research-article

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

Authors:

Ali A. Al-Jadaa,

Abdallatif S Abu-Issa,

Wasel T. Ghanem,

Mohammed S. HusseinAuthors Info & Claims

ICC '17: Proceedings of the Second International Conference on Internet of things, Data and Cloud Computing

Article No.: 146, Pages 1 - 5

https://doi.org/10.1145/3018896.3036389

Published: 22 March 2017 Publication History

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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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Cited By

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Ley TTammets KPishtari GChejara PKasepalu RKhalil MSaar MTuvi IVäljataga TWasson B(2023)Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model‐based learning analyticsJournal of Computer Assisted Learning10.1111/jcal.1284439:5(1397-1417)Online publication date: 29-Jun-2023
https://doi.org/10.1111/jcal.12844
Sergis SSampson D(2019)An Analysis of Open Learner Models for Supporting Learning AnalyticsLearning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment10.1007/978-3-030-15130-0_9(155-190)Online publication date: 25-May-2019
https://doi.org/10.1007/978-3-030-15130-0_9
Leonardou ARigou MGarofalakis J(2018)Open Learner Models in Smart Learning EnvironmentsCases on Smart Learning Environments10.4018/978-1-5225-6136-1.ch019(346-368)Online publication date: 28-Dec-2018
https://doi.org/10.4018/978-1-5225-6136-1.ch019

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Published In

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

General Chair:
Hani Hamdan
University of Paris-Saclay, Paris, France
,
Program Chairs:
Homero Toral-Cruz
University of Quintana Roo, Mexico
,
Sedat Akleylek
Ondokuz Mayis University, Turkey
,
Hamid Mcheick
Université du Québec, Canada
,
Publications Chair:
Djallel Eddine Boubiche
University of Batna, Algeria

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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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

ICC '17: Second International Conference on Internet of Things, Data and Cloud Computing

March 22 - 23, 2017

Cambridge, United Kingdom

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ICC '17 Paper Acceptance Rate 213 of 590 submissions, 36%;

Overall Acceptance Rate 213 of 590 submissions, 36%

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Cited By

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Ley TTammets KPishtari GChejara PKasepalu RKhalil MSaar MTuvi IVäljataga TWasson B(2023)Towards a partnership of teachers and intelligent learning technology: A systematic literature review of model‐based learning analyticsJournal of Computer Assisted Learning10.1111/jcal.1284439:5(1397-1417)Online publication date: 29-Jun-2023
https://doi.org/10.1111/jcal.12844
Sergis SSampson D(2019)An Analysis of Open Learner Models for Supporting Learning AnalyticsLearning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment10.1007/978-3-030-15130-0_9(155-190)Online publication date: 25-May-2019
https://doi.org/10.1007/978-3-030-15130-0_9
Leonardou ARigou MGarofalakis J(2018)Open Learner Models in Smart Learning EnvironmentsCases on Smart Learning Environments10.4018/978-1-5225-6136-1.ch019(346-368)Online publication date: 28-Dec-2018
https://doi.org/10.4018/978-1-5225-6136-1.ch019

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User Perceptions of Using an Open Learner Model Visualisation Tool for Facilitating Self-regulated Learning

Enhancing learning outcomes through self-regulated learning support with an Open Learner Model

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