Gender: Male/Female

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. (2022)

• 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

Rzepka et al. (2022) pdf

• Models predicting whether student will quit spelling learning activity without completing
• Multiple algorithms have slightly better false positive rates and AUC ROC for male students than female students, but equivalent performance on multiple other metrics.

Li, Xing, & Leite (2022) pdf

• Models predicting whether two students will communicate on an online discussion forum
• Multiple fairness approaches lead to ABROCA of under 0.01 for female versus male students

Sha et al. (2021) pdf

• Models predicting a MOOC discussion forum post is content-relevant or content-irrelevant
• Some algorithms achieved ABROCA under 0.01 for female students versus male students,

but other algorithms (Naive Bayes) had ABROCA as high as 0.06

• Balancing the size of each group in the training set reduced ABROCA

Litman et al. (2021) html

• Automated essay scoring models inferring text evidence usage
• All algorithms studied have less than 1% of error explained by whether student is female and male

Sha et al. (2022) [1]

• Three data sets and algorithms: predicting course pass/fail (random forest), dropout (neural network), and forum post relevance (neural network)
• A range of over-sampling methods tested
• Regardless of over-sampling method used, course pass/fail performance was moderately better for males, dropout performance was slightly better for males, and forum post relevance performance was moderately better for females.

Deho et al. (2023) [2]

• Predicting whether course grade will be above or below 0.5
• Better prediction for female students in some courses, better prediction for male students in other courses

Permodo et al. (2023) pdf

• Paper discusses system that predicts probabilities of on-time graduation
• DEWS prediction is comparable for males and females

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 males and females.

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 male and female students
• For model trained on larger dataset, prediction is more accurate for female students than male students.

Chiu (2020) pdf

• Model identifies affective states (boredom, concentration, confusion, frustration, off task and gaming) of middle school students’ online mathematics learning in predicting their choice to study STEM in higher education.
• Model detects interaction with the ASSISTments system
• Model performs better for males (AUC =0.641 for RFPS; AUC =0.571 for LR) than female students (AUC = 0.492 for RFPS; AUC=0.535 for LR).

Cock et al.(2023) [pdf]

• Paper investigates biases in models designed to early identify middle school students at risk of failing in flipped-classroom course and open-ended exploration environment (TugLet)
• Model performs worse for males in open-ended environment (FNR=0.70 for males and FNR of 0.53 for females)
• Model performs worse for females in flipped classrooms(FNR=0.56 for females and FNR of 0.43 for males)