Program Code Navigation Model for Individuals based on LSTM with Co-clustering
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Ming Sun, Minoru NakayamaAuthors Info & Claims
Ming Sun, Minoru NakayamaAuthors Info & ClaimsArticle No.: 88, Pages 1 - 6
Abstract
To improve program learning tools with eye tracking technology, it is crucial to understand fixation points and individual information, providing personalized navigation cues for different levels of expertise programmers. Meanwhile, long short-term memory (LSTM) and clustering techniques revealed important characteristics for eye movement data regarding comprehension performance. This paper is about a spatial analysis by co-clustering with the gaze location among different levels of expertise programmers. Then it predicts the next fixation point based on the human eye movement data by LSTM. Finally, combining the individual background information and the eye movement information, it generates a new indicator, named ‘code comprehension index’, to indicate the current code understanding level on real-time gaze information for individuals, which can be applied to improve the program navigation tools’ effectiveness and efficiency for different levels of expertise programmers.
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Published: 30 May 2023
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ETRA '23: 2023 Symposium on Eye Tracking Research and Applications
May 30 - June 2, 2023
Tubingen, Germany
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