Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models
Abstract
This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field’s current challenges that need to be resolved in the upcoming years.
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Cites
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A systematic literature review of personalized learning terms
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When the Robotic Maths Tutor is Wrong - Can Children Identify Mistakes Generated by ChatGPT?
Helal, M., Holthaus, P., Wood, L., Velmurugan, V., Lakatos, G., Moros, S., & Amirabdollahian, F. (2024) · 2024 5th International Conference on Artificial Intelligence, Robotics and Control (AIRC) · doi:10.1109/airc61399.2024.10672220 · Cited by 6 · see citing works
“AI-powered applications began offering personalised learning experiences [26], intelligent tutoring systems [27], and automated grading [28].”
Background
- Authors
- Ismail, H., Hussein, N., Harous, S., & Khalil, A.
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2023-07-20
- Volume
- 13
- Issue
- 7
- Pages
- 741
- ISSN
- 2227-7102
- Cited by
- 22
- References
- 3
- Language
- en
- Funders
- Office of Research and Sponsored Programs at Abu Dhabi University
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