Automated Assessment of Student Self-explanation During Source Code Comprehension
This paper presents a novel method to automatically assess self-explanations generated by students during code comprehension activities. The self-explanations are produced in the context of an online learning environment that asks students to freely explain Java code examples line-by-line. We explor...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2022-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/130540 |
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| _version_ | 1849763277552222208 |
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| author | Jeevan Chapagain Lasang Tamang Rabin Banjade Priti Oli Vasile Rus |
| author_facet | Jeevan Chapagain Lasang Tamang Rabin Banjade Priti Oli Vasile Rus |
| author_sort | Jeevan Chapagain |
| collection | DOAJ |
| description | This paper presents a novel method to automatically assess self-explanations generated by students during code comprehension activities. The self-explanations are produced in the context of an online learning environment that asks students to freely explain Java code examples line-by-line. We explored a number of models consisting of textual features in conjunction with machine learning algorithms such as Support Vector Regression (SVR), Decision Trees (DT), and Random Forests (RF). Support Vector Regression (SVR) performed best having a correlation score with human judgments of 0.7088. The best model used a combination of features such as semantic measures obtained using a Sentence BERT pre-trained model and from previously developed semantic algorithms used in a state-of-the-art intelligent tutoring system. |
| format | Article |
| id | doaj-art-dfb6bd2fb60049e482e78d32cc91db92 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-dfb6bd2fb60049e482e78d32cc91db922025-08-20T03:05:26ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622022-05-013510.32473/flairs.v35i.13054066739Automated Assessment of Student Self-explanation During Source Code ComprehensionJeevan Chapagain0Lasang TamangRabin BanjadePriti OliVasile RusUniversity of MemphisThis paper presents a novel method to automatically assess self-explanations generated by students during code comprehension activities. The self-explanations are produced in the context of an online learning environment that asks students to freely explain Java code examples line-by-line. We explored a number of models consisting of textual features in conjunction with machine learning algorithms such as Support Vector Regression (SVR), Decision Trees (DT), and Random Forests (RF). Support Vector Regression (SVR) performed best having a correlation score with human judgments of 0.7088. The best model used a combination of features such as semantic measures obtained using a Sentence BERT pre-trained model and from previously developed semantic algorithms used in a state-of-the-art intelligent tutoring system.https://journals.flvc.org/FLAIRS/article/view/130540self-explanationsource code comprehensionsemantic similarity |
| spellingShingle | Jeevan Chapagain Lasang Tamang Rabin Banjade Priti Oli Vasile Rus Automated Assessment of Student Self-explanation During Source Code Comprehension Proceedings of the International Florida Artificial Intelligence Research Society Conference self-explanation source code comprehension semantic similarity |
| title | Automated Assessment of Student Self-explanation During Source Code Comprehension |
| title_full | Automated Assessment of Student Self-explanation During Source Code Comprehension |
| title_fullStr | Automated Assessment of Student Self-explanation During Source Code Comprehension |
| title_full_unstemmed | Automated Assessment of Student Self-explanation During Source Code Comprehension |
| title_short | Automated Assessment of Student Self-explanation During Source Code Comprehension |
| title_sort | automated assessment of student self explanation during source code comprehension |
| topic | self-explanation source code comprehension semantic similarity |
| url | https://journals.flvc.org/FLAIRS/article/view/130540 |
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