Dyslexia Adaptive Learning Model: Student Engagement Prediction Using Machine Learning Approach

  • Siti Suhaila Abdul Hamid
  • Novia Admodisastro
  • Noridayu Manshor
  • Azrina Kamaruddin
  • Abdul Azim Abd Ghani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)


Education barriers are synonym with people with dyslexia life experience. People with dyslexia encounter barriers such as in academic related areas, mistreated with negative reaction on their behaviour and limitation to acquire a suitable support to overcome the barriers. Therefore, this work focus on giving the support to help students with dyslexia deal with their difficulty through adaptively sense their behaviour for engagement perspective. For that reason, we apply machine learning approach that utilises Bag of Features (BOF) image classification to predict student engagement towards the learning content. The engagement prediction was relatively using frontal face of the 30 students. We used Speeded-Up Robust Feature (SURF) key point descriptor and clustered using k-Means method for the codebook in this BOF model. Then, we classify the model using 3 types of classifier which are Support Vector Machine (SVM), Naïve Bayes and K-Nearest Neighbour (k-NN) to find the best classification result. Through these methods, we managed to get high accuracy with 97–97.8%.


Adaptive learning Engagement Dyslexia Machine learning 



Special thanks to Dyslexia Association Malaysia (DAM), UPM IPS grant for the research funding and university’s ethics committee who approved our application to conduct this study.


  1. 1.
    Benyon, D., Murray, D.: Applying User Modelling to Human-Computer Interaction Design (1993)Google Scholar
  2. 2.
    Siti Zulaiha, A., Nik Noor Amalina Amirah, N.L., Hawa, M.E., Arifah Fasha, R., Mohd Hafiz, I.: Bijak Membaca—applying phonic reading technique and multisensory approach with interactive multimedia for dyslexia children. In: CHUSER 2012—2012 IEEE Colloquium on Humanities, Science and Engineering Research, pp. 554–559 (2012)Google Scholar
  3. 3.
    Slavuj, V., Kova, B., Jugo, I.: Intelligent tutoring systems for language learning. In: Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE (2015)Google Scholar
  4. 4.
    Chu, C.N., Yeh, Y.M.: Adaptiver reading: a design of reading browser with dynamic alternative text multimedia dictionaries for the text reading difficulty readers. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 661–664 (2010)Google Scholar
  5. 5.
    Francis, D.J., Shaywitz, S.E., Shaywitz, B.A., Stuenbing, K.K.: Developmental lag versus deficit models of reading disability: a longitudinal individual growth curves analaysis. Educ. Psychol. 88, 3–17 (1996)CrossRefGoogle Scholar
  6. 6.
    Oga, C., Haron, F.: Life experiences of individuals living with dyslexia in malaysia: a phenomenological study. Procedia—Soc. Behav. Sci. 46, 1129–1133 (2012)CrossRefGoogle Scholar
  7. 7.
    Ndombo, D.M.: An intelligent integrative assistive system for dyslexic learners. J. Assist. Technol. 7, 172–187 (2013)CrossRefGoogle Scholar
  8. 8.
    Rello, L., Ballesteros, M., Bigham, J.P.: A spellchecker for dyslexia. In: ASSETS 2015: the 17th International ACM SIGACCESS Conference of Computers and Accessibility, pp. 39–47 (2015)Google Scholar
  9. 9.
    Franceschini, S., Bertoni, S., Ronconi, L., Molteni, M., Gori, S., Facoetti, A.: “Shall We Play a Game?”: Improving Reading Through Action Video Games in Developmental Dyslexia. Current Developmental Disorders Reports (2015)Google Scholar
  10. 10.
    Rauschenberger, M.: DysMusic: Detecting Dyslexia by Web-based Games with Music Elements. Web4all’16 7–8 (2016)Google Scholar
  11. 11.
    Saputra, M.R.U.: LexiPal: Design, implementation and evaluation of gamification on learning application for dyslexia. Int. J. Comput. Appl. 131, 37–43 (2015)Google Scholar
  12. 12.
    Azhar, R., Tuwohingide, D., Kamudi, D., Sarimuddin, Suciati, N.: Batik image classification using SIFT feature extraction, bag of features and support vector machine. In: Procedia Computer Science. pp. 24–30. Elsevier Masson SAS (2015)Google Scholar
  13. 13.
    Yang, S., Bebis, G., Chu, Y., Zhao, L.: Effective face recognition using bag of features with additive kernels. J. Electron. Imaging 25, 13025 (2016)CrossRefGoogle Scholar
  14. 14.
    Al-Dmour, A., Abuhelaleh, M.: Arabic handwritten word category classification using bag of features. J. Theor. Appl. Inf. Technol. 89, 320–328 (2016)Google Scholar
  15. 15.
    Surakarin, W., Chongstitvatana, P.: Predicting types of clothing using SURF and LDP based on Bag of Features. In: ECTI-CON 2015–2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (2015)Google Scholar
  16. 16.
    Shaywitz, S.E., Shaywitz, B. a.: Dyslexia (specific reading disability). Biol. Psychiatry 57, 1301–1309 (2005)Google Scholar
  17. 17.
    Sahari, S.H., Johari, A.: Improvising reading classes and classroom environment for children with reading difficulties and dyslexia symptoms. In: Asia Pacific International Conference on Environment-Behaviour Studies. pp. 100–107. Elsevier B.V. Selection (2012)Google Scholar
  18. 18.
    Pardos, Z.A., Baker, R.S.J., Pedro, M.O.C.Z.S., Gowda, S.M., Gowda, S.M.: Affective States and State Tests: Investigating How Affect and Engagement During the School Year Predict End of Year Learning Outcomes (2012)Google Scholar
  19. 19.
    Whitehill, J., Serpell, Z., Yi-Ching Lin, Y.-C., Foster, A., Movellan, J.R.: The faces of engagement: automatic recognition of student engagementfrom facial expressions. IEEE Trans. Affect. Comput. 5, 86–98 (2014)CrossRefGoogle Scholar
  20. 20.
    Cetintas, S., Si, L., Xin, Y.P., Hord, C.: Automatic detection of off-task behaviors in intelligent tutoring systems with machine learning techniques. IEEE Trans. Learn. Technol. 3, 228–236 (2010)CrossRefGoogle Scholar
  21. 21.
    El Khayat, G.A., Mabrouk, T.F., Elmaghraby, A.S.: Intelligent serious games system for children with learning disabilities. In: Proceedings of CGAMES’2012 USA—17th International Conference on Computer Games AI, Animation. Mobile, Interactive. Multimedia, Educational Serious Games, pp. 30–34 (2012)Google Scholar
  22. 22.
    Magnisalis, I., Demetriadis, S.: Karakostas, a: adaptive and intelligent systems for collaborative learning support: a review of the field. IEEE Trans. Learn. Technol. 4, 5–20 (2011)CrossRefGoogle Scholar
  23. 23.
    Li, A.Q.: PoliSpell: an adaptive spellchecker and predictor for people with dyslexia. In: Proceedings of the 21th International Conference, UMAP 2013, Rome, Italy, 10–14 June 2013, pp. 302–309 (2013)Google Scholar
  24. 24.
    Li, K., Wang, F., Zhang, L.: A new algorithm for image recognition and classification based on improved Bag of Features algorithm. Opt.—Int. J. Light Electron Opt. 127, 4736–4740 (2016)Google Scholar
  25. 25.
    Hiba, C., Hamid, Z., Omar, A.: Bag of features model using the new approaches: a comprehensive study. Int. J. Adv. Comput. Sci. Appl. 1, 226–234 (2016)Google Scholar
  26. 26.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  27. 27.
    Mansourian, L., Abdullah, M.T., Abdullah, L.N., Azman, A.: Evaluating classification strategies in bag of SIFT feature method for animal recognition. In: Research Journal of Applied Sciences, Engineering and Technology. pp. 1266–1272 (2015)Google Scholar
  28. 28.
    Feng, J., Liu, Y., Wu, L.: Bag of visual words model with deep spatial features for geographical scene classification. Hindawi Comput. Intell. Neurosci. 2017 (2017)Google Scholar
  29. 29.
    Schmitt, D., McCoy, N.: Object Classification and Localization Using SURF Descriptors (2011)Google Scholar
  30. 30.
    Siti Suhaila, A.H., Novia, A., Abdul Azim, A.: Computer-based learning model to improve learning of the Malay Language amongst dyslexic primary school students. In: Proceedings of the Asia Pacific HCI and UX Design Symposium, pp. 37–41 (2015)Google Scholar
  31. 31.
    Tan, L., Sun, X., Khoo, S.T.: Can engagement be compared? Measuring academic engagement for comparison. In: Proceedings of the 7th International Conference on Educational Data Mining (EDM), pp. 213–216 (2014)Google Scholar
  32. 32.
    Nasirahmadi, A., Miraei Ashtiani, S.H.: Bag-of-Feature model for sweet and bitter almond classification. Biosyst. Eng. 156, 51–60 (2017)CrossRefGoogle Scholar
  33. 33.
    Abdelkhalak, B.: Hamid Zouaki: a surf-color moments for image retrieval based on bag-of-features. Eur. J. Comput. Sci. Inf. Technol. 1, 11–22 (2013)Google Scholar
  34. 34.
    Pancal, P., Pancal, S., Shah, S.: A comparison of SIFT and SURF. Int. J. Innov. Res. Comput. Commun. Eng. 1, 323–327 (2013)Google Scholar
  35. 35.
    Wang, R., Ding, K., Yang, J., Xue, L.: A novel method for image classification based on bag of visual words. J. Vis. Commun. Image Represent. 40, 24–33 (2016)CrossRefGoogle Scholar
  36. 36.
    Krig, S.: Comput. Vis. Metrics (2016)Google Scholar
  37. 37.
    Pérez, D.S., Bromberg, F., Diaz, C.A.: Image classification for detection of winter grapevine buds in natural conditions using scale-invariant features transform, bag of features and support vector machines. Comput. Electron. Agric. 135, 81–95 (2017)CrossRefGoogle Scholar
  38. 38.
    Anthimopoulos, M., Gianola, L., Scarnato, L., Diem, P., Mougiakkou, S.: A food recognition system for diabetic patients based on an optimized bag of features model. IEEE J. Biomed. Heal. inform. 18, 1261–1271 (2014)CrossRefGoogle Scholar
  39. 39.
    Dong, C.P.: Image classification using Naive Bayes classfier. Int. J. Comput. Sci. Electron. Eng. 4 (2016)Google Scholar
  40. 40.
    Karaaba, M.F., Surinta, O., Schomaker, L.R.B., Wiering, M.A.: Robust face identification with small sample sizes using bag of words and histogram of oriented gradients. In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 582–589 (2016)Google Scholar
  41. 41.
    Huang, H.-Y., Lin, C.-J.: Linear and Kernel classification: When to Use Which? In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 216–224 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Siti Suhaila Abdul Hamid
    • 1
  • Novia Admodisastro
    • 1
  • Noridayu Manshor
    • 1
  • Azrina Kamaruddin
    • 1
  • Abdul Azim Abd Ghani
    • 1
  1. 1.University Putra MalaysiaSeri KembanganMalaysia

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