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Reinforcement Learning in Education: A Multi-armed Bandit Approach

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Emerging Technologies for Developing Countries (AFRICATEK 2022)

Abstract

Advances in reinforcement learning research have demonstrated the ways in which different agent-based models can learn how to optimally perform a task within a given environment. Reinforcement leaning solves unsupervised problems where agents move through a state-action-reward loop to maximize the overall reward for the agent, which in turn optimizes the solving of a specific problem in a given environment. However, these algorithms are designed based on our understanding of actions that should be taken in a real-world environment to solve a specific problem. One such problem is the ability to identify, recommend and execute an action within a system where the users are the subject, such as in education. In recent years, the use of blended learning approaches integrating face-to-face learning with online learning in the education context, has increased. Additionally, online platforms used for education require the automation of certain functions such as the identification, recommendation or execution of actions that can benefit the user, in this sense, the student or learner. As promising as these scientific advances are, there is still a need to conduct research in a variety of different areas to ensure the successful deployment of these agents within education systems. Therefore, the aim of this study was to contextualise and simulate the cumulative reward within an environment for an intervention recommendation problem in the education context.

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Notes

  1. 1.

    Source: https://www.sciencedirect.com/science/article/pii/S00078 specifix50618301471.

  2. 2.

    Source: https://iopscience.iop.org/article/10.1088/1742-6596/1717/1/012002.

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Authors and Affiliations

  1. Department of Computer Science, University of Pretoria, Pretoria, South Africa

    Herkulaas MvE Combrink & Vukosi Marivate

  2. School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa

    Benjamin Rosman

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  1. Herkulaas MvE Combrink
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  2. Vukosi Marivate
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  3. Benjamin Rosman
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Correspondence to Herkulaas MvE Combrink .

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Editors and Affiliations

  1. Central University of Technology, Free State, Bloemfontein, South Africa

    Muthoni Masinde

  2. University of the Western Cape, Cape Town, South Africa

    Antoine Bagula

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Combrink, H.M., Marivate, V., Rosman, B. (2023). Reinforcement Learning in Education: A Multi-armed Bandit Approach. In: Masinde, M., Bagula, A. (eds) Emerging Technologies for Developing Countries. AFRICATEK 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 503. Springer, Cham. https://doi.org/10.1007/978-3-031-35883-8_1

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  • DOI: https://doi.org/10.1007/978-3-031-35883-8_1

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