Computational Characterization of Activities and Learners in a Learning System

• Main Authors: Alberto Real-Fernández, Rafael Molina-Carmona, Faraón Llorens-Largo
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Applied Sciences
• Subjects: smart learning; learner characterization; student characterization; feature vector; adaptive learning
• Online Access: https://www.mdpi.com/2076-3417/10/7/2208

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.