Tutorial

TUTORIAL: MOOC

HOW TO DESCRIBE A MOOC FOR RESEARCH PURPOSES?

Although distance education started long ago (by postal mail in the 1840s), online courses via the Internet have grown up since the 2000s. However, these courses mainly stayed in institutional contexts and were not free of charge. MOOCs recently brought a big change.

A MOOC (Massive Open Online Course) is an online course, where many students participate, from a hundred to sometimes hundreds of thousands, hence the letter “M”. However, authors precisely define each term in the acronym very variously. The acronym appeared in the context of the CCK'08 (Connectivism and Connective Knowledge) course in 2008, which involved 25 students in the face-to-face course and about 2,300 online participants. It was a connectionist course. It means that the purpose of teaching team was to have participants learn a topic with one another.

The teacher is no longer the repository of some knowledge to transmit to the student or that students should acquire. In this type of MOOCs, learners co-construct knowledge; teachers facilitate, moderate, and expertise learners’ interactions and activities. The MOOC has learners create, become autonomous, learn in network, etc.

A few years later, in November 2011, MIT and Stanford proposed an online course, that they also called a MOOC. This online course on artificial intelligence (Introduction to Artificial Intelligence, Sebastian Thrun, Stanford Engineering) ran in parallel of the face-to-face course. It attracted 160,000 participants. This MOOC is definitely massive. In addition, it was “open” because no prerequisites and no fee were required to register. It was also online. Knowledge delivery relied on video presentations. Participants were assessed with short quizzes (MCQ - Multiple Choice Quizzes), automated tests, peer assessment (each participant rated one or more other participants), etc.

These two MOOCs have different spirits. Thus, in 2012, Siemens distinguished the first type of MOOC-cMOOC (connectionist MOOC) and the second one - xMOOC (non-connectionist MOOC, as those of "Coursera and edX" or MOOC on a ‘x’ content). However, in one category (Either xMOOC, or cMOOC), two MOOCs can be so different. Indeed, they can involve different teaching methods, different interaction modes, etc. Moreover, an xMOOC and cMOOC can involve the same teaching activities, the same interaction mode, etc. Thus, MOOC (Massive Online Open Course) is a single acronym that refers to many different realities.

Therefore, it is difficult for researcher:

To compare a (past, ongoing, or future) MOOC to another one

To compare a MOOC to other devices designed for distance or blended learning (e.g. LMS Learning Management System, CSCL-Computer Supported Collaborative Learning)

To estimate whether we can transpose the results of Technology Enhanced Learning (TEL) researches to MOOC researches.

To capitalize on MOOC research results themselves

To index MOOCs for research purposes

In one word, when we study MOOCs as objects of research, we would like to know which ones are close enough. Therefore we need to better describe MOOCs.In this tutorial, we aim to better describe a MOOC. Therefore, after having illustrated the limitations of existing typologies to describe a MOOC, we propose to describe MOOCs with two complementary levels. The macroscopic description level uses a simple typology. The microscopic description level is a “description framework”, which includes metadata and existing standard. Then we will apply both of them in order to describe a MOOC within two scenarios of uses, which are defined for two different researchers.

Finally, participants will work in teams. Each team will define their own scenario of use in their research field for a specific MOOC. They will fill in an online form, which relies on our two description levels.

Tutorial Leaders

Marilyne Rosselle,

Associate Professor,

Université Picardie Jules Verne, France

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After a Master in Cognitive Sciences, Marilyne Rosselle received her Ph.D. in Computer Sciences in 2001 from Université Henri Poincaré in Nancy (University of Lorraine, France). She has held various research and teaching positions since then. She is currently an Associate Professor in Amiens (Université Picardie Jules Verne).

Inside computer science, she focuses on models in Technology Enhanced Learning (TEL), Interactive Learning Environment (ILE), or the teaching-learning devices. She is interested in how learners and teachers grasp a learning device.

About MOOC, she participated in the research team that observes and accompanies the design of the “iNum” MOOC. INum was the prototype of a MOOC (that ran in France in May-June 2013), which led to the French platform for MOOCs: “FUN” (France Université Numérique - France Digital University).

The MINES (French acronym for “digital mission for the higher Education”) of the Ministry of Education ordered this research mission.Dr. Rosselle also co-led the first French research workshop on MOOC in May 28, 2013 in Toulouse, France. After that, she co-presented a workshop at the CNRS Summer School on "MOOC and TEL" in July 2014 and three colloquiums on MOOC on the theme of learner persistence in MOOCs.

From now, Dr. Rosselle has authored and co-authored more than 13 research publications about MOOC in refereed journals and conferences since 2012 (44 publications in total in TEL).

TUTORIAL: SMART CITIES

HOW TO IMPROVE THE SMARTNESS OF A CITY?

Based on the study of Rudolf Giffinger, Christian Fertner, Hans Kramar, Robert Kalasek, Natasa Pichler-Milanovic’, and Evert Meijers in “Smart cities: Ranking of European medium-sized cities” (Centre of Regional Science SRF, Vienna University of Technology, October 2007),”A Smart City is a city well performing in a forward-looking way in these six characteristics, built on the “smart” combination of endowments and activities of self-decisive, independent and aware citizens.

Each characteristic is defined by a number of factors and each factor is described by a number of indicators.

Actually, 33 factors were chosen to describe the 6 characteristics which are: Smart economy (competitiveness, including innovation, entrepreneurship, trademarks, productivity, flexibility, international embeddedness and ability to transform), smart governance (participation, including participation in decision-making, public and social services, transparent governance, political strategies and perspectives), smart environment (natural resources, including attractivity of natural conditions, pollution, environmental protection and sustainable resource management), smart people (social and human capital, including the level of qualification, affinity to lifelong learning, social and ethnic plurality, flexibility, creativity, cosmopolitanism/open mindedness and participation in public life), smart mobility (transport and ICT, including local accessibility, inter-national accessibility, availability of ICT-infrastructure, sustainable, innovative and safe transport systems), smart living (quality of life, including cultural facilities, health conditions, individual safety, housing quality, education facilities, touristic attractivity and social cohesion).

These six characteristics and the corresponding factors form the framework for the indicators and the assessment of a city’s performance as smart city.

The indicators that describe the factors of a smart city are derived from public and freely available data”. They focus on medium-sized cities, and on the analysis of characteristics and factors which are decisive for a successful forward-looking city development, using data from official, public and freely available sources, on the basis of 74 indicators. Thus, in order to obtain a given indicator, many actions have been done. For example: for “smart environment” and for the factor” sustainable resource management”, the smart city with a high level of indicator makes the actions: after midnight shut down the public lights and the fountains.

To the best of our knowledge, there are no studies that attempt to help cities make a decision in such a context. For example, helping cities identifying the actions to be implemented to improve their smartness and recommending such actions is an emerging and promising field of investigation.

Such decision approaches raise some challenges such as:

How to compare two cities?

Because there exist many different characteristics, factors and indicators

  • How to compare them?
  • Is standardization possible?

How to integrate stakeholder’s knowledge in a computer system in order to automate decision making?

Tutorial Leaders

Elsa Negre

Assistant Professor

Université Paris-Dauphine, France

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Elsa Negre received her Ph.D. in Computer Sciences in 2009 from François-Rabelais University of Tours, France. She was a postdoctoral fellow at Université du Québec en Outaouais (UQO), Canada 2010-2011, then at Laboratoire d'Informatique Nantes-Atlantique (LINA), France in 2011. She is currently an Assistant Professor at Paris-Dauphine University, France. Her research interests include recommender systems, similarity measures, information systems and knowledge management, data warehousing and social network analysis, early warning systems and smart cities. Dr. Negre authored and co-authored more than 20 publications in refereed journals and conferences.

Camille Rosenthal-Sabroux

Professor, Information System,

Université Paris-Dauphine, France

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Camille Rosenthal-Sabroux is a full Professor at Dauphine University, Paris IX. She is a graduate of PHD Pierre et Marie Curie, Paris VI, (1971) and HDR (habilitation à diriger des recherches) in Computer science (1996). From 1976 to 1989, she was an assistant professor in Paris XI, in Expert System. Since 1989, she is Professor at Paris Dauphine University, Paris IX and advises some large companies (AG2R, Sallustro [space] Redel Management, Bureau Veristas, PSA Citroën, Arcelor, France [space] Télécom) about Information Systems, Knowledge Management, Decision Science. She is the founder of the SIGECAD Group, whose domain topics are Information System, Knowledge Management and Decision Aid. Her main research topics are: Modeling Language (UML), Decision Aid, Knowledge Acquisition, Knowledge Management, and Information System. She is also the Director of the Master “Extended Company’s Information System: Audit and Consulting”. She published several books and articles.