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A Benchmarking Study of Clustering Techniques Applied to a Set of Characteristics of MOOC Participants

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Conference

2016 ASEE Annual Conference & Exposition

Location

New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016

ISBN

978-0-692-68565-5

ISSN

2153-5965

Conference Session

Research Methods I: Developing Research Tools and Methods

Tagged Division

Educational Research and Methods

Tagged Topic

Diversity

Page Count

16

DOI

10.18260/p.26247

Permanent URL

https://peer.asee.org/26247

Download Count

45

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Paper Authors

biography

Rosa Cabedo Universidad Politecnica de Madrid

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Rosa Cabedo is Engineer in Computer Science
and currently Ph.D. Student at Technical University of Madrid (Spain) in the field of Open Education. The final purpose of her research is the identification and analysis of the profiles of language MOOC participants and the features of language learning (interaction, feedback, evaluation, certification) in order to adequate the design to MOOC format to facilitate the linguistic and communicativa competences improvement of LMOOC participants and their professional development. Her research focuses on the analysis of the heterogeneity of (L)MOOCs participants with the help of clustering techniques.

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biography

Tovar Caro Edmundo Universidad Politecnica de Madrid

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Edmundo Tovar, computer engineering educator, has a Ph.D. (1994) and a bachelor’s degree (1986) in computer engineering from the Universidad Politécnica de Madrid (UPM). He is a certified Software Development Professional (CSDP) from the IEEE Computer Society. He is Associate Dean for Quality and Strategic Planning in the Computing School of the Universidad Politécnica de Madrid. From this last position, he is in charge of the training for academic staff, the introduction of innovative solutions including new pedagogies, new approaches that improve student learning of technical skills and cultural skills, improved methods of blended learning, and others.
He works in the open educational resources area. He is leader of an Innovation Group in Education in the UPM. He is Executive Director of the OCW UPM Office and an elected member of the Board of Directors of the OpenCourseWare Consortium. He is author of many papers in engineering education, a member of the Steering Committee of and Coc-hair for Europe of Frontiers Education Conference (FIE), and member of the IEEE RITA Editorial Committee. He is IEEE Senior Member, Past Chairman of the Spanish Chapter, and, as member of the Board of Governors of the IEEE Education Society, he is currently Chair of the Distinguished Lectures Program for the IEEE Education Society.

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Manuel Castro Universidad Nacional de Educacion a Distancia

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Manuel Castro, Electrical and Computer Engineering educator in the Spanish University for Distance Education (UNED) has a doctoral industrial engineering degree from the ETSII/UPM. Full Professor of Electronics Technology inside the Electrical and Computer Engineering Department. He is Head of Department of Electrical and Computer Engineering at UNED. Was co-chair of the conference FIE 2014 (Frontiers in Education Conference) organized in Madrid, Spain, by the IEEE and the ASEE, and will co-chair REV 2016 (Remote engineering and Virtual Instrumentations) in Madrid, Spain. He is Fellow member of IEEE (for contributions to distance learning in electrical and computer engineering education) and member of the Board of Governors (BoG) (2005–2018) of the IEEE Education Society, President (2013-2014) and Jr Past-President (2015-2016) of the IEEE Education Society; Founder and Past-Chairman (2004-2006) of the Spanish Chapter of the IEEE Education Society, Past-Chair of the IEEE Spain Section (2010-2011) and IEEE Region 8 Educational Activities Subcommittee Chair. He has been awarded with the 2012 TAEE (Technologies Applied to Electronic Education) Professional Career Award, IEEE EDUCON 2011 Meritorious Service Award (jointly with Edmundo Tovar) of the EDUCON 2011 conference; 2010 Distinguished Member Award of the IEEE Education Society; 2009 Edwin C. Jones, Jr. Meritorious Service Award of the IEEE Education Society; with the 2006 Distinguished Chapter Leadership Award and for the collective work inside the Spanish Chapter of the IEEE Education Society with the 2011 Best Chapter Award (by the IEEE Region 8) and with the 2007 Chapter Achievement Award (by the IEEE Education Society). He is Member of the Board of the Spanish International Solar Energy Society (ISES).

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Abstract

Massive Open Online Courses (MOOC) format is characterized by the great diversity of enrolled people. Moreover, the lack of prior knowledge of their profiles constitutes an important barrier with a view to identifying and getting a better understanding of underlying relationships in the internal structure of the features that make up the profile of the participants in those courses. This paper has the aim of identifying and analyzing the feasible set of MOOC participants' profiles by running two unsupervised clustering techniques, K-Means as a partitional clustering algorithm and Kohonen’s Self-Organizing Maps (SOMs), hereinafter SOM, as a representative technique of Artificial Neural Networks (ANNs). The selected dataset for this paper comes from the MOOCKnowledge project data collection, which provides an opportunity to work with real-world data from hundreds of people. K-Means and SOM algorithms are performed with a subset of participants' features as input data. The clustering evaluation, meanwhile, is achieved with a selection of indices, an intra-cluster measure and an overall quality criterion for K-Means, and two measures related to topological ordering for SOM. The comparison of internal structure of both clustering (set of profiles) shows that there are similarities between them on the one hand and some pinpointed differences that can not be evaluated in advance without the opinion of an expert familiarized with the specifications of the MOOC on the other. Therefore, this comparison can not be considered conclusive until after a preliminary study of the results of the clustering interpretation for both algorithms. Finally, although it is not determined the clustering that best fits between K-Means and SOM, this study might help to provide a methodological guide on how to identify and select the appropriate clustering according to several quality criteria.

Cabedo, R., & Edmundo, T. C., & Castro, M. (2016, June), A Benchmarking Study of Clustering Techniques Applied to a Set of Characteristics of MOOC Participants Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26247

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