An Approach to Detect Abnormal Submissions for CodeWorkout Dataset

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, ano...

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Bibliographic Details
Published inarXiv.org
Main Authors Hicks, Alex, Yang, Shi, Lekshmi-Narayanan, Arun-Balajiee, Yan, Wei, Samiha Marwan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.06.2024
Subjects
Anomalies
Learning
Problem solving
Programming
Students
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Summary: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.
ISSN:2331-8422