Traditional plagiarism detection tools, which primarily rely on direct comparisons between a user’s input and existing sources, often struggle to identify more sophisticated forms of cheating, such as extensive paraphrasing or the use of external assistance, including generative AI or other individuals.
Thus, this study aims to address academic dishonesty in coding by analyzing typing patterns and examining the differences in typing dynamics when individuals code and code trace compared to when they refer to or copy responses from ChatGPT. These differences are characterized by variations in thinking time, typing speed, and the frequency of editing actions during the programming and code tracing process.
Data Collection Process:
There are four different sessions for collecting data. In each session, participants will respond to six Python coding exercises, which are designed to invoke various cognitive load levels.
You are currently on Bonafide Coding Session 1.
In this session, participants need to generate responses to each exercise independently, without any external assistance.
Evaluation Criteria:
Upon submission, participant responses will be evaluated based on several criteria: