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A Similarity-based Assisted Grading for Introductory Programming Course
http://hdl.handle.net/10061/0002000086
http://hdl.handle.net/10061/0002000086deda524e-4d31-4ff3-a94e-00806a7149f6
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
fulltext (0.076MB)
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| アイテムタイプ | 会議発表論文 / Conference Paper(1) | |||||||
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| 公開日 | 2023-12-20 | |||||||
| タイトル | ||||||||
| タイトル | A Similarity-based Assisted Grading for Introductory Programming Course | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 資源タイプ | ||||||||
| 資源タイプ | conference paper | |||||||
| 著者 |
Fukushima, Kazuki
× Fukushima, Kazuki
× 石尾, 隆× 嶋利, 一真× 松本, 健一 |
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | 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. | |||||||
| 書誌情報 |
en : 2022 IEEE 16th International Workshop on Software Clones (IWSC) 発行日 2022-12-14 |
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| 会議情報 | ||||||||
| 会議名 | 2022 IEEE 16th International Workshop on Software Clones (IWSC) | |||||||
| 開始年 | 2022 | |||||||
| 開始月 | 10 | |||||||
| 開始日 | 02 | |||||||
| 終了年 | 2022 | |||||||
| 終了月 | 10 | |||||||
| 終了日 | 02 | |||||||
| 開催地 | Limassol | |||||||
| 開催国 | CYP | |||||||
| 出版者 | ||||||||
| 出版者 | IEEE | |||||||
| ISSN | ||||||||
| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 2572-6587 | |||||||
| 出版者版DOI | ||||||||
| 関連タイプ | isVersionOf | |||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.1109/IWSC55060.2022.00012 | |||||||
| 出版者版URI | ||||||||
| 関連タイプ | isVersionOf | |||||||
| 識別子タイプ | URI | |||||||
| 関連識別子 | https://ieeexplore.ieee.org/document/9978183 | |||||||
| 権利 | ||||||||
| 権利情報 | $00A9 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 出版社許諾条件により、本文は2024年12月14日以降に公開 | |||||||
| 著者版フラグ | ||||||||
| 出版タイプ | AM | |||||||
