading comprehension ability in the context of middle school students’ usage of Carnegie Learning’s ITS for mathematics instruction
- Model trained on smaller dataset achieves greater fairness in prediction for white and non-white students
- For model trained on larger dataset, prediction is more accurate for white students than for non-white students.