Course Grade and GPA Prediction

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Lee and Kizilcec (2020) pdf

  • 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), for male students than female students
  • 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

  • Models predicting undergraduate course grades and average GPA
  • Students who are international, first-generation, or from low-income households were inaccurately predicted to get lower course grade and average GPA than their peer, and fairness of models improved with the inclusion of clickstream and survey data
  • Female students were inaccurately predicted to achieve greater short-term and long-term success than male students, and fairness of models improved when a combination of institutional and click data was used 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, or differences between the area under curve
  • Students with self-declared disability were predicted to pass the course more often


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


Kung & Yu (2020) pdf

  • Predicting course grades and later GPA at public U.S. university
  • Five algorithms and three metrics (independence, separation, sufficiency) analyzed
  • Poorer performance for Latinx students on course grade prediction for all three metrics; poorer performance for Latinx students on GPA prediction in terms of independence and sufficiency, but not separation
  • Poorer performance for first-generation students on course grade prediction for independence and separation, and for some algorithms for GPA prediction as well
  • Poorer performance for low-income students in several cases, about 1/3 of cases checked


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 and White students' performance, and underpredict Black, Hispanic, and Native American students' performance
  • Several fairness correction methods equalize false positive and false negative rates across groups.


Sha et al. (2022) [2]

  • Predicting course pass/fail with random forest in Open University data
  • A range of over-sampling methods tested
  • Regardless of over-sampling method used, course pass/fail performance was moderately better for males