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2018 | OriginalPaper | Buchkapitel

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

verfasst von : Siti Suhaila Abdul Hamid, Novia Admodisastro, Noridayu Manshor, Azrina Kamaruddin, Abdul Azim Abd Ghani

Erschienen in: Recent Advances on Soft Computing and Data Mining

Verlag: Springer International Publishing

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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%.

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Titel: Dyslexia Adaptive Learning Model: Student Engagement Prediction Using Machine Learning Approach
verfasst von: Siti Suhaila Abdul Hamid
Novia Admodisastro
Noridayu Manshor
Azrina Kamaruddin
Abdul Azim Abd Ghani
Verlag: Springer International Publishing
Buch: Recent Advances on Soft Computing and Data Mining
Print ISBN: 978-3-319-72549-9

Electronic ISBN: 978-3-319-72550-5

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-72550-5_36

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