Difference between revisions of "Course Grade and GPA Prediction"
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Švábenský et al. (2024) [https://educationaldatamining.org/edm2024/proceedings/2024.EDM-posters.82/2024.EDM-posters.82.pdf pdf] | |||
* Classification models for predicting grades (worse than an average grade, “unsuccessful”, or equal/better than an average grade, “successful”) | |||
* Investigating bias based on university students' regional background in the context of the Philippines | |||
* Demographic groups based on 1 of 5 locations from which students accessed online courses in Canvas | |||
* Bias evaluation using AUC, weighted F1-score, and MADD showed consistent results across all groups, no unfairness was observed | |||
Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf] | Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf] | ||
* Models predicting college success (or median grade or above) | * 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 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 | ||
* The fairness of the model | *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<br /> | |||
Yu et al. (2020) [https://files.eric.ed.gov/fulltext/ED608066.pdf pdf] | Yu et al. (2020) [https://files.eric.ed.gov/fulltext/ED608066.pdf 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 | * 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 | ||
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* More male students were predicted to pass the course than female students, but this overestimation was fairly small and not consistent across different algorithms | * 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 | *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) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 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) | |||
[https://dl.acm.org/doi/pdf/10.1145/3386527.3406755 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) [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 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) [https://ieeexplore.ieee.org/abstract/document/9849852] | |||
* 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 | |||
* | Deho et al. (2023) [https://files.osf.io/v1/resources/5am9z/providers/osfstorage/63eaf170a3fade041fe7c9db?format=pdf&action=download&direct&version=1] | ||
* 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 | |||
* Generally worse prediction for international students |
Latest revision as of 20:06, 1 September 2024
Švábenský et al. (2024) pdf
- Classification models for predicting grades (worse than an average grade, “unsuccessful”, or equal/better than an average grade, “successful”)
- Investigating bias based on university students' regional background in the context of the Philippines
- Demographic groups based on 1 of 5 locations from which students accessed online courses in Canvas
- Bias evaluation using AUC, weighted F1-score, and MADD showed consistent results across all groups, no unfairness was observed
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
Deho et al. (2023) [3]
- 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
- Generally worse prediction for international students