Title: A characterisation of passive and active interactions and their influence on students' achievement using Moodle LMS logs

Authors: Felix Pascual-Miguel, Julian Chaparro-Pelaez, Angel Hernandez-Garcia, Santiago Iglesias-Pradas

Addresses: Universidad Politecnica de Madrid, Grupo de Tecnologias de la Informacion para la Gestion Empresarial, Escuela Tecnica Superior de Ingenieros de Telecomunicacion, Av. Complutense 30, 28040 Madrid, Spain. ' Universidad Politecnica de Madrid, Grupo de Tecnologias de la Informacion para la Gestion Empresarial, Escuela Tecnica Superior de Ingenieros de Telecomunicacion, Av. Complutense 30, 28040 Madrid, Spain. ' Universidad Politecnica de Madrid, Grupo de Tecnologias de la Informacion para la Gestion Empresarial, Escuela Tecnica Superior de Ingenieros de Telecomunicacion, Av. Complutense 30, 28040 Madrid, Spain. ' Universidad Politecnica de Madrid, Grupo de Tecnologias de la Informacion para la Gestion Empresarial, Escuela Tecnica Superior de Ingenieros de Telecomunicacion, Av. Complutense 30, 28040 Madrid, Spain

Abstract: During the last years, there has been much concern about learning management systems| (LMS) effectiveness when compared to traditional learning and about how to assess students| participation during the course. The tracking and monitoring capabilities of most recent LMS have made it possible to analyse every interaction in the system. The issues addressed on this study are: a) Is LMS student|s interaction an indicator of academic performance?; b) Are different results in performance expected between distance and in-class LMS-supported education?; c) How can LMS interactions from logs be categorised?; d) May this categorisation detect |learning witnesses|? To answer these questions, a set of interaction types from Moodle LMS activity record logs has been analysed during two years in online and in-class Master|s degrees at the UPM. The results show partial or no evidence of influence between interaction indicators and academic performance, although the proposed categorisation may help detect learning witnesses.

Keywords: e-learning; electronic learning; online learning; active interactions; passive interactions; student performance; learning management systems; LMS effectiveness; activity logs; Moodle; student achievements; traditional learning; student participation; tracking capabilities; monitoring capabilities; interaction analysis; academic performance; distance education; in-class education; categorisation; learning witnesses; interaction types; activity records; internet; world wide web; Master|s degrees; Technical University; Madrid; UPM; Spain; higher education; universities; interaction indicators; technology enhanced learning.

DOI: 10.1504/IJTEL.2011.041283

International Journal of Technology Enhanced Learning, 2011 Vol.3 No.4, pp.403 - 414

Published online: 26 Feb 2015 *

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