@inproceedings{oai:naist.repo.nii.ac.jp:02000086,
 author = {Fukushima, Kazuki and Ishio, Takashi and 石尾, 隆 and Shimari, Kazumasa and 嶋利, 一真 and 松本, 健一},
 book = {2022 IEEE 16th International Workshop on Software Clones (IWSC)},
 month = {Dec},
 note = {In an assignment-based introductory programming course, a teacher is required to grade assignments. However, this is generally a boring, labor-intensive task. Assisted grading is an approach to reduce the effort for a manual grading process by automatically classifying student submissions into groups so that a teacher can manually check only representative submissions. In this study, we try an assisted grading using source code similarity. Given a number of manually graded submissions, our method calculates source code similarity between a new submission with them. If there exists a similar submission, we automatically give the same grade as the submission. Otherwise, we ask the teacher to manually grade the submission and use the grade for future submissions. As a preliminary analysis, we have implemented the idea as a simple algorithm and evaluated the reduction in effort using student submissions in a programming course conducted in the department of the authors.},
 publisher = {IEEE},
 title = {A Similarity-based Assisted Grading for Introductory Programming Course},
 year = {2022},
 yomi = {イシオ, タカシ and シマリ, カズマサ and マツモト, ケンイチ}
}