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arXiv 2024年

作者： Hicks, Alex Shi, Yang Arun-Balajiee Yan, Wei Marwan, Samiha Dept of Computer Science Virginia Tech BlacksburgVA United States Dept of Computer Science Utah State University LoganUT United States Lekshmi-Narayanan Intelligent Systems Program University of Pittsburgh PittsburghPA United States School of Informatics Computing and Cyber Systems North Arizona University FlagstaffAZ United States Dept. of Computer Science University of Virginia CharlottesvilleVA United States

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. © 2024, CC BY-NC-SA.

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