An Approach to Detect Abnormal Submissions for CodeWorkout Dataset
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910541156450304 |
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| author | Hicks, Alex Shi, Yang Lekshmi-Narayanan, Arun-Balajiee Yan, Wei Marwan, Samiha |
| author_facet | Hicks, Alex Shi, Yang Lekshmi-Narayanan, Arun-Balajiee Yan, Wei Marwan, Samiha |
| contents | Students interactions while solving problems in learning environments (i.e. log data) are often used to support students learning. For example, researchers use log data to develop systems that can provide students with personalized problem recommendations based on their knowledge level. However, anomalies in the students log data, such as cheating to solve programming problems, could introduce a hidden bias in the log data. As a result, these systems may provide inaccurate problem recommendations, and therefore, defeat their purpose. Classical cheating detection methods, such as MOSS, can be used to detect code plagiarism. However, these methods cannot detect other abnormal events such as a student gaming a system with multiple attempts of similar solutions to a particular programming problem. This paper presents a preliminary study to analyze log data with anomalies. The goal of our work is to overcome the abnormal instances when modeling personalizable recommendations in programming learning environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17475 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Approach to Detect Abnormal Submissions for CodeWorkout Dataset Hicks, Alex Shi, Yang Lekshmi-Narayanan, Arun-Balajiee Yan, Wei Marwan, Samiha Computers and Society Artificial Intelligence Software Engineering Students interactions while solving problems in learning environments (i.e. log data) are often used to support students learning. For example, researchers use log data to develop systems that can provide students with personalized problem recommendations based on their knowledge level. However, anomalies in the students log data, such as cheating to solve programming problems, could introduce a hidden bias in the log data. As a result, these systems may provide inaccurate problem recommendations, and therefore, defeat their purpose. Classical cheating detection methods, such as MOSS, can be used to detect code plagiarism. However, these methods cannot detect other abnormal events such as a student gaming a system with multiple attempts of similar solutions to a particular programming problem. This paper presents a preliminary study to analyze log data with anomalies. The goal of our work is to overcome the abnormal instances when modeling personalizable recommendations in programming learning environments. |
| title | An Approach to Detect Abnormal Submissions for CodeWorkout Dataset |
| topic | Computers and Society Artificial Intelligence Software Engineering |
| url | https://arxiv.org/abs/2407.17475 |