Kendall Walter

Research student profile

Research degree: PhD programme
Start date: 2023
Open Research and Contributor ID (ORCID)0009-0003-9806-0043

Research summary

Project title: Development of an electronic software package that delivers an evidence-based assessment of reading performance using eye movement data.

The aim of Kendall’s research is to develop a software package that utilizes eye movement data to accurately assess reading performance in children.

The ability to read is a vital skill in modern society. Therefore, it is critical to conduct scientific research to better understand the nature and development of the psychological processes underlying reading. The examination of eye movements is a well-established method to provide an excellent and precise index of the moment-to-moment cognitive and visual processes that occur during reading. Further, the study of eye movements can provide insight into children’s literacy development as eye movement behaviours provide an accurate reflection of the developmental changes that occur as our reading improves with tuition and time.

Although there is a clear link between eye movement behaviours and the psychological processes that underlie reading, little research has been done to investigate the study of eye movements as a potential assessment of reading performance. Traditional, ‘pen and paper’ assessments of reading performance are widely used. These assessments of reading are cheap and easy to administer, but it is unclear to what extent these assessments truly assess components of reading that they intend to measure. Utilizing eye movement data to assess reading performance may be a way forward. If we are successful, we will develop a system that can accurately assess reading performance based on eye movement data metrics.

In collaboration with Thomson Software Solutions, a company that develops software for vision assessment, Kendall and her colleagues will develop, test, and validate a system that is capable of assessing reading performance in children based on eye movement data. This software will offer potential to provide individualized profiles of reading performance. As such, this project will contribute to the understanding of children’s literacy development and reading performance, which could inform teaching practices and improve remedial reading interventions.

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If you are interested in engaging with this researcher please get in touch with them:

Research Supervisor: Simon P Liversedge
Student: Kendall Walter

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