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Enhancing the intelligence of web tutoring systems using a multi-entry based open learner model

Published:22 March 2017Publication History

ABSTRACT

The accuracy of learner model is the heart of any Intelligent Tutoring System (ITS). More intelligence in the ITS needs a more accurate learner model. In the earlier versions of ITS, the student must submit a test before using the ITS. That test was used to build the student model, which contains information about the knowledge of the student, his/her misconceptions, preferences and other related issues. However, this method doesn't work efficiently for school students, because one test canfit accurately evaluate their knowledge and misconceptions. In this research, we implement a system (web application) to get the student model for school students by allowing the students, parents, and instructors to add their assessment and feedback to the model. Then the system uses these multi-entries together with the traditional test to build an enhanced student model (smart learner model). Furthermore, in order to support collaborative learning, the implemented system gives the student the access to open his/her model for other instructors and peers. The proposed system has been applied on a group of students, their parents and instructors. According to the obtained results and the surveys, the studentfis knowledge has been improved in many students. also the students, parents, instructors found the system to be useful, interesting and easy to use. Furthermore, all parties were happy to be engaged in the educational process.

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  • Published in

    cover image ACM Other conferences
    ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing
    March 2017
    1349 pages
    ISBN:9781450347747
    DOI:10.1145/3018896

    Copyright © 2017 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 22 March 2017

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    ICC '17 Paper Acceptance Rate213of590submissions,36%Overall Acceptance Rate213of590submissions,36%

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