{"title":"Advancing Dynamic-Time Warp Techniques for Correcting Eye Tracking Data in Reading Source Code","authors":"Naser Al Madi","doi":"10.16910/jemr.17.1.4","DOIUrl":null,"url":null,"abstract":"Background: Automated eye tracking data correction algorithms such as Dynamic-Time Warp always made a trade-off between the ability to handle regressions (jumps back) and distortions (fixation drift). At the same time, eye movement in code reading is characterized by non-linearity and regressions. \nObjective: In this paper, we present a family of hybrid algorithms that aim to handles both regressions and distortions with high accuracy. \nMethod: Through simulations with synthetic data we replicate known eye movement phenomena to assess our algorithms against Warp algorithm as a baseline. Furthermore, we utilize three real datasets to evaluate the algorithms in correcting data from reading source code and see if the proposed algorithms generalize to correcting data from reading natural language text. \nResults: Our results demonstrate that most proposed algorithms match or outperform baseline warp in correcting both synthetic and real data. Also, we show the prevalence of regressions in reading source code. \nConclusion: Our results highlight our hybrid algorithms as an improvement to Dynamic-Time Warp in handling regressions with higher accuracy and better runtime.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":"9 1","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.16910/jemr.17.1.4","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Automated eye tracking data correction algorithms such as Dynamic-Time Warp always made a trade-off between the ability to handle regressions (jumps back) and distortions (fixation drift). At the same time, eye movement in code reading is characterized by non-linearity and regressions.
Objective: In this paper, we present a family of hybrid algorithms that aim to handles both regressions and distortions with high accuracy.
Method: Through simulations with synthetic data we replicate known eye movement phenomena to assess our algorithms against Warp algorithm as a baseline. Furthermore, we utilize three real datasets to evaluate the algorithms in correcting data from reading source code and see if the proposed algorithms generalize to correcting data from reading natural language text.
Results: Our results demonstrate that most proposed algorithms match or outperform baseline warp in correcting both synthetic and real data. Also, we show the prevalence of regressions in reading source code.
Conclusion: Our results highlight our hybrid algorithms as an improvement to Dynamic-Time Warp in handling regressions with higher accuracy and better runtime.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.