Laboratoire d'InfoRmatique en Image et Systèmes d'information - Centre National de la Recherche Scientifique
Contact person: Bertrand David
bertrand.david [at] ec-lyon.fr
is a Full Professor of Computer Science at the Ecole Centrale de Lyon (ECL) in Lyon working in the area of human-computer interaction, mobile learning, cooperative systems and wearable computer use in pervasive environments. He was cofounder and director for 8 years of a multidisciplinary research lab ICTT, working on cooperative systems design and evaluation. He is coeditor in chief of RIHM: Francophone Journal of Human-Machine Interaction.
Description of the smart city learning group operating in the affiliated institution
LIRIS lab is a Computer Science & Information Communication and Technology research lab in which work more than 250 persons (100 permanents (professors, associate professors, researchers and technical staff) and 150 PhD students and post-docs) from 2 universities and 2 engineering schools of Lyon. LIRIS is associated to CNRS. Its organization is based on 2 departments and 5 research teams in each. SILEX team (Supporting Interaction and Learning by Experience) is working with 9 permanent researchers and 13 PhD students on Knowledge engineering for dynamic situations, Artificial Intelligence, Human learning. Main know-how is: Case-Based Reasoning, Audiovisual Active reading, Assistance for Computer Based Activities, Experience Based Knowledge Management, and Knowledge Discovering from Interaction Traces. Half of these researchers is concerned by different Learning styles as gaming, MOOC, contextual mobile learning, just-in time mobile learning is our main investigation contribution to Smart City learning.
Competences
Increased interest has been shown in context-aware mobile learning systems. These systems aim to provide learning supports via mobile devices and adapt them to specific educational needs, personal characteristics and particular circumstances of an individual learner or a group of interconnected learners. Work-based learning is a crucial approach to promote professionals’ working and learning efficiency, which is need-targeted, personalized, just-in-time and location-based. These characteristics predict that context-aware mobile learning systems can play a role in promoting the effect of work-based learning. However, relatively few context-aware mobile learning systems are proposed for learning and competence development in work contexts. Our work proposes the design, implementation and evaluation of a context-aware mobile system for work-based learning. Smart City is our natural application field.
Relevant achievements, best practices, products
• Proposal of an ontology-based hierarchical context model for work-based learning. We defined an initial context model for describing contextual information in work-based mobile learning. This model adopts a hierarchical designing approach which classifies context into a common layer and a domain layer. This approach improves the reusability of this context model. Also, the context model is built based on ontology for describing context semantically.
• Design of a set of adaptation strategies for work-based learning and an adaptation engine to execute these strategies. We proposed a set of adaptation strategies concerning learning unit selection, learning unit sequence, learning unit navigation, learning partner communication, and learning activity generation. These strategies adapt learning supports depending on professionals’ just-in-time learning context. We proposed also an adaptation engine which executes these proposed strategies to implement learning adaptations.
On going challenges
We are concerned by two important challenges:
• First one is related to study of different learning styles in relation with presentation (textual or visual), activity (passive or active implication in learning), exploration (individual or collaborative work) and explanation (theory or experiment based) and their use in personalization of learning activities, mainly in MOOC.
• Second one is related with complex networks observation and appreciation in order to propose appropriate learning activities. Complex networks are generally networks having lots of nodes and connections, such as Internet, citation network, online social network, interpersonal relation network and metabolic network etc. Due to Internet’s development and popularization, there are more and more researches on complex networks in recent years. Researches show that there exists community structure in complex networks. Connections within a community are dense, while connections between different communities are rare. Communities can be considered as groups having common interests or similar properties, so finding communities helps to mine some useful information of complex networks, for example, people having common interests or relations between people having different interests. How to detect communities in complex networks and propose them appropriate learning activities is a hotspot in recent years.
Relevant publications
• C. Yin, B. Zhang, B. David, N. Noël, R. Chalon, Z. Xiong
“Design and Case Study of WoBaLearn - A Work-based Learning System”
The 14th IEEE International Conference on Advanced Learning Technologies - ICALT2014, Athens, pp. 77-79, IEEE, 2014.
• B. Zhang, C. Yin, B. David, R. Chalon, Z. Xiong
“A Context-Awareness Model for Nowadays Professional Learning”
13th IEEE International Conference on Advanced Learning Technologies (ICALT 2013), IEEE ed. Beijing, China, pp. 23-25, IEEE, 2013.
• Zhang B., David B.T., Chalon R., Yin C., Zhou Y
“Contextual Mobile Learning for professionals working in 'Smart City' “
Workshop SCiLearn at The 11th International Conference on Web-based Learning (ICWL’12), 2-4 September 2012, Sinaia, Romania, 2012
• DAVID Bertrand, YIN Chuantao, CHALON René
'Contextual Mobile Learning Strongly Related to Industrial Activities: Principles and Case Study'
iJAC Journal, International Journal of Advanced Corporate Learning, vol. 2, issue 3, pp. 12-20, 2009
• YIN Chuantao, DAVID Bertrand, CHALON René
'Use your mobile computing devices to learn Contextual mobile learning system design and case studies'
2nd IEEE International Conference on Computer Science and Information Technology (IEEE ICCSIT 2009), Beijing, Chine, pp. 111-121, 2009
• YIN Chuantao, DAVID Bertrand, CHALON René
'A Contextual Mobile Learning System for Mastering Domestic and Professional Equipments'
IEEE International Symposium on IT in Medicine & Education(ITME2009), IEEE Press, IEEE Catalog Number: CFP0953E-CDR, issue ISBN: 978-, JI'NAN, Chine, pp. 773-779, 2009
• YIN Chuantao, DAVID Bertrand, CHALON René
'A contextual mobile learning system in our daily lives and professional situations'
8th European Conference on E-Learning, Bari, Italy, pp. 110-122, 2009
• DAVID B., YIN C., CHALON R.
“Contextual Mobile Learning strongly related to industrial activities: principles and case study”
ICELW 2009 (2nd International Conference on E-Learning in the Workplace), Columbia University in New York June 10th-12th, 2009, New York, NY, USA, pp. 50-59, 2009