Difference between revisions of "Course Grade and GPA Prediction"

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* Predicting course grades and later GPA at public U.S. university
* Predicting course grades and later GPA at public U.S. university
* Five algorithms and three metrics (independence, separation, sufficiency) analyzed
* 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 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 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 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
* Poorer performance for low-income students in several cases, about 1/3 of cases checked

Revision as of 10:53, 13 June 2022

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