Comparison of Data Collection Models in an Intelligent Tutoring System for the Inclusive Education of the Learning-Disabled

Abstract

Intelligent tutoring systems were developed to provide effective learning and educate learners without the human interventions. However, identifying learning disabilities is a challenge that requires an effective data collection process. As, the process of data collection affects the generated learner’s details and their learning outcomes in an intelligent tutoring system. Therefore, in the present work, we have compared the data collection process to record the response of the learning-disabled learners for the developed ITS. Also, the machine learning (ML) techniques were explored to extract and select the features related to learning disabilities (dyslexia, dysgraphia, and dyscalculia). This study includes comparison of some popular and effective feature selection and classification algorithms. The feature selection methods utilized in the present study are relief, XGBoost (XGB), elastic net (EN), least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GDBT). The classification algorithms that have been compared based on these feature selection techniques for learning disability prediction are K-nearest neighbors (KNN), logistic regression (LR), linear discriminant analysis (LDA), classification and regression trees (CART), Naïve Bayes (NB), and support vector machine (SVM). The result proves both data collection methods were equally effective and accuracy of CART with the feature selection method LASSO yields highest performance for feature selection and classification process.