Cited by 8Digital Accessibility for DisabilitiesDisability Education and EmploymentEducational Technology in Learning

Analysing Disability Descriptions and Student Suggestions as a Foundation to Overcome Barriers to Learning

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Coughlan, Tim, Iniesto, Francisco, Carr, Jessica E.·2024·Journal of Interactive Media in Education

Abstract

Artificial intelligence can support increasingly complex conversational interactions and also has the potential to interpret meanings from free text input and make recommendations based on patterns in data. There are important opportunities to apply this to real-world problems faced in access to education. In this paper, we summarise existing research and trends linking disability support and technology, then report on a survey conducted with students with disclosed disabilities (n = 138) to explore what systems might need to do to effectively understand disabled students in their own words, and provide suggestions of technologies, strategies and resources that could be relevant to overcoming barriers to learning. Through thematic analysis, five approaches that students used to talk about their disabilities are identified (medical, functional, support, experiential and administrative), and three major types of suggestions they make around what supported them and may be useful to others are also identified (external tools, university support and practices, concerns and solutions). The survey approach and the findings of the analysis provide a potential foundation for the effective design of systems that could crowdsource a knowledge base around disabilities, hold conversations to understand disabilities and barriers and make relevant recommendations to individuals as to how they could overcome these barriers.

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  1. Bada, SO and Olusegun, S. 2015. Constructivism learning theory: A paradigm for teaching and learning. Journal of Research & Method in Education, 5(6): 66-70.
  2. Coughlan et al. Journal of Interactive Media in Education DOI: 10.5334/jime.836
  3. Draffan, EA and Heumader, P. 2020. Artificial Intelligence, accessible and assistive technologies: Introduction to the special thematic session. In Computers Helping People with Special Needs: 17 th International Conference, ICCHP 2020. Springer International Publishing. 53-58. DOI: https://doi. org/10.1007/978-3-030-58796-3_7 · DOI
  4. Finkelstein, V. 1993. The commonality of disability. In Swain, J, Finkelstein, V, French, S and Oliver, M (eds.), Disabling barriers, enabling environments. Open University Press; Sage Publications, Inc. pp. 9-16.
  5. Gierach, J. (ed.) 2009. Assessing students' needs for assistive technology (ASNAT). Madison, WI: Wisconsin Assistive Technology Initiative (WATI) and the Wisconsin Department of Public Instruction (DPI).
  6. Heumader, P, Murillo-Morales, T and Miesenberger, K. 2022. AI based recommendation and assessment of AT for people with cognitive disabilities. The Journal on Technology and Persons with Disabilities, 10.
  7. Coughlan et al. Journal of Interactive Media in Education DOI: 10.5334/jime.836
  8. Murphy, R. 2021. Higher education students with disabilities' perceptions of emergency remote learning- exploring the benefits and barriers of e-learning. The AHEAD Journal, 12.
  9. Sapsford, R and Jupp, V. 2006. Data collection and analysis. Sage Publications Ltd. DOI: https://doi. org/10.4135/9781849208802 · DOI
  10. Slaughter, R and Waller, A. 2022. MSc Educational Assistive Technology: Training a new professional group. Proceedings of Communication Matters International AAC Conference 2022. Available from: https://discovery.dundee.ac.uk/ws/portalfiles/portal/84691618/CM2022BookofAbstracts.pdf. DOI: https://doi.org/10.3233/SHTI230661 · DOI

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Authors
Coughlan, T., Iniesto, F., & Carr, J. E.
Affiliations
  • Universidad Nacional de Educación a Distancia (ES)
  • The Open University (GB)
Corresponding author
  • Coughlan, Tim
  • Iniesto, Francisco
  • Carr, Jessica E.
Venue
Journal of Interactive Media in Education
Published
2024
Volume
2024
Issue
1
ISSN
1365-893X (ISSN-L)
OpenAlex
W4391852727
Cited by
8
References
31
Citations by year
2026: 2 · 2025: 2 · 2024: 4
FWCI
16.73
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Interactive Media in Education (publishedVersion, cc-by)
  • Open Research Online (The Open University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UNED repository (submittedVersion, other-oa)
Topics
  • Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
  • Social Sciences › Safety Research › Disability Education and Employment
  • Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
Keywords
disability, students with disabilities, barriers to learning, assistive technology, artificial intelligence, chatbots, student perceptions, university support, accessibility in higher education, knowledge base, recommender systems

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