Mobile search made easier: An ability-based mobile search prototype for people with dyslexia
Pages 45 - 55
Abstract
Although 1 person in 14 has dyslexia, most search interfaces are designed based on a ‘one-size-fits-all’ approach, creating inequity for neurodiverse searchers. This is also the case for mobile search, which accounts for most Google searches. While existing research has found search typically presents greater challenges for people with dyslexia, no prior work has examined how best to support them when searching on mobile devices. Rather than focus on addressing their search difficulties, we adopted an ability-based design approach. This involved designing a prototype, based on modifications to Google's mobile SERPs, aimed at enhancing their abilities – identified through interviews and observations with mobile searchers with dyslexia. A user evaluation found several of the modifications were useful; they supported searchers with dyslexia in making relevance judgements and boosted their resilience and self-efficacy. This research provides valuable insight into how to better support mobile searchers with dyslexia that can inform IIR research and design. It also demonstrates the potential of ability-based design approaches in supporting neurodiverse searchers.
References
[1]
Mohammad Aliannejadi, Morgan Harvey, Luca Costa, Matthew Pointon, and Fabio Crestani. 2019. Understanding mobile search task relevance and user behaviour in context. In CHIIR 2019 - Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, Association for Computing Machinery, Inc, 143–151. https://doi.org/10.1145/3295750.3298923
[2]
Gerd Berget and Andrew MacFarlane. 2019. Experimental methods in IIR: The tension between rigour and ethics in studies involving users with dyslexia. In CHIIR 2019 - Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, Association for Computing Machinery, Inc, 93–101. https://doi.org/10.1145/3295750.3298939
[3]
Gerd Berget and Andrew MacFarlane. 2020. What Is Known About the Impact of Impairments on Information Seeking and Searching? J. Assoc. Inf. Sci. Technol. 71, 5, 596–611. https://doi.org/10.1002/asi.24256
[4]
Gerd Berget and Frode Eika Sandnes. 2016. Do autocomplete functions reduce the impact of dyslexia on information-searching behavior? The case of Google. J. Assoc. Inf. Sci. Technol. 67, 10, 2320–2328. https://doi.org/10.1002/asi.23572
[5]
Gerd Berget and Frode Eika Sandnes. 2019. Why textual search interfaces fail: a study of cognitive skills needed to construct successful queries. Information Research 24, 1.
[6]
Ann Blandford, Dominic Furniss, and Stephann Makri. 2016. Qualitative HCI Research: Going Behind the Scenes. In John Carroll (Ed.) Synthesis Lectures on Human-Centered Informatics. Morgan & Claypool, 61–78.
[7]
Pia Borlund. 2003. The IIR evaluation model: a framework for evaluation of interactive information retrieval systems. Information Research 8, 3 (2003).
[8]
Pia Borlund. 2016. A study of the use of simulated work task situations in interactive information retrieval evaluations: A meta-evaluation. Journal of Documentation 72, 3, 394–413. https://doi.org/10.1108/JD-06-2015-0068
[9]
Virginia Braun and Victoria Clarke. 2022. Thematic Analysis: A Practical Guide. Sage, London.
[10]
Lynne Cole, Andrew Macfarlane, and George Buchanan. 2016. Does dyslexia present barriers to information literacy in an online environment? A pilot study. Library and Information Research 40.
[11]
Lynne Cole, Andrew MacFarlane, and Stephann Makri. 2020. More than Words: The impact of memory on how undergraduates with dyslexia interact with information. In CHIIR 2020 - Proceedings of the 2020 Conference on Human Information Interaction and Retrieval, Association for Computing Machinery, Inc, 353–357. https://doi.org/10.1145/3343413.3378005
[12]
Jonathan David Day. 2018. Exploring the Nature of Cognitive Resilience Strategies. Doctoral thesis. City, University of London.
[13]
Adam Fourney, Meredith Ringel Morris, Abdullah Ali, and Laura Vonessen. 2018. Assessing the readability of web search results for searchers with dyslexia. In 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, Association for Computing Machinery, Inc, 1069–1072. https://doi.org/10.1145/3209978.3210072
[14]
Matt Jones, George Buchanan, and Harold Thimbleby. 2002. Sorting Out Searching on Small Screen Devices. In: Paternò, F. (eds) Human-Computer Interaction with Mobile Devices. Mobile HCI 2002. Lecture Notes in Computer Science, vol 2411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45756-9_8
[15]
David Kirsh. 2000. A Few Thoughts on Cognitive Overload. Intellectica Revue de l Association pour la Recherche Cognitive 1, 30, 19–51.
[16]
Birgit Kvikne and Gerd Berget. 2021. In search of trustworthy information: a qualitative study of the search behavior of people with dyslexia in Norway. Univers. Access. Inf. Soc. 20, 1. https://doi.org/10.1007/s10209-019-00703-9
[17]
Qisheng Li, Meredith Ringel Morris, Adam Fourney, Kevin Larson, and Katharina Reinecke. 2019. The impact of web browser reader views on reading speed and user experience. In Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery. https://doi.org/10.1145/3290605.3300754
[18]
Inger Marie Lid. 2013. Developing the theoretical content in Universal Design. Scandinavian Journal of Disability Research 15, 3, 203–215. https://doi.org/10.1080/15017419.2012.724445
[19]
Andrew MacFarlane, Asaad Albrair, Chloe Marshall, and George Buchanan. 2012. Phonological working memory impacts on information searching: An investigation of dyslexia. Proceedings of the 4th Information Interaction in Context Symposium, 27–34.
[20]
Stephann Makri, Dana McKay, George Buchanan, Shanton Chang, Dirk Lewandowski, Andy MacFarlane, Lynne Cole, Sanne Vrijenhoek, and Andrés Ferraro. 2021. Search a Great Leveler? Ensuring More Equitable Information Acquisition. Proceedings of the Association for Information Science and Technology 58, 1, 613–618. https://doi.org/10.1002/pra2.511
[21]
Kirsti Malterud, Volkert Dirk Siersma, and Ann Dorrit Guassora. 2016. Sample Size in Qualitative Interview Studies: Guided by Information Power. Qual. Health. Res. 26, 13, 1753–1760. https://doi.org/10.1177/1049732315617444
[22]
Jiaxin Mao, Cheng Luo, Min Zhang, and Shaoping Ma. 2018. Constructing click models for mobile search. In 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, Association for Computing Machinery, Inc, 775–784. https://doi.org/10.1145/3209978.3210060
[23]
Gary Marchionini and Ryen White. 2007. Find what you need, understand what you find. Int. J. Hum. Comput. Interact. 23, 3, 205–237. https://doi.org/10.1080/10447310701702352
[24]
Meredith Ringel Morris, Adam Fourney, Abdullah Ali, and Laura Vonessen. 2018. Understanding the needs of searchers with dyslexia. In Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery. https://doi.org/10.1145/3173574.3173609
[25]
Srishti Palani, Adam Fourney, Shane Williams, Kevin Larson, Irina Spiridonova, and Meredith Ringel Morris. 2020. An Eye Tracking Study of Web Search by People with and Without Dyslexia. In SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, Inc, 729–738. https://doi.org/10.1145/3397271.3401103
[26]
Robin L. Peterson and Bruce F. Pennington. 2015. Developmental dyslexia. Annu. Rev. Clin. Psychol. 11, (March 2015), 283–307. https://doi.org/10.1146/annurev-clinpsy-032814-112842
[27]
Yvonne Rogers. 2012. HCI theory: Classical, Modern, and Contemporary. In John Carroll (Ed.) Synthesis Lectures on Human-Centered Informatics. Morgan & Claypool, 1–8.
[28]
Reijo Savolainen. 2018. Berrypicking and information foraging: Comparison of two theoretical frameworks for studying exploratory search. J. Inf. Sci. 44, 5 (October 2018), 580–593. https://doi.org/10.1177/0165551517713168
[29]
Nick Statt. 2015. More than half of all Google searches now happen on mobile devices. The Verge. Retrieved September 4, 2022 from https://www.theverge.com/2015/10/8/9480779/google-search-mobile-vs-desktop-2015
[30]
The British Dyslexia Association. 20123. Adult Checklist. Retrieved July 27, 2022 from https://cdn.bdadyslexia.org.uk/uploads/documents/Dyslexia/Adult-Checklist-1.pdf
[31]
The Competition and Markets Authority. 2020. Online platforms and digital advertising. Retrieved July 6, 2022 from https://assets.publishing.service.gov.uk/media/5fa557668fa8f5788db46efc/Final_report_Digital_ALT_TEXT.pdf
[32]
Richard K. Wagner, Fotena A. Zirps, Ashley A. Edwards, Sarah G. Wood, Rachel E. Joyner, Betsy J. Becker, Guangyun Liu, and Bethany Beal. 2020. The Prevalence of Dyslexia: A New Approach to Its Estimation. J. Learn. Disabil. 53, 5, 354–365. https://doi.org/10.1177/0022219420920377
[33]
Daniel Wessel, Ann Kathrin Kennecke, and Moreen Heine. 2021. WCAG and Dyslexia Improving the Search Function of Websites for Users with Dyslexia (Without Making It Worse for Everyone Else). In ACM International Conference Proceeding Series, Association for Computing Machinery, 168–179. https://doi.org/10.1145/3473856.3473867
[34]
Jacob O. Wobbrock, Krzysztof Z. Gajos, Shaun K. Kane, and Gregg C. Vanderheiden. 2018. Ability-based design. Commun. ACM 61, 6, 62–71. https://doi.org/10.1145/3148051
[35]
Jacob O. Wobbrock, Shaun K. Kane, Krzysztof Z. Gajos, Susumu Harada, and Jon Froehlich. 2011. Ability-based design: Concept, principles and examples. ACM Trans. Access. Comput. 3, 3. https://doi.org/10.1145/1952383.1952384
[36]
Yukun Zheng, Jiaxin Mao, Yiqun Liu, Mark Sanderson, Min Zhang, and Shaoping Ma. 2020. Investigating examination behavior in mobile search. In WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, 771-779. https://doi.org/10.1145/3336191.3371797
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- Mobile search made easier: An ability-based mobile search prototype for people with dyslexia
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Published: 10 March 2024
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CHIIR '24: 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval
March 10 - 14, 2024
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