loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Rina Azoulay 1 ; Esther David 2 ; Dorit Hutzler 1 and Mireille Avigal 3

Affiliations: 1 Jerusalem College of Technology, Israel ; 2 Ashkelon Academic College, Israel ; 3 The Open University of Israel, Israel

ISBN: 978-989-758-015-4

Keyword(s): Intelligent Tutoring Systems, Reinforcement Learning, Bayesian Inference.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: The main challenge in developing a good Intelligent Tutoring System (ITS) is suit the difficulty level of questions and tasks to the current student's capabilities. According to state of the art, most ITS systems use the Q-learning algorithm for this adaptation task. Our paper presents innovative results that compare the performance of several methods, most of which have not been previously applied for ITS, to handle the above challenge. In particular, to the best of our knowledge, this is the first attempt to apply the Bayesian inference algorithm to question level matching in ITS. To identify the best adaptation scheme based on this groundwork research, for the evaluation phase we used an artificial environment with simulated students. The results were benchmarked with the optimal performance of the system, assuming the user model (abilities) is completely known to the ITS. The results show that the best performing method, in most of the environments considered, is based on a Bayes ian Inference, which achieved 90% or more of the optimal performance. Our conclusion is that it may be worthwhile to integrate Bayesian inference based algorithms to adapt questions to a student's level in ITS. Future work is required to apply these empirical results to environments with real students. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.210.28.227

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Azoulay, R.; David, E.; Hutzler, D. and Avigal, M. (2014). Adaptation Schemes for Question's Level to be Proposed by Intelligent Tutoring Systems.In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 245-255. DOI: 10.5220/0004732402450255

@conference{icaart14,
author={Rina Azoulay. and Esther David. and Dorit Hutzler. and Mireille Avigal.},
title={Adaptation Schemes for Question's Level to be Proposed by Intelligent Tutoring Systems},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={245-255},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004732402450255},
isbn={978-989-758-015-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Adaptation Schemes for Question's Level to be Proposed by Intelligent Tutoring Systems
SN - 978-989-758-015-4
AU - Azoulay, R.
AU - David, E.
AU - Hutzler, D.
AU - Avigal, M.
PY - 2014
SP - 245
EP - 255
DO - 10.5220/0004732402450255

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.