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Towards intelligent E-learning systems

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Abstract

The prevalence of e-learning systems has made educational resources more accessible, interactive and effective to learners without the geographic and temporal boundaries. However, as the number of users increases and the volume of data grows, current e-learning systems face some technical and pedagogical challenges. This paper provides a comprehensive review on the efforts of applying new information and communication technologies to improve e-learning services. We first systematically investigate current e-learning systems in terms of their classification, architecture, functions, challenges, and current trends. We then present a general architecture for big data based e-learning systems to meet the ever-growing demand for e-learning. We also describe how to use data generated in big data based e-learning systems to support more flexible and customized course delivery and personalized learning.

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Notes

  1. https://www.udecity.com

  2. https://www.edx.org

  3. https://www.coursera.org

  4. https://www.futurelearn.com

  5. https://www.xuetangx.com

  6. https://www.fun-mooc.fr

  7. https://open.hpi.de

  8. https://learn.eduopen.org

  9. https://swayam.gov.in

  10. https://gacco.org

  11. https://thaimooc.org

  12. https://openedu.ru/university/hse

  13. https://moodle.org

  14. https://www.instructure.com

  15. https://open.edx.org

  16. https://www.sakailms.org

  17. http://www.weblearn.cn/

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Acknowledgements

The authors would like to thank the anonymous reviewers for their critical reading of the article and their valuable feedbacks, which have substantially helped to improve the quality and accuracy of this article.

Funding

This work was partly supported by Guangzhou Key Laboratory of Big Data and Intelligent Education (No. 2015010009) and National Natural Science Foundation of China (No. 61672389)

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  1. Guangzhou Key Laboratory of Big Data and Intelligent Education School of Computer Science South China Normal University Guangzhou, Guangdong, 510631, China

    Mengchi Liu

  2. Shanghai Astronomical Observatory Chinese Academy of Sciences, Shanghai, 200030, China

    Dongmei Yu

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Liu, M., Yu, D. Towards intelligent E-learning systems. Educ Inf Technol 28, 7845–7876 (2023). https://doi.org/10.1007/s10639-022-11479-6

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