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Cited by 43Intelligent Tutoring Systems and Adaptive LearningEdcuational Technology SystemsMetaheuristic Optimization Algorithms Research

Improved modeling of intelligent tutoring systems using ant colony optimization

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Rastegarmoghadam, Mahin, Ziarati, Koorush·2017·Education and Information Technologies
DOI 10.1007/s10639-016-9472-2
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Abstract

Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is personalized based on learning and solving problem styles. The purposed algorithm, based on ACO, generates the adaptive optimal learning path. The algorithm describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The algorithm parameters are tuned dynamically to conform to the actual pedagogical process. The article includes the results of implementation and experiment represent this algorithm is able to provide its main purpose which is finding optimal learning paths based on learning styles and improved performance of previous adaptive tutoring systems.

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  • In academia, adaptive learning and personalized learning are mostly used interchangeably.

    “when it comes to academia, it is mostly adaptive learning and personalized learning being used interchangeably Rastegarmoghadam and Ziarati (2017)”

    From A systematic literature review of personalized learning terms (2020) · cites this paper as Rastegarmoghadam and Ziarati (2017)

    The Introduction says that adaptation to a person is also called personalization and describes an adaptive e-learning system as a personalized system. This supports the conceptual overlap, but the paper does not establish that academics mostly use the terms interchangeably. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

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Authors
Rastegarmoghadam, M., & Ziarati, K.
Affiliations
Shiraz University (IR)
Corresponding author
  • Rastegarmoghadam, Mahin
  • Ziarati, Koorush
ORCID
Ziarati, Koorush
Venue
Education and Information Technologies
Publisher
Springer Science and Business Media LLC
Published
2016-03-04
Volume
22
Issue
3
Pages
1067-1087
ISSN
  • 1360-2357 (ISSN-L)
  • 1573-7608
DOI
10.1007/s10639-016-9472-2
OpenAlex
W2294945224
Cited by
43
References
0
Citations by year
2025: 1 · 2024: 11 · 2023: 6 · 2022: 6 · 2021: 4 · 2020: 9 · 2019: 3 · 2018: 2 · 2017: 1
FWCI
1.65
Type
journal article
Language
en
Open access
Licence
Locations
Education and Information Technologies (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
Keywords
intelligent tutoring systems, ant colony optimization, adaptive learning paths, learning styles, personalized learning, adaptive e-learning, swarm intelligence, learner behavior modeling, dynamic parameter adjustment, self-organization

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