Features (Predicting student performance using data from an Auto-grading system)
In their models they have used 4 distinctive features:
-
Passing rate for each task - passing rate for the best submission. The total number of tasks is 28 and first 16 assignments are due before the midterm exam and the rest is due before the final exam.
-
Testcase Outcome - testcase outcomes for the best submission.
-
Submission Time Interval - time interval between submission and the deadline.
-
Number of Submissions
0
1
Tags
Data Science
Related
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)
Modeling Techniques (Predicting student performance using data from an Auto-grading system)
Features (Predicting student performance using data from an Auto-grading system)
Experiments and Evaluation (Predicting student performance using data from an Auto-grading system)
Reference for (Predicting student performance using data from an Auto-grading system)