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Definition of a Feature Vector to Characterise Learners in Adaptive Learning Systems

Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Adaptive learning can be defined as a learning model based on technology that can detect the students individual situation, context, learning needs and style, and the state of their learning process dynamically, and act according to them. So, it is necessary to define a student or learner model, that is, the set of information obtained and retained by the learning system about the learner so that the learner is characterised, and the learning process is adapted. In this work, we propose a learner model made of three main types of information: behavioural features, performance features and personal features. For this model to be useful in automatic learning systems, a formal feature vector must be then obtained. The features in the vector must be meaningful, discriminating and independent so that effective machine learning algorithms can be applied.

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Acknowledgements

This research is partially supported by Unidad Científica de Innovación Empresarial “Ars Innovatio”, Agència Valenciana d’Innovació and University of Alicante, Spain.

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Correspondence to Rafael Molina-Carmona .

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Real-Fernández, A., Molina-Carmona, R., Pertegal-Felices, M.L., Llorens-Largo, F. (2019). Definition of a Feature Vector to Characterise Learners in Adaptive Learning Systems. In: Visvizi, A., Lytras, M. (eds) Research & Innovation Forum 2019. RIIFORUM 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-30809-4_8

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  • DOI: https://doi.org/10.1007/978-3-030-30809-4_8

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