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Research Trends in Adaptive Online Learning: Systematic Literature Review (2011–2020)

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Abstract

With the improvement of Information and Communication Technologies (ICTs, online learning has become a viable means for teaching and learning. Nonetheless, online learning is still facing various challenges. The challenges include lack of support and loneliness experienced by learners. Adaptive online learning is one of the means that researchers are proposing to support learners and reduce the loneliness they experience in online learning. Research in adaptive online learning has been on the rise. Though there are several review studies that have attempted to provide summaries of research and development happening in this area, there is still lack of a comprehensive and up-to-date review that looks at the aspects of adaptive online learning systems in terms of the learner characteristics being modelled, domain model, adaptation model, the various techniques used to achieve the various tasks in those models and the impact the adaptive online learning has on learning. This study therefore was initiated in order to fill this gap. The study was carried out using a systematic literature review methodology. A total of 59 articles were used in the study, drawn from six databases namely Science direct, IEEE explore, ACM, Emerald, Springer and Taylor and Francis. The results indicate that: the most used learner characteristic is learning style even though the use of learning knowledge is on the rise; there is a rise in the use of machine learning algorithms in learner modelling; learning content is the most common target for adaptation; rules is the most utilized method in the adaptation model; and most adaptive online learning have not been evaluated in terms of learning. There is therefore a need for evaluation of the developed adaptive online learning and more studies that utilize more than one learner characteristic as the basis for adaptation and those that use machine learning.

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Abbreviations

CNN:

Convolutional Neural Networks

RL:

Reinforcement Learning

HMM:

Hidden Markov Model

ACM:

Association for Computing Machinery

IEEE:

Institute of Electrical and Electronics Engineers

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Acknowledgements

The authors wish to acknowledge the support given by the University of Nairobi’s School of Computing and Informatics faculty.

Funding

This research was funded by the Kenya National Research Fund 2016/2017 grant award, awarded to Kenyatta University, University of Nairobi, and The Cooperative University of Kenya in the multidisciplinary-multiinstitutional category.

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  1. University of Nairobi, Nairobi, Kenya

    Selina Atwani Ochukut, Robert Obwocha Oboko & Evans Miriti

  2. Kenyatta University, Nairobi, Kenya

    Elizaphan Maina

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Ochukut, S.A., Oboko, R.O., Miriti, E. et al. Research Trends in Adaptive Online Learning: Systematic Literature Review (2011–2020). Tech Know Learn 28, 431–448 (2023). https://doi.org/10.1007/s10758-022-09615-9

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