Preprint Article Version 1 This version is not peer-reviewed

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Version 1 : Received: 17 November 2018 / Approved: 19 November 2018 / Online: 19 November 2018 (11:46:56 CET)

A peer-reviewed article of this Preprint also exists.

Abidi, S.M.R.; Hussain, M.; Xu, Y.; Zhang, W. Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development. Sustainability 2019, 11, 105. Abidi, S.M.R.; Hussain, M.; Xu, Y.; Zhang, W. Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development. Sustainability 2019, 11, 105.

Journal reference: Sustainability 2018, 11, 105
DOI: 10.3390/su11010105

Abstract

Incorporating substantial sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study is to identify the confused students who have failed to master the skill(s) given by the tutors as a homework using Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models that include: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated and tested learning algorithms, performed stratified cross-validation and measured the performance of the models through various performance metrics i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity & Specificity. We found GLM, DT & RF are high accuracies achieving classifiers. However, other perceptions such as detection of unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students which were confused attempting the homework exercise and can help students foster their knowledge, and talent to play a vital role in environmental development.

Subject Areas

education for sustainable development; confusion; intelligent tutoring system (ITS); ASSISTments; machine learning; computer-based homework; algebra mathematics technology education; sustainable development

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