Difference between revisions of "Gender: Male/Female"

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(Added Verdugo et al (2022))
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* A later version of automated scoring models for evaluating English essays, or e-rater
* A later version of automated scoring models for evaluating English essays, or e-rater
* E-Rater system correlated comparably well with human rater when assessing TOEFL and GRE essays written by male and female students
* E-Rater system correlated comparably well with human rater when assessing TOEFL and GRE essays written by male and female students


Verdugo et al. (2022) [https://https://www.researchgate.net/profile/Jonathan-Vasquez-Verdugo/publication/359176069_FairEd_A_Systematic_Fairness_Analysis_Approach_Applied_in_a_Higher_Educational_Context/links/622ba9e89f7b324634245afa/FairEd-A-Systematic-Fairness-Analysis-Approach-Applied-in-a-Higher-Educational-Context.pdf pdf]
Verdugo et al. (2022) [https://https://www.researchgate.net/profile/Jonathan-Vasquez-Verdugo/publication/359176069_FairEd_A_Systematic_Fairness_Analysis_Approach_Applied_in_a_Higher_Educational_Context/links/622ba9e89f7b324634245afa/FairEd-A-Systematic-Fairness-Analysis-Approach-Applied-in-a-Higher-Educational-Context.pdf pdf]
* An algorithm predicting dropout from university after the first year
* An algorithm predicting dropout from university after the first year
* Several algorithms achieved better AUC for male than female students; results were mixed for F1.
* Several algorithms achieved better AUC for male than female students; results were mixed for F1.
Zhang et al. (in press)
* 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 gender groups.
* No gender group consistently had best-performing detectors

Revision as of 13:24, 3 June 2022

Kai et al. (2017) pdf

  • Models predicting student retention in an online college program
  • J48 decision trees achieved significantly lower Kappa but higher AUC for male students than female students
  • JRip decision rules achieved much lower Kappa and AUC for male students than female students


Christie et al. (2019) pdf

  • Models predicting student's high school dropout
  • The decision trees showed very minor differences in AUC between female and male students


Hu and Rangwala (2020) pdf

  • Models predicting if a college student will fail in a course
  • Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against male students, performing particularly better for Psychology course.
  • Other models (Logistic Regression and Rawlsian Fairness) performed far worse for male students, performing particularly worse in Computer Science and Electrical Engineering.


Anderson et al. (2019) pdf

  • Models predicting six-year college graduation
  • False negatives rates were greater for male students than female students when SVM, Logistic Regression, and SGD were used


Gardner, Brooks and Baker (2019) pdf

  • Model predicting MOOC dropout, specifically through slicing analysis
  • Some algorithms studied performed worse for female students than male students, particularly in courses with 45% or less male presence


Riazy et al. (2020) pdf

  • Model predicting course outcome
  • Marginal differences were found for prediction quality and in overall proportion of predicted pass between groups
  • Inconsistent in direction between algorithms.


Lee and Kizilcec (2020) pdf

  • Models predicting college success (or median grade or above)
  • Random forest algorithms performed significantly worse for male students than female students
  • 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


Yu et al. (2020) pdf

  • Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
  • Female students were inaccurately predicted to achieve greater short-term and long-term success than male students.
  • The fairness of models improved when a combination of institutional and click data was used in the model


Yu et al. (2021) pdf

  • Models predicting college dropout for students in residential and fully online program
  • Whether the socio-demographic information was included or not, the model showed worse true negative rates and worse accuracy for male students
  • The model showed better recall for male students, especially for those studying in person
  • The difference in recall and true negative rates were lower, and thus fairer, for male students studying online if their socio-demographic information was not included in the model


Riazy et al. (2020) pdf

  • Models predicting course outcome of students in a virtual learning environment (VLE)
  • More male students were predicted to pass the course than female students, but this overestimation was fairly small and not consistent across different algorithms
  • Among the algorithms, Naive Bayes had the lowest normalized mutual information value and the highest ABROCA value


Bridgeman et al. (2009) pdf

  • Automated scoring models for evaluating English essays, or e-rater
  • E-Rater system performed comparably accurately for male and female students when assessing their 11th grade essays


Bridgeman et al. (2012) pdf

  • A later version of automated scoring models for evaluating English essays, or e-rater
  • E-Rater system correlated comparably well with human rater when assessing TOEFL and GRE essays written by male and female students


Verdugo et al. (2022) pdf

  • An algorithm predicting dropout from university after the first year
  • Several algorithms achieved better AUC for male than female students; results were mixed for F1.


Zhang et al. (in press)

  • 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 gender groups.
  • No gender group consistently had best-performing detectors