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. © 2023 by the authors.

Keywords

Author Keywords

Taxonomy Learning environments glossary learner models learning content personalized learning software systems personalized learning software systems architecture

Index Keywords

Author Affiliations

Department of Computer Science and Information Technology, Abu Dhabi University, Abu Dhabi, United Arab Emirates

Department of Computer Science, University of Sharjah, Sharjah, Sharjah, United Arab Emirates

College of Technological Innovation, Zayed University, Dubai, United Arab Emirates

Funding & Acknowledgements

No funding information

References 10 References

1 Journal of Career and Technical Education, (2000)

2 Innovate to Educate System Re Design for Personalized Learning A Report from the 2010 Symposium College and Career Readiness and Success Center

3 Heba, Ismail Mahmood, Review of personalized language learning systems, Proceedings of the 2016 12th International Conference on Innovations in Information Technology, IIT 2016, (2017)

4 Zydney, Janet Mannheimer, Mobile apps for science learning: Review of research, Computers and Education, 94, pp. 1-17, (2016)

5 Ghanvatkar, Suparna, User models for personalized physical activity interventions: Scoping review, JMIR mHealth and uHealth, 7, 1, (2019)

6 Lapointe, Jean François, A review of personal profile features in personalized learning systems, Advances in Intelligent Systems and Computing, 596, pp. 46-55, (2018)

7 Raj, Nisha S., A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020, Journal of Computers in Education, 9, 1, pp. 113-148, (2022)

8 Language Learning Technology, (2001)

9 Stone, Elizabeth A., COMPUTER-ADAPTIVE TESTING FOR STUDENTS WITH DISABILITIES: A REVIEW OF THE LITERATURE, ETS Research Report Series, 2011, 2, pp. i-24, (2011)

10 Jung, Jaeyoung, An approach for personalized web-based vocabulary learning through word association games, Proceedings - 2008 International Symposium on Applications and the Internet, SAINT 2008, pp. 325-328, (2008)

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

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