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)

Abstract

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%.

Keywords

Adaptive learning Engagement Dyslexia Machine learning 

Notes

Acknowledgements

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.

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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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