Marmoset Auto-grading Features for Predicting Exam Performance
The student-performance models use four categories of data from the Marmoset auto-grading system: (1) the per-task passing rate for each student's best submission, (2) public, release, and hidden test-case outcomes for the best submission, (3) the interval between submission time and the task deadline, and (4) the number of submissions. The dataset contains 28 tasks: the first 16 were due before the midterm exam, and the remaining 12 were due before the final exam.
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Introduction (Predicting student performance using data from an Auto-grading system)
Related Work (Predicting student performance using data from an Auto-grading system)
Student Performance and Marmoset (Predicting student performance using data from an Auto-grading system)
Reference for (Predicting student performance using data from an Auto-grading system)
Modeling Techniques (Predicting student performance using data from an Auto-grading system)
Marmoset Auto-grading Features for Predicting Exam Performance
Classification and Regression Results for Marmoset-Based Exam Prediction