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

Agudo Peregrina, Ángel and Hernández García, Ángel and Iglesias Pradas, Santiago (2012). Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications. In: "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.

Description

Title: Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications
Author/s:
  • Agudo Peregrina, Ángel
  • Hernández García, Ángel
  • Iglesias Pradas, Santiago
Item Type: Presentation at Congress or Conference (Article)
Event Title: International Symposium on Computers in Education (SIIE), 2012
Event Dates: 29/10/2012 - 31/10/2012
Event Location: Andorra La Vella (Andorra)
Title of Book: Proceeding of International Symposium on Computers in Education (SIIE), 2012
Date: October 2012
ISBN: 978-1-4673-4743-3
Subjects:
Freetext Keywords: interactions; learning analytics; academic performance; typologies.
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería de Organización, Administración de Empresas y Estadística
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 20991
DC Identifier: http://oa.upm.es/20991/
OAI Identifier: oai:oa.upm.es:20991
Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6403184
Deposited by: Memoria Investigacion
Deposited on: 21 Oct 2013 15:49
Last Modified: 21 Apr 2016 11:05
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