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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.
References
- M. A. S. Machado, T. D. R. G. Moreira, L. F. A. M. Gomes, A. M. Caldeira, and D. J. Santos, “A Fuzzy Logic Application in Virtual Education,” Procedia Comput. Sci., vol. 91, no. Itqm, pp. 19–26, 2016, doi: 10.1016/j.procs.2016.07.037.
- J. J. Castro-schez, C. Glez-morcillo, J. Albusac, and D. Vallejo, “Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19 . The COVID-19 resource centre is hosted on Elsevier Connect , the company ’ s public news and information ,” no. January, 2020.
- M. Badaracco and L. Mart??nez, “A fuzzy linguistic algorithm for adaptive test in Intelligent Tutoring System based on competences,” Expert Syst. Appl., vol. 40, no. 8, pp. 3073–3086, 2013, doi: 10.1016/j.eswa.2012.12.023.
- B. Chakraborty and M. Sinha, “Student evaluation model using bayesian network in an intelligent E-learning system,” IIOAB J., vol. 7, no. 2, pp. 51–60, 2016.
- K. Almohammadi, H. Hagras, D. Alghazzawi, and G. Aldabbagh, “a Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems Within E-Learning,” vol. 7, no. 1, pp. 47–64, 2017, doi: 10.1515/jaiscr-2017-0004.
- P. Asopa, S. Asopa, N. Joshi, and I. Mathur, “Evaluating student performance using fuzzy inference system in fuzzy ITS,” 2016 Int. Conf. Adv. Comput. Commun. Informatics, pp. 1847–1851, 2016, doi: 10.1109/ICACCI.2016.7732318.
- K. Pai, B. Kuo, C. Liao, and Y. Liu, “An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning,” Educ. Psychol., vol. 0, no. 0, pp. 1–16, 2020, doi: 10.1080/01443410.2020.1731427.
- A. Ramírez-Noriega, R. Juárez-Ramírez, and Y. Martínez-Ramírez, “Evaluation module based on Bayesian networks to Intelligent Tutoring Systems,” Int. J. Inf. Manage., 2016, doi: 10.1016/j.ijinfomgt.2016.05.007.
- A. (2020). Cheng, X., Sun, J., & Zarifis, “Artificial intelligence and deep learning in educational technology research and practice,” vol. 0, no. 0, pp. 1–4, 2020, doi: 10.1111/bjet.13018.
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