Adaptation schemes for question's level to be proposed by intelligent tutoring systems

Rina Azoulay, Esther David, Dorit Hutzler, Mireille Avigal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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 Bayesian 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.

Original languageEnglish
Title of host publicationICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence
PublisherSciTePress
Pages245-255
Number of pages11
ISBN (Print)9789897580154
DOIs
StatePublished - 2014
Externally publishedYes
Event6th International Conference on Agents and Artificial Intelligence, ICAART 2014 - Angers, Loire Valley, France
Duration: 6 Mar 20148 Mar 2014

Publication series

NameICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence
Volume1

Conference

Conference6th International Conference on Agents and Artificial Intelligence, ICAART 2014
Country/TerritoryFrance
CityAngers, Loire Valley
Period6/03/148/03/14

Keywords

  • Bayesian inference
  • Intelligent tutoring systems
  • Reinforcement learning

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