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

Syed Muhammad Raza Abidi, Mushtaq Hussain, Yonglin Xu and Wu Zhang
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Syed Muhammad Raza Abidi: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Mushtaq Hussain: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Yonglin Xu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Wu Zhang: Shanghai Institute of Applied Mathematics and Mechanics, School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Sustainability, 2018, vol. 11, issue 1, 1-21

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.

Keywords: education for sustainable development; confusion; intelligent tutoring system (ITS); ASSISTments; machine learning; computer-based homework; algebra mathematics technology education; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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