Exploring the Effectiveness of Adaptation Based on Dyslexia Type and Reading Skill Level to Support Learners with Dyslexia

E-learning has become a popular tool for helping people with dyslexia to improve their reading, as it provides interactivity anywhere and at any time. However, most traditional e-learning systems are designed for a generic learner, regardless of each individual’s differences and needs. In this context, a successful e-learning system needs to consider the learner’s individual characteristics – in particular, their dyslexia type and reading skill level. This can lead to a more appropriate learning experience and interaction. There is, however, a need to understand the value of this adaptation, particularly on the learning gain. This study contributes to research by bridging this under-investigated gap by evaluating the learning effectiveness of adapting material based on the learner’s dyslexia type and reading skill. A controlled, between-subjects experiment with 47 subjects is described and the results presented and analysed. The findings indicate that adaptation based on the combination of dyslexia type and reading skill level results in significantly better short-term and long-term learning gains and greater learner satisfaction than non-adapted material. There is also evidence that this benefit transfers to learners’ reading performance on unseen material.

Content

Author and article information

Contributors

Weam Gaoud Alghabban

Robert Hendley

Conference

Publication date: July 2022

Publication date (Print): July 2022

Pages: 1-10

Affiliations

[0001]Computer Science Department

University of Tabuk

Tabuk

Saudi Arabia

[0002]School of Computer Science

University of Birmingham

Edgbaston, Birmingham, B15 2TT

United Kingdom

Article

DOI: 10.14236/ewic/HCI2022.11

SO-VID: 10a6bf00-6a39-42e6-985b-f572cfe43508

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 35th International BCS Human-Computer Interaction Conference

Conference acronym: HCI2022

Conference number: 35

Conference location: Keele, Staffordshire

Conference date: July 11th to 13th, 2022

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Towards a Human-Centred Digital Society

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2022.11

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

Abdul Hamid, S. S., Admodisastro, N., Kamaruddin, A., Manshor, N., and Ghani, A. A. A. (2017). Informing design of an adaptive learning model for student with dyslexia: A preliminary study. In Proceedings of the 3^rdInternational Conference on Human-Computer Interaction and User Experience in Indonesia, pages 67–75. Association for Computing Machinery.
Al-Wabil, A., Zaphiris, P., and Wilson, S. (2006). Web design for dyslexics: Accessibility of Arabic content. In Miesenberger, K., Klaus, J., Zagler, W. L., and Karshmer, A. I., editors, Computers Helping People with Special Needs, pages 817–822, Berlin, Heidelberg. Springer Berlin Heidelberg.
Alghabban, W. G. and Hendley, R. (2020a). Adapting e-learning to dyslexia type: An experimental study to evaluate learning gain and perceived usability. In Stephanidis, C., Harris, D., Li, W.C., Schmorrow, D. D., Fidopiastis, C. M., Zaphiris, P., Ioannou, A., Fang, X., Sottilare, R. A., and Schwarz, J., editors, HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games, pages 519–537, Cham. Springer International Publishing.
Alghabban, W. G. and Hendley, R. (2020b). The impact of adaptation based on students’ dyslexia type: An empirical evaluation of students’ satisfaction. In Adjunct Publication of the 28^thACM Conference on User Modeling, Adaptation and Personalization, UMAP ’20 Adjunct, pages 41–46, New York, NY, USA. Association for Computing Machinery.
Alghabban, W. G., Salama, R. M., and Altalhi, A. H. (2017). Mobile cloud computing: An effective multimodal interface tool for students with dyslexia. Computers in Human Behavior, 75:160–166.
Alsobhi, A. and Alyoubi, K. (2020). Learning styles and dyslexia types -understanding their relationship and its benefits in adaptive e-learning systems. International Journal of Interactive Mobile Technologies (iJIM), 14(15):25–43.
Alsobhi, A. Y. and Alyoubi, K. H. (2019). Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type. Data Technologies and Applications, 53(2):189–200.
Alsobhi, A. Y., Khan, N., and Rahanu, H. (2014). Toward linking dyslexia types and symptoms to the available assistive technologies. In 2014 IEEE 14^thInternational Conference on Advanced Learning Technologies, pages 597–598. IEEE.
Alsobhi, A. Y., Khan, N., and Rahanu, H. (2015). DAEL framework: A new adaptive e-learning framework for students with dyslexia. Procedia Computer Science, 51:1947–1956.
Annett, M. (1996). Laterality and types of dyslexia. Neuroscience & Biobehavioral Reviews, 20(4):631–636.
Beland, R. and Mimouni, Z. (2001). Deep dyslexia in the two languages of an arabic/french bilingual patient. Cognition, 82(2):77–126.
Benmarrakchi, F. E., Kafi, J. E., and Elhore, A. (2017a). Communication technology for users with specific learning disabilities. Procedia Computer Science, 110:258–265.
Benmarrakchi, F. E., Kafi, J. E., and Elhore, A. (2017b). User modeling approach for dyslexic students in virtual learning environments. International Journal of Cloud Applications and Computing (IJCAC), 7(2):1–9.
Benmarrakchi, F. E., Kafi, J. E., Elhore, A., and Haie, S. (2017c). Exploring the use of the ICT in supporting dyslexic students’ preferred learning styles: A preliminary evaluation. Education and Information Technologies, 22:2939–2957.
Bonacina, S., Cancer, A., Lanzi, P. L., Lorusso, M. L., and Antonietti, A. (2015). Improving reading skills in students with dyslexia: the efficacy of a sublexical training with rhythmic background. Frontiers in Psychology, 6:1–8.
Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1):87–110.
Brusilovsky, P. and Millán, E. (2007). User Models for Adaptive Hypermedia and Adaptive Educational Systems, pages 3–53. Springer Berlin Heidelberg, Berlin, Heidelberg.
Bukhari, Y. A., AlOud, A. S., Abughanem, T. A., AlMayah, S. A., Al-Shabib, M. S., and Al-Jaber, S. A. (2016). Alaikhtibarat Altashkhisiat Lidhuyi Sueubat Altaalum Fi Madatay Allughat Alarabia Wa Alriyadiat Fi Almarhalat Alebtidaeiia [Diagnostic Tests for People with Learning Difficulties in the Subjects of Arabic Language and Mathematics at the Primary Stage]. General Administration for Special Education, The General Administration for Evaluation and Quality of Education, Ministry of Education, Saudi Arabia.
Burke, M. D., Crowder, W., Hagan-Burke, S., and Zou, Y. (2009). A comparison of two path models for predicting reading fluency. Remedial and Special Education, 30(2):84–95.
Daly III, E. J., Martens, B. K., Kilmer, A., and Massie, D. R. (1996). The effects of instructional match and content overlap on generalized reading performance. Journal of Applied Behavior Analysis, 29(4):507–518.
Dolgin, A. B. (1975). How to match reading materials to student reading levels. The Social Studies, 66(6):249–252.
El Fazazi, H., Elgarej, M., Qbadou, M., and Mansouri, K. (2021). Design of an adaptive e-learning system based on multi-agent approach and reinforcement learning. Engineering, Technology &Applied Science Research, 11(1):6637–6644.
Elbeheri, G. and Everatt, J. (2007). Literacy ability and phonological processing skills amongst dyslexic and non-dyslexic speakers of Arabic. Reading and Writing, 20:273–294.
Elbeheri, G., Everatt, J., Reid, G., and Mannai, H. a. (2006). Dyslexia assessment in arabic. Journal of Research in Special Educational Needs, 6(3):143–152.
Ertmer, P. A. and Newby, T. J. (1993). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 6(4):50–72.
Friedmann, N. and Coltheart, M. (2016). Types of developmental dyslexia. In Bar-On, A. and Ravid, D., editors, Handbook of communication disorders: Theoretical, empirical, and applied linguistics perspectives, pages 1–37. Berlin, Boston: De Gruyter Mouton.
Friedmann, N. and Haddad-Hanna, M. (2014). Types of developmental dyslexia in Arabic. In Saiegh-Haddad, E. and Joshi, R. M., editors, Handbook of Arabic Literacy: Insights and Perspectives, pages 119–151. Springer Netherlands, Dordrecht.
Gauch, S., Speretta, M., Chandramouli, A., and Micarelli, A. (2007). User Profiles for Personalized Information Access, pages 54–89. Springer Berlin Heidelberg, Berlin, Heidelberg.
Kuo, Y.-C., Walker, A. E., Belland, B. R., and Schroder, K. E. E. (2013). A predictive study of student satisfaction in online education programs. International Review of Research in Open and Distributed Learning, 14(1):16–39.
Lerner, J. W. (1989). Educational interventions in learning disabilities. Journal of the American Academy of Child & Adolescent Psychiatry, 28(3):326 – 331.
Nist, L. and Joseph, L. M. (2008). Effectiveness and efficiency of flashcard drill instructional methods on urban first-graders’ word recognition, acquisition, maintenance, and generalization. School Psychology Review, 37(3):294–308.
Pickering, J. D. (2017). Measuring learning gain: Comparing anatomy drawing screencasts and paper-based resources. Anatomical Sciences Education, 10(4):307–316.
Read, J. C., MacFarlane, S., and Casey, C. (2002). Endurability, engagement and expectations: Measuring children’s fun. In Interaction design and children, volume 2, pages 1–23. Shaker Publishing Eindhoven.
Rodrigues, H., Almeida, F., Figueiredo, V., and Lopes, S. L. (2019). Tracking e-learning through published papers: A systematic review. Computers & Education, 136:87–98.
Verhoeven, L., Perfetti, C., and Pugh, K. (2019). Developmental dyslexia across languages and writing systems. Cambridge University Press.
Wang, Y.-S. (2003). Assessment of learner satisfaction with asynchronous electronic learning systems. Information & Management, 41(1):75–86.
World Health Organization (1992). The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. World Health Organization.

Comments

Comment on this article

[1] Abdul Hamid, S. S., Admodisastro, N., Kamaruddin, A., Manshor, N., and Ghani, A. A. A. (2017). Informing design of an adaptive learning model for student with dyslexia: A preliminary study. In Proceedings of the 3^rdInternational Conference on Human-Computer Interaction and User Experience in Indonesia, pages 67–75. Association for Computing Machinery.

[2] Al-Wabil, A., Zaphiris, P., and Wilson, S. (2006). Web design for dyslexics: Accessibility of Arabic content. In Miesenberger, K., Klaus, J., Zagler, W. L., and Karshmer, A. I., editors, Computers Helping People with Special Needs, pages 817–822, Berlin, Heidelberg. Springer Berlin Heidelberg.

[3] Alghabban, W. G. and Hendley, R. (2020a). Adapting e-learning to dyslexia type: An experimental study to evaluate learning gain and perceived usability. In Stephanidis, C., Harris, D., Li, W.C., Schmorrow, D. D., Fidopiastis, C. M., Zaphiris, P., Ioannou, A., Fang, X., Sottilare, R. A., and Schwarz, J., editors, HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games, pages 519–537, Cham. Springer International Publishing.

[4] Alghabban, W. G. and Hendley, R. (2020b). The impact of adaptation based on students’ dyslexia type: An empirical evaluation of students’ satisfaction. In Adjunct Publication of the 28^thACM Conference on User Modeling, Adaptation and Personalization, UMAP ’20 Adjunct, pages 41–46, New York, NY, USA. Association for Computing Machinery.

[5] Alghabban, W. G., Salama, R. M., and Altalhi, A. H. (2017). Mobile cloud computing: An effective multimodal interface tool for students with dyslexia. Computers in Human Behavior, 75:160–166.

[6] Alsobhi, A. and Alyoubi, K. (2020). Learning styles and dyslexia types -understanding their relationship and its benefits in adaptive e-learning systems. International Journal of Interactive Mobile Technologies (iJIM), 14(15):25–43.

[7] Alsobhi, A. Y. and Alyoubi, K. H. (2019). Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type. Data Technologies and Applications, 53(2):189–200.

[8] Alsobhi, A. Y., Khan, N., and Rahanu, H. (2014). Toward linking dyslexia types and symptoms to the available assistive technologies. In 2014 IEEE 14^thInternational Conference on Advanced Learning Technologies, pages 597–598. IEEE.

[9] Alsobhi, A. Y., Khan, N., and Rahanu, H. (2015). DAEL framework: A new adaptive e-learning framework for students with dyslexia. Procedia Computer Science, 51:1947–1956.

[10] Annett, M. (1996). Laterality and types of dyslexia. Neuroscience & Biobehavioral Reviews, 20(4):631–636.

[11] Beland, R. and Mimouni, Z. (2001). Deep dyslexia in the two languages of an arabic/french bilingual patient. Cognition, 82(2):77–126.

[12] Benmarrakchi, F. E., Kafi, J. E., and Elhore, A. (2017a). Communication technology for users with specific learning disabilities. Procedia Computer Science, 110:258–265.

[13] Benmarrakchi, F. E., Kafi, J. E., and Elhore, A. (2017b). User modeling approach for dyslexic students in virtual learning environments. International Journal of Cloud Applications and Computing (IJCAC), 7(2):1–9.

[14] Benmarrakchi, F. E., Kafi, J. E., Elhore, A., and Haie, S. (2017c). Exploring the use of the ICT in supporting dyslexic students’ preferred learning styles: A preliminary evaluation. Education and Information Technologies, 22:2939–2957.

[15] Bonacina, S., Cancer, A., Lanzi, P. L., Lorusso, M. L., and Antonietti, A. (2015). Improving reading skills in students with dyslexia: the efficacy of a sublexical training with rhythmic background. Frontiers in Psychology, 6:1–8.

[16] Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1):87–110.

[17] Brusilovsky, P. and Millán, E. (2007). User Models for Adaptive Hypermedia and Adaptive Educational Systems, pages 3–53. Springer Berlin Heidelberg, Berlin, Heidelberg.

[18] Bukhari, Y. A., AlOud, A. S., Abughanem, T. A., AlMayah, S. A., Al-Shabib, M. S., and Al-Jaber, S. A. (2016). Alaikhtibarat Altashkhisiat Lidhuyi Sueubat Altaalum Fi Madatay Allughat Alarabia Wa Alriyadiat Fi Almarhalat Alebtidaeiia [Diagnostic Tests for People with Learning Difficulties in the Subjects of Arabic Language and Mathematics at the Primary Stage]. General Administration for Special Education, The General Administration for Evaluation and Quality of Education, Ministry of Education, Saudi Arabia.

[19] Burke, M. D., Crowder, W., Hagan-Burke, S., and Zou, Y. (2009). A comparison of two path models for predicting reading fluency. Remedial and Special Education, 30(2):84–95.

[20] Daly III, E. J., Martens, B. K., Kilmer, A., and Massie, D. R. (1996). The effects of instructional match and content overlap on generalized reading performance. Journal of Applied Behavior Analysis, 29(4):507–518.

[21] Dolgin, A. B. (1975). How to match reading materials to student reading levels. The Social Studies, 66(6):249–252.

[22] El Fazazi, H., Elgarej, M., Qbadou, M., and Mansouri, K. (2021). Design of an adaptive e-learning system based on multi-agent approach and reinforcement learning. Engineering, Technology &Applied Science Research, 11(1):6637–6644.

[23] Elbeheri, G. and Everatt, J. (2007). Literacy ability and phonological processing skills amongst dyslexic and non-dyslexic speakers of Arabic. Reading and Writing, 20:273–294.

[24] Elbeheri, G., Everatt, J., Reid, G., and Mannai, H. a. (2006). Dyslexia assessment in arabic. Journal of Research in Special Educational Needs, 6(3):143–152.

[25] Ertmer, P. A. and Newby, T. J. (1993). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 6(4):50–72.

[26] Friedmann, N. and Coltheart, M. (2016). Types of developmental dyslexia. In Bar-On, A. and Ravid, D., editors, Handbook of communication disorders: Theoretical, empirical, and applied linguistics perspectives, pages 1–37. Berlin, Boston: De Gruyter Mouton.

[27] Friedmann, N. and Haddad-Hanna, M. (2014). Types of developmental dyslexia in Arabic. In Saiegh-Haddad, E. and Joshi, R. M., editors, Handbook of Arabic Literacy: Insights and Perspectives, pages 119–151. Springer Netherlands, Dordrecht.

[28] Gauch, S., Speretta, M., Chandramouli, A., and Micarelli, A. (2007). User Profiles for Personalized Information Access, pages 54–89. Springer Berlin Heidelberg, Berlin, Heidelberg.

[29] Kuo, Y.-C., Walker, A. E., Belland, B. R., and Schroder, K. E. E. (2013). A predictive study of student satisfaction in online education programs. International Review of Research in Open and Distributed Learning, 14(1):16–39.

[30] Lerner, J. W. (1989). Educational interventions in learning disabilities. Journal of the American Academy of Child & Adolescent Psychiatry, 28(3):326 – 331.

[31] Nist, L. and Joseph, L. M. (2008). Effectiveness and efficiency of flashcard drill instructional methods on urban first-graders’ word recognition, acquisition, maintenance, and generalization. School Psychology Review, 37(3):294–308.

[32] Pickering, J. D. (2017). Measuring learning gain: Comparing anatomy drawing screencasts and paper-based resources. Anatomical Sciences Education, 10(4):307–316.

[33] Read, J. C., MacFarlane, S., and Casey, C. (2002). Endurability, engagement and expectations: Measuring children’s fun. In Interaction design and children, volume 2, pages 1–23. Shaker Publishing Eindhoven.

[34] Rodrigues, H., Almeida, F., Figueiredo, V., and Lopes, S. L. (2019). Tracking e-learning through published papers: A systematic review. Computers & Education, 136:87–98.

[35] Verhoeven, L., Perfetti, C., and Pugh, K. (2019). Developmental dyslexia across languages and writing systems. Cambridge University Press.

[36] Wang, Y.-S. (2003). Assessment of learner satisfaction with asynchronous electronic learning systems. Information & Management, 41(1):75–86.

[37] World Health Organization (1992). The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. World Health Organization.

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