--- http_interactions: - request: method: get uri: https://api.crossref.org/works/10.16910/jemr.9.1.2/transform/application/vnd.crossref.unixsd+xml body: encoding: US-ASCII string: '' headers: User-Agent: - Mozilla/5.0 (compatible; Maremma/4.9.4; mailto:info@datacite.org) Accept: - text/xml;charset=utf-8 Accept-Encoding: - gzip,deflate response: status: code: 200 message: OK headers: Link: - ; rel="canonical", ; version="vor"; type="application/pdf"; rel="item", ; version="vor"; rel="item" Access-Control-Allow-Origin: - "*" Access-Control-Allow-Headers: - X-Requested-With Content-Length: - '5603' Server: - http-kit Date: - Wed, 09 Jun 2021 12:57:39 GMT X-Rate-Limit-Limit: - '50' X-Rate-Limit-Interval: - 1s Permissions-Policy: - interest-cohort=() Connection: - close body: encoding: ASCII-8BIT string: "\n\r\n \r\n \ \r\n none\r\n \r\n \r\n \ \r\n 10.16910/jemr.9.1.2\r\n \ University of Bern\r\n \ University of Bern\r\n \ 22676\r\n \ 124518013\r\n \ 421317\r\n \ 1614427024\r\n \ 10.16910\r\n \ 2021-02-27T11:57:27Z\r\n \ 2021-02-27T11:57:27Z\r\n \ 3\r\n \ 10.3390/s18061822\r\n \ \r\n \r\n \ \r\n \r\n Journal of Eye Movement Research\r\n JEMR\r\n \ 1995-8692\r\n \r\n \ \r\n \r\n \ 02\r\n 10\r\n \ 2016\r\n \r\n \ \r\n 9\r\n \ \r\n 1\r\n \ \r\n \r\n \r\n \ Eye tracking scanpath analysis techniques on web pages: A survey, evaluation and comparison\r\n \r\n \ \r\n \r\n Sukru\r\n \ Eraslan\r\n \r\n \ \r\n \ Yeliz\r\n Yesilada\r\n \ \r\n \r\n Simon\r\n \ Harper\r\n \r\n \ \r\n \r\n Eye tracking has commonly been used to investigate how users interact with web pages, with the goal of improving their usability. This article comprehensively revisits the techniques that could be applicable to eye tracking data for analysing user scanpaths on web pages. It also uses a third-party eye tracking study to compare these techniques. This allows researchers to recognise existing techniques for their goals, understand how they work and know their strengths and limitations so that they can make an efficient choice for their studies. These techniques can mainly be used for calculating similarities/dissimilarities between scanpaths, computing transition probabilities between web page elements, detecting patterns in scanpaths and identifying common scanpaths. The scanpath analysis techniques are classified into four groups by their goals so that researchers can directly focus on the appropriate techniques for a sequential analysis of user scanpaths on web pages. This article also suggests dealing with the limitations of these techniques by pre-processing eye tracking data, considering cognitive processing and addressing their reductionist approach.\r\n \ \r\n \r\n \ 12\r\n 30\r\n \ 2015\r\n \r\n \ \r\n 10.16910/jemr.9.1.2\r\n \ https://bop.unibe.ch/JEMR/article/view/2430\r\n \ \r\n \r\n https://bop.unibe.ch/JEMR/article/download/2430/3624\r\n \ \r\n \r\n \r\n \r\n https://bop.unibe.ch/JEMR/article/download/2430/3624\r\n \ \r\n \r\n \r\n \ \r\n \r\n \r\n \ \r\n \r\n \r\n \r\n" http_version: recorded_at: Wed, 09 Jun 2021 12:57:39 GMT recorded_with: VCR 3.0.3