Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications

Agudo Peregrina, Ángel; Hernández García, Ángel y Iglesias Pradas, Santiago (2012). Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications. En: "International Symposium on Computers in Education (SIIE), 2012", 29/10/2012 - 31/10/2012, Andorra La Vella (Andorra). ISBN 978-1-4673-4743-3. pp. 1-6.

Descripción

Título: Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications
Autor/es:
  • Agudo Peregrina, Ángel
  • Hernández García, Ángel
  • Iglesias Pradas, Santiago
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: International Symposium on Computers in Education (SIIE), 2012
Fechas del Evento: 29/10/2012 - 31/10/2012
Lugar del Evento: Andorra La Vella (Andorra)
Título del Libro: Proceeding of International Symposium on Computers in Education (SIIE), 2012
Fecha: Octubre 2012
ISBN: 978-1-4673-4743-3
Materias:
Palabras Clave Informales: interactions; learning analytics; academic performance; typologies.
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería de Organización, Administración de Empresas y Estadística
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Learning analytics is the analysis of static and dynamic data extracted from virtual learning environments, in order to understand and optimize the learning process. Generally, this dynamic data is generated by the interactions which take place in the virtual learning environment. At the present time, many implementations for grouping of data have been proposed, but there is no consensus yet on which interactions and groups must be measured and analyzed. There is also no agreement on what is the influence of these interactions, if any, on learning outcomes, academic performance or student success. This study presents three different extant interaction typologies in e-learning and analyzes the relation of their components with students? academic performance. The three different classifications are based on the agents involved in the learning process, the frequency of use and the participation mode, respectively. The main findings from the research are: a) that agent-based classifications offer a better explanation of student academic performance; b) that at least one component in each typology predicts academic performance; and c) that student-teacher and student-student, evaluating students, and active interactions, respectively, have a significant impact on academic performance, while the other interaction types are not significantly related to academic performance.

Más información

ID de Registro: 20991
Identificador DC: http://oa.upm.es/20991/
Identificador OAI: oai:oa.upm.es:20991
URL Oficial: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6403184
Depositado por: Memoria Investigacion
Depositado el: 21 Oct 2013 15:49
Ultima Modificación: 21 Abr 2016 11:05
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