Social learning analytics for determining learning styles in a smart classroom

ABSTRACT

Social Learning Analytics (SLA) seeks to obtain hidden information in large amounts of data, usually of an educational nature. SLA focuses mainly on the analysis of social networks (Social Network Analysis, SNA) and the Web, to discover patterns of interaction and behavior of educational social actors. This paper incorporates the SLA in a smart classroom. Specifically, this paper proposes to determine the learning styles of the students in a smart classroom using SLA. In this proposal is analyzed external data from the web and social networks to build knowledge models about the students, in order to improve the learning processes that occur in the smart classrooms. In general, these SLA tasks will be organized in autonomous cycles, in order to integrate them with each other. The autonomic cycle will automate the execution of those tasks and the generation of knowledge models, in such a way to permanently monitor the learning process, observing it, analyzing it and determining the student learning styles. For the development of the SLA tasks, we will use concepts from the Semantic Mining, Text Mining, Data Mining, among other domains. Finally, we experiment in a test scenario, with results very interesting.

KEYWORDS:

• Social learning analytics
• smart classrooms
• learning styles
• everything mining

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

J. Aguilar

Jose Aguilar received the B. S. degree in System Engineering in 1987 (Universidad de los Andes-Venezuela), the M. Sc. degree in Computer Sciences in 1991 (Universite Paul Sabatier-France), and the Ph.D degree in Computer Sciences in 1995 (Universite Rene Descartes-France). He was a Postdoctoral Research Fellow in the Department of Computer Sciences at the University of Houston (1999–2000). He is a Titular Professor in the Department of Computer Science at the Universidad de los Andes, Mérida, Venezuela. He is member of the Mérida Science Academy and of the IEEE CIS Technical Committee on Neural Networks. Currently, he is Prometeo Researcher at the Escuela Politécnica Nacional, Universidad Técnica Particular de Loja and Yachay-EP, Quito. He has published more than 400 papers and 9 books, in the field of parallel and distributed systems, computational intelligence, data and semantic mining, multiagent systems, etc.

O. Buendia

Buendia Omar received the M. Sc. degree in Computer Sciences in 2017 (Universidad de los Andes-Venezuela), and currently is a Ph.D student at the same university. He work in the domain of artificial intelligence, data analysis, among others.

A. Pinto

Pinto Angel received the B. S. degree in System Engineering in 2004 (Universidad del Sinú, Montería, Colombia), the M. Sc. degree in Telematica in 2008 (Universidad Dr. Rafael Belloso Chacín -Venezuela), and the Ph.D degree in “Gestion de la Ciencia y la Tecnologia” in 2015 (Universidad Dr. Rafael Belloso Chacín, Venezuela). He was a Postdoctoral Research Fellow in “Gestion de la Ciencia y la Tecnologia” at the Universidad Dr. Rafael Belloso Chacín, Venezuela (2015–2016). He is a Titular Professor in the Departamento de Ingeniería de Sistemas, Universidad del Sinú, Montería, Colombia.

J. Gutiérrez

José Antonio Gutiérrez de Mesa is a Doctor from the University of Alcalá. His areas of interest are focused on Structures, Databases, Big Data and Data Analysis, as well as on the tools of Virtual Training together with the Planning and Management of Computer Systems. He has been responsible for nine years of the Research Group “Information Technologies for Training and Knowledge (TIFyC)” of the University of Alcalá. He has participated in 40 competitive research projects (acting in 7 of them as the main IP), and in 59 research projects with companies (in 16 as IP). He has published in 128 national and international conferences and workshops, and 32 publications and book chapters (16 indexed in JCR, 9 in the first quartile), with publications in prestigious journals such as Information Technology in Education, Advances in Intelligent Systems Computing, Computing and Education, International Journal of Engineering Education or Computing Standards and Interfaces.