Skip to main content
SpringerLink
Log in

Understanding the Impact on Learners’ Reading Performance and Behaviour of Matching E-Learning Material to Dyslexia Type and Reading Skill Level

  • Conference paper
  • First Online:
HCI in Games: Serious and Immersive Games (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12790))

Included in the following conference series:

Abstract

Dyslexia is a universal reading difficulty where each individual with dyslexia can have a different combination of underlying reading difficulties. For instance, errors in letter identification or omissions or transpositions within or between words. Nowadays, adaptive e-learning and gamification have become more common. Different learner characteristics have been used when adapting e-learning systems, such as the user’s learning style or knowledge level. However, little attention has been directed towards understanding the benefits of using dyslexia type or reading skill level when adapting systems for learners with dyslexia. This, despite dyslexia type and reading skill level being significant factors in their education and learning. This paper reports on research which aims to improve this understanding through empirical studies designed to evaluate the benefits of adaptation with native Arabic speaking children with dyslexia. A mixed-methods approach was used. In the first experiment the focus is on a qualitative understanding of the effects of adaptation based on dyslexia type. The second experiment provides a quantitative analysis of the effects of adaptation based upon the reading skill level of learners with dyslexia. Findings revealed that the majority of learners are motivated when adapting learning material to dyslexia type. Analysis of the results indicated that adapting based on reading skill level does achieve improved learning gain and lead to greater learner satisfaction compared to a non-adaptive version. Implications of these experiments are discussed.

Supported by University of Tabuk and Princess Nourah Bint Abdulrahman University, Saudi Arabia.

W. G. Alghabban and H. M. Al-Dawsari—Both authors contributed equally to this research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Al-Dawsari, H., Hendley, R.: The effect of matching learning material to learners’ dyslexia type on reading performance. In: 2020 World Congress in Computer Science, Computer Engineering, and Applied Computing (2020, in press)

    Google Scholar 

  2. Alghabban, W.G., Salama, R.M., Altalhi, A.H.: Mobile cloud computing: an effective multimodal interface tool for students with dyslexia. Comput. Hum. Behav. 75, 160–166 (2017). https://doi.org/10.1016/j.chb.2017.05.014

    Article  Google Scholar 

  3. Alghabban, W.G., Hendley, R.: The impact of adaptation based on students’ dyslexia type: an empirical evaluation of students’ satisfaction. In: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2020, pp. 41–46. Adjunct, Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3386392.3397596

  4. Alghabban, W.G., Salama, R.M., Altalhi, A.: M-learning: effective framework for dyslexic students based on mobile cloud computing technology. Int. J. Adv. Res. Comput. Commun. Eng. 5(2), 513–517 (2016)

    Google Scholar 

  5. Aljojo, N., et al.: Arabic alphabetic puzzle game using eye tracking and chatbot for dyslexia. Int. J. Interact. Mob. Technol. (iJIM) 12(5), 58–80 (2018)

    Article  Google Scholar 

  6. AlRowais, F., Wald, M., Wills, G.: An Arabic framework for dyslexia training tools. In: 1st International Conference on Technology for Helping People with Special Needs (ICTHP-2013) (19/02/13–20/02/13), pp. 63–68, February 2013

    Google Scholar 

  7. AlRowais, F., Wald, M., Wills, G.: Developing a new framework for evaluating Arabic dyslexia training tools. In: Miesenberger, K., Fels, D., Archambault, D., Peňáz, P., Zagler, W. (eds.) ICCHP 2014. LNCS, vol. 8548, pp. 565–568. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08599-9_83

    Chapter  Google Scholar 

  8. Alsobhi, A.Y., Khan, N., Rahanu, H.: Toward linking dyslexia types and symptoms to the available assistive technologies. In: 2014 IEEE 14th International Conference on Advanced Learning Technologies, pp. 597–598. IEEE (2014). https://doi.org/10.1109/ICALT.2014.174

  9. Annett, M.: Laterality and types of dyslexia. Neurosci. Biobehav. Rev. 20(4), 631–636 (1996). https://doi.org/10.1016/0149-7634(95)00076-3

    Article  Google Scholar 

  10. Benmarrakchi, F.E., Kafi, J.E., Elhore, A.: User modeling approach for dyslexic students in virtual learning environments. Int. J. Cloud Appl. Comput. (IJCAC) 7(2), 1–9 (2017). https://doi.org/10.4018/IJCAC.2017040101

    Article  Google Scholar 

  11. Benmarrakchi, F., Kafi, J.E., Elhore, A.: Communication technology for users with specific learning disabilities. Procedia Comput. Sci. 110, 258–265 (2017). https://doi.org/10.1016/j.procs.2017.06.093. 14th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2017)/12th International Conference on Future Networks and Communications (FNC 2017)/Affiliated Workshops

  12. Benmarrakchi, F.E., El Kafi, J., Elhore, A., Haie, S.: Exploring the use of the ICT in supporting dyslexic students’ preferred learning styles: a preliminary evaluation. Educ. Inf. Technol. 22(6), 2939–2957 (2016). https://doi.org/10.1007/s10639-016-9551-4

    Article  Google Scholar 

  13. Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006). https://doi.org/10.1191/1478088706qp063oa

    Article  Google Scholar 

  14. Brusilovsky, P.: Adaptive hypermedia for education and training. In: Durlach, P.J., Lesgold, A.M. (eds.) Adaptive Technologies for Training and Education, pp. 46–66. Cambridge University Press. https://doi.org/10.1017/cbo9781139049580.006

  15. Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 3–53. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_1

    Chapter  Google Scholar 

  16. Chrysafiadi, K., Virvou, M.: Student modeling approaches: a literature review for the last decade. Expert Syst. Appl. 40(11), 4715–4729 (2013). https://doi.org/10.1016/j.eswa.2013.02.007

    Article  Google Scholar 

  17. Dolgin, A.B.: How to match reading materials to student reading levels. Soc. Stud. 66(6), 249–252 (1975). https://doi.org/10.1080/00220973.1943.11019435

    Article  Google Scholar 

  18. Elbeheri, G.: Dyslexia in Egypt. In: Smythe, I., Everatt, J., Salter, R. (eds.) The International Book of Dyslexia: A Guide to Practice and Resources, pp. 79–85. Wiley, Hoboken (2005)

    Google Scholar 

  19. Essalmi, F., Ayed, L.J.B., Jemni, M., Kinshuk, Graf, S.: A fully personalization strategy of e-learning scenarios. Comput. Hum. Behav. 26(4), 581–591 (2010). https://doi.org/10.1016/j.chb.2009.12.010

  20. Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988)

    Google Scholar 

  21. Friedmann, N., Coltheart, M.: Types of developmental dyslexia. In: Bar-On, A., Ravid, D. (eds.) Handbook of Communication Disorders: Theoretical, Empirical, and Applied Linguistics Perspectives, pp. 1–37. De Gruyter Mouton, Berlin (2016)

    Google Scholar 

  22. Friedmann, N., Haddad-Hanna, M.: Types of developmental dyslexia in Arabic. In: Saiegh-Haddad, E., Joshi, R.M. (eds.) Handbook of Arabic Literacy. LS, vol. 9, pp. 119–151. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-017-8545-7_6

    Chapter  Google Scholar 

  23. Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_2

    Chapter  Google Scholar 

  24. Ghaban, W., Hendley, R.: Can we predict the best gamification elements for a user based on their personal attributes? In: Fang, X. (ed.) HCII 2020. LNCS, vol. 12211, pp. 58–75. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50164-8_4

    Chapter  Google Scholar 

  25. Harandi, S.R.: Effects of e-learning on students’ motivation. Procedia Soc. Behav. Sci. 181, 423–430 (2015)

    Article  Google Scholar 

  26. Kangas, M., Siklander, P., Randolph, J., Ruokamo, H.: Teachers’ engagement and students’ satisfaction with a playful learning environment. Teach. Teach. Educ. 63, 274–284 (2017). https://doi.org/10.1016/j.tate.2016.12.018

    Article  Google Scholar 

  27. Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z.: E-learning personalization based on hybrid recommendation strategy and learning style identification. Comput. Educ. 56(3), 885–899 (2011). https://doi.org/10.1016/j.compedu.2010.11.001

    Article  Google Scholar 

  28. Lyytinen, H., Erskine, J., Kujala, J., Ojanen, E., Richardson, U.: In search of a science-based application: a learning tool for reading acquisition. Scand. J. Psychol. 50(6), 668–675 (2009). https://doi.org/10.1111/j.1467-9450.2009.00791.x

    Article  Google Scholar 

  29. M. Mastropavlou, V.Z.: Integrated intelligent learning environment for reading and writing d3. 2 - learning strategies specification report (2013)

    Google Scholar 

  30. Mahfoudhi, A., Everatt, J., Elbeheri, G.: Introduction to the special issue on literacy in Arabic. Read. Writ. 24(9), 1011–1018 (2011). https://doi.org/10.1007/s11145-011-9306-y

    Article  Google Scholar 

  31. Maravanyika, M., Dlodlo, N., Jere, N.: An adaptive recommender-system based framework for personalised teaching and learning on e-learning platforms. In: 2017 IST-Africa Week Conference (IST-Africa), pp. 1–9 (2017). https://doi.org/10.23919/ISTAFRICA.2017.8102297

  32. Melis, E., et al.: ActiveMath: a generic and adaptive web-based learning environment. Int. J. Artif. Intell. Educ. (IJAIED) 12, 385–407 (2001)

    Google Scholar 

  33. Mitrovic, A.: An intelligent SQL tutor on the web. Int. J. Artif. Intell. Educ. 13(2–4), 173–197 (2003)

    Google Scholar 

  34. Osipov, I.V., Nikulchev, E., Volinsky, A.A., Prasikova, A.Y.: Study of gamification effectiveness in online e-learning systems. Int. J. Adv. Comput. Sci. Appl. 6(2), 71–77 (2015)

    Google Scholar 

  35. Papanikolaou, K.A., Grigoriadou, M., Kornilakis, H., Magoulas, G.D.: Personalizing the interaction in a web-based educational hypermedia system: the case of inspire. User Model. User-Adap. Inter. 13(3), 213–267 (2003). https://doi.org/10.1023/A:1024746731130

    Article  Google Scholar 

  36. Read, J.C., MacFarlane, S.: Using the fun toolkit and other survey methods to gather opinions in child computer interaction. In: Proceedings of the 2006 Conference on Interaction Design and Children, IDC 2006, pp. 81–88. Association for Computing Machinery, New York (2006). https://doi.org/10.1145/1139073.1139096

  37. Schiaffino, S., Garcia, P., Amandi, A.: eteacher: providing personalized assistance to e-learning students. Comput. Educ. 51(4), 1744–1754 (2008). https://doi.org/10.1016/j.compedu.2008.05.008

    Article  Google Scholar 

  38. Self, J.: The defining characteristics of intelligent tutoring systems research: ITSs care, precisely. Int. J. Artif. Intell. Educ. (IJAIED) 10, 350–364 (1998)

    Google Scholar 

  39. Self, J.A.: Formal approaches to student modelling. In: Greer, J.E., McCalla, G.I. (eds.) Student Modelling: The Key to Individualized Knowledge-Based Instruction, pp. 295–352. Springer, Heidelberg (1994). https://doi.org/10.1007/978-3-662-03037-0_12

    Chapter  Google Scholar 

  40. Stansfield, J.L., Carr, B.P., Goldstein, I.P.: Wumpus advisor I. A first implementation of a program that tutors logical and probabilistic reasoning skills. AI memo 381 (1976)

    Google Scholar 

  41. Sun, R., Peterson, T.: A hybrid model for learning sequential navigation. In: Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA 1997, pp. 234–239. Towards New Computational Principles for Robotics and Automation (1997). https://doi.org/10.1109/CIRA.1997.613863

  42. Wang, Y.S.: Assessment of learner satisfaction with asynchronous electronic learning systems. Inf. Manag. 41(1), 75–86 (2003). https://doi.org/10.1016/S0378-7206(03)00028-4

    Article  Google Scholar 

  43. Weber, G., Brusilovsky, P.: ELM-ART – an interactive and intelligent web-based electronic textbook. Int. J. Artif. Intell. Educ. 26(1), 72–81 (2015). https://doi.org/10.1007/s40593-015-0066-8

    Article  Google Scholar 

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

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Alwajh University College, University of Tabuk, Tabuk, Saudi Arabia

    Weam Gaoud Alghabban

  2. Department of Computer Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Hadeel Mohammed Al-Dawsari

  3. School of Computer Science, University of Birmingham, Birmingham, UK

    Weam Gaoud Alghabban, Hadeel Mohammed Al-Dawsari & Robert Hendley

Authors
  1. Weam Gaoud Alghabban
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hadeel Mohammed Al-Dawsari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert Hendley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weam Gaoud Alghabban .

Editor information

Editors and Affiliations

  1. DePaul University, Chicago, IL, USA

    Xiaowen Fang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alghabban, W.G., Al-Dawsari, H.M., Hendley, R. (2021). Understanding the Impact on Learners’ Reading Performance and Behaviour of Matching E-Learning Material to Dyslexia Type and Reading Skill Level. In: Fang, X. (eds) HCI in Games: Serious and Immersive Games. HCII 2021. Lecture Notes in Computer Science(), vol 12790. Springer, Cham. https://doi.org/10.1007/978-3-030-77414-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77414-1_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77413-4

  • Online ISBN: 978-3-030-77414-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation