Next Article in Journal
Finite Element Analysis of Reinforced Concrete Bridge Piers Including a Flexure-Shear Interaction Model
Next Article in Special Issue
Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards
Previous Article in Journal
Effects of Irrigation with Desalinated Water on Lettuce Grown under Greenhouse in South Korea
Previous Article in Special Issue
The Effectiveness of Embodied Pedagogical Agents and Their Impact on Students Learning in Virtual Worlds
Open AccessArticle

Computational Characterization of Activities and Learners in a Learning System

Smart Learning Research Group, University of Alicante, 03690 Alicante, Spain
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(7), 2208; https://doi.org/10.3390/app10072208
Received: 24 January 2020 / Revised: 21 March 2020 / Accepted: 22 March 2020 / Published: 25 March 2020
(This article belongs to the Special Issue Smart Learning)
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. View Full-Text
Keywords: smart learning; learner characterization; student characterization; feature vector; adaptive learning smart learning; learner characterization; student characterization; feature vector; adaptive learning
Show Figures

Figure 1

MDPI and ACS Style

Real-Fernández, A.; Molina-Carmona, R.; Llorens-Largo, F. Computational Characterization of Activities and Learners in a Learning System. Appl. Sci. 2020, 10, 2208.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop