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Sustainability 2019, 11(1), 105; https://doi.org/10.3390/su11010105

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

1
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
2
Shanghai Institute of Applied Mathematics and Mechanics, School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
*
Authors to whom correspondence should be addressed.
Received: 17 November 2018 / Revised: 18 December 2018 / Accepted: 19 December 2018 / Published: 25 December 2018
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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 was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the 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 including: 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, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting 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 that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development. View Full-Text
Keywords: education for sustainable development; confusion; intelligent tutoring system (ITS); ASSISTments; machine learning; computer-based homework; algebra mathematics technology education; sustainable development 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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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.

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