DIGITAL LIBRARY
ARE LEARNING ANALYTICS GOOD ENOUGH TO MONITOR STUDENT LEARNING? LEARN HOW THE EMMA PROJECT COPES WITH THE TRIANGULATION OF APPROACHES
1 Université de Bourgogne (FRANCE)
2 CSP - Innovazione nelle ICT s.c.a r.l. (ITALY)
About this paper:
Appears in: EDULEARN16 Proceedings
Publication year: 2016
Page: 5840 (abstract only)
ISBN: 978-84-608-8860-4
ISSN: 2340-1117
doi: 10.21125/edulearn.2016.2414
Conference name: 8th International Conference on Education and New Learning Technologies
Dates: 4-6 July, 2016
Location: Barcelona, Spain
Abstract:
The introduction of various educational software systems dramatically changed the process of educational delivery for both distance and on-campus modes of instruction. Learning tracking systems (including logs, quizzes, social network analysis, etc.) are growing in popularity as they seem to give face-validity to learning outcomes in the digital world. But the H Factor still needs to be considered very carefully. The EMMA experience is about a monitoring approach based on triangulation of methods.

Learning analytics are a vital aspect of the EMMA project, providing the data and input for other tasks. In addition, questionnaires to be completed by the participants supplement the learning analytics mainly with qualitative insights. By implementing learning analytics into the EMMA platform, it is possible to obtain additional valuable information about participants’ real behaviour on the platform in addition to their own judgements provided via the questionnaires. Learning analytics methodology enables to cluster the participants based on their patterns in their learning behaviour in the MOOCs and to approach them with relevant questions in the evaluation phase. So, both learning analytics and questionnaires are tools for measuring learning behaviour on the platform, in the learning community and in the social media context.

Additionally, the data collected by the questionnaires enable the consortium to profile learners as per the main socio-demographic variables (age, gender, educational background, professional profile, country origin, ...) and per additional variables (such as knowledge of languages) which will produce an interpretative framework to the learning behaviour analysis.

This presentation will start with an introduction to the EMMA approach to learning analytics, will describe the different tools used and the way they interact and will provide insights gained from the analysis of the data. Questions of data protection and ethics will also be addressed and debated.

This contribution is part of the initiatives of the European Multiple MOOC Aggregator (EMMA for Short), a European CIP Funded Project (www.europeanmoocs.eu)
Keywords:
learning analytics, data, ethics, MOOCs.