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Reseach Article
Bayesian-ANFIS Student Model for an Intelligent Tutoring System
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 37 |
| Year of Publication: 2021 |
| Authors: Angela Makolo, Rukayat Olapojoye |
10.5120/ijais2021451907
|
Angela Makolo, Rukayat Olapojoye . Bayesian-ANFIS Student Model for an Intelligent Tutoring System. International Journal of Applied Information Systems. 12, 37 ( June 2021), 16-22. DOI=10.5120/ijais2021451907
Abstract
Intelligent tutoring system (ITS) is a software system that uses artificial intelligence techniques to interact with students and teach them in the same way as a teacher does. The task of dealing with the uncertainty management for the student model is challenging and various approaches in Artificial Intelligence have been proposed for uncertainty reasoning. The paper proposes a Bayesian - Adaptive Neuro-Fuzzy Inference system student model for an ITS. Several models have been developed over time; in a bid to improve the student model accuracy, our paper focuses on using a hybrid of Bayesian inference and Adaptive neuro-fuzzy inference systems as a soft computing technique for creating the desired model. The data gathered were subjected to pre-processing; evaluating the probability values for the questions using the students’ cumulative responses. These probability values, question level, students’ responses and understanding level formed the data matrix that were trained and tested using the Adaptive Neuro-fuzzy inference system (ANFIS). Our model gave a better prediction accuracy of 79.9% and therefore can be put to use by Intelligent Tutoring Systems for any domain.
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