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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Syed Muhammad Raza Abidi & Mushtaq Hussain & Yonglin Xu & Wu Zhang, 2018. "Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development," Sustainability, MDPI, vol. 11(1), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:105-:d:193111
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    References listed on IDEAS

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    1. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
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    1. Syed Muhammad Raza Abidi & Wu Zhang & Saqib Ali Haidery & Sanam Shahla Rizvi & Rabia Riaz & Hu Ding & Se Jin Kwon, 2020. "Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers," Sustainability, MDPI, vol. 12(15), pages 1-23, July.

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