Design a personalized e-learning system based on item response theory and artificial neural network approach
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Despite being limited in domain, ITS have proven to be effective in providing relevant content to students, interacting with students, and improving students' academic performance.
“Despite being limited in terms of the domain that a particular intelligent tutoring system addresses, they have proven to be effective in providing relevant content to students, interacting with students [6] and improving students’ academic performance [18, 41].”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [6]
It is not necessary that ITS would work in every context and facilitate every teacher.
“It is not necessary that ITS would work in every context and facilitate every teacher [7, 13, 46, 48].”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [7]
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Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021
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Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education
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- Authors
- Baylari, A., & Montazer, G. A.
- Affiliations
- Tarbiat Modares University (IR)
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2009-05
- Volume
- 36
- Issue
- 4
- Pages
- 8013-8021
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2034543389
- Cited by
- 182
- References
- 17
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 5 · 2023: 14 · 2022: 17 · 2021: 6 · 2020: 10 · 2019: 16 · 2018: 12 · 2017: 12 · 2016: 15 · 2015: 12 · 2014: 9 · 2013: 11 · 2012: 17
- FWCI
- 10.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Funders
- Iran Telecommunication Research Center
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- personalized e-learning, item response theory, artificial neural networks, adaptive testing, adaptive tests, personalized recommendation, multi-agent systems, web-based educational systems, interactivity
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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