Predicting Students' Software Engineering Class Performance with Machine Learning and Pre-Class GitHub Metrics-Reference-Cited by-同舟云学术

Predicting Students' Software Engineering Class Performance with Machine Learning and Pre-Class GitHub Metrics

Published:2023-10-18 Issue: Volume:97 Page:1-9
ISSN:
Container-title:2023 IEEE Frontiers in Education Conference (FIE)
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Author:

Cui Jialin¹,Zhou Fangtong¹,Zhang Runqiu²,Li Ruochi¹,Liu Chengyuan¹,Gehringer Ed¹

Affiliation:

1. North Carolina State University,Department of Computer Science,Raleigh,USA

2. University of Virginia,Department of Statistics,Charlottesville,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10342661/10342889/10343357.pdf?arnumber=10343357

Reference33 articles.

1. Psychological testing in computing staff selection

2. Using student performance predictions in a computer science curriculum

3. The prediction of students' academic performance using classification data mining techniques

4. Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm

5. Correlates of success in java programming: Predicting the performance of students in a java course from the results of lower level courses;Galadanci;Journal of Theoretical and Applied Information Technology,2019

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