Computational Characterization of Activities and Learners in a Learning System

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/104469
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Title: Computational Characterization of Activities and Learners in a Learning System
Authors: Real-Fernández, Alberto | Molina-Carmona, Rafael | Llorens Largo, Faraón
Research Group/s: Grupo de Investigación en Tecnologías Inteligentes para el Aprendizaje (Smart Learning)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Smart learning | Learner characterization | Student characterization | Feature vector | Adaptive learning
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 25-Mar-2020
Publisher: MDPI
Citation: Real-Fernández A, Molina-Carmona R, Llorens-Largo F. Computational Characterization of Activities and Learners in a Learning System. Applied Sciences. 2020; 10(7):2208. doi:10.3390/app10072208
Abstract: For a technology-based learning system to be able to personalize its learning process, it must characterize the learners. This can be achieved by storing information about them in a feature vector. The aim of this research is to propose such a system. In our proposal, the students are characterized based on their activity in the system, so learning activities also need to be characterized. The vectors are data structures formed by numerical or categorical variables such as learning style, cognitive level, knowledge type or the history of the learner’s actions in the system. The learner’s feature vector is updated considering the results and the time of the activities performed by the learner. A use case is also presented to illustrate how variables can be used to achieve different effects on the learning of individuals through the use of instructional strategies. The most valuable contribution of this proposal is the fact that students are characterized based on their activity in the system, instead of on self-reporting. Another important contribution is the practical nature of the vectors that will allow them to be computed by an artificial intelligence algorithm.
URI: http://hdl.handle.net/10045/104469
ISSN: 2076-3417
DOI: 10.3390/app10072208
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: https://doi.org/10.3390/app10072208
Appears in Collections:INV - Smart Learning - Artículos de Revistas

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