{"title":"Virtual Finger-Point Reading Behaviors: A Case Study of Mouse Cursor Movements on a Website","authors":"Ilan Kirsh","doi":"10.1016/j.bdr.2022.100328","DOIUrl":null,"url":null,"abstract":"<div><p>Web analytics<span> has changed significantly in recent years. As part of the big data revolution, frequent low-level user actions, such as mouse movements and clicks, are often used in modern web analytics. Various studies show that when a user moves or clicks the mouse, the position of the mouse cursor is relatively close to the position of the eye gaze on the screen. Accordingly, mouse cursor positions can indicate user attention and interest in specific areas of web pages. This study focuses on mouse movement directions and speeds rather than on mouse cursor positions. A statistical analysis of mouse movements on an online learning website, which was selected for this study, sheds light on several interesting patterns. For example, most mouse movements in the examined usage data are either approximately horizontal or approximately vertical, and horizontal mouse movements are more frequent than vertical mouse movements. Besides, horizontal movements to the left are not equivalent to horizontal movements to the right, in terms of moving time and speed. As this study shows, these statistical findings are related to Pointer Assisted Reading (PAR), a reading behavior consisting of moving the mouse cursor (also known as the mouse pointer) along sentences, marking the reading position, similarly to finger-pointing when reading a book. Associating mouse movements with text reading may potentially highlight content that most users tend to skip, and therefore, might not interest the website's audience, as well as content that many readers read more than once or slowly, suggesting a lack of clarity or ambiguity. As discussed in this paper, this could be useful in locating issues in the textual content of websites and especially in online learning and educational technology applications.</span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2022-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data Research","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214579622000223","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 1
Abstract
Web analytics has changed significantly in recent years. As part of the big data revolution, frequent low-level user actions, such as mouse movements and clicks, are often used in modern web analytics. Various studies show that when a user moves or clicks the mouse, the position of the mouse cursor is relatively close to the position of the eye gaze on the screen. Accordingly, mouse cursor positions can indicate user attention and interest in specific areas of web pages. This study focuses on mouse movement directions and speeds rather than on mouse cursor positions. A statistical analysis of mouse movements on an online learning website, which was selected for this study, sheds light on several interesting patterns. For example, most mouse movements in the examined usage data are either approximately horizontal or approximately vertical, and horizontal mouse movements are more frequent than vertical mouse movements. Besides, horizontal movements to the left are not equivalent to horizontal movements to the right, in terms of moving time and speed. As this study shows, these statistical findings are related to Pointer Assisted Reading (PAR), a reading behavior consisting of moving the mouse cursor (also known as the mouse pointer) along sentences, marking the reading position, similarly to finger-pointing when reading a book. Associating mouse movements with text reading may potentially highlight content that most users tend to skip, and therefore, might not interest the website's audience, as well as content that many readers read more than once or slowly, suggesting a lack of clarity or ambiguity. As discussed in this paper, this could be useful in locating issues in the textual content of websites and especially in online learning and educational technology applications.
期刊介绍:
The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic.
The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.