Equipo : MOCAH - Models and Tools in Knowledge Engineering for Human Apprenticeship
Responsable : Vanda Luengo Campus Pierre et Marie Curie 26-00/306
The MOCAH team (Models and Tools in Knowledge Engineering for Human Learning) is specialized in Technology Enhanced Learning (TEL) with an AI background (knowledge engineering, ontologies, conversational agents, data mining). The research themes have evolved for the last 10 years, with a reuse and development of our expertise into new themes. We have been working on authoring tools and cognitive diagnosis, using mainly symbolic approach. The core of our research is proposing models that embed human knowledge in TEL systems to improve learner models, diagnosis, feedback and the systems themselves. Knowledge is gathered (1) directly from humans (experts, learners, teachers...) using knowledge engineering techniques and/or (2) from data using educational data mining techniques.
Models are associated to open computer environments such as simulations, serious games and virtual reality systems, i.e. environnement with higher possibilities of interaction. Other less interactive environments like MOOCs interest us to address the issues they raise about the learner assessment, feedback and adaptation scenarios from large amount of data.
Currently MOCAH is striving to experiment in real situations with large number of students and various domains and levels. The team has obtained a chair (V. Luengo) from Sorbonne Universités in 2015 to apply their research to our university context. Additionally, MOCAH participates to 3 ANR projects, and has a contract with the DNE (Direction du Numérique Educatif). All these projects are funded until 2018.
In years to come, MOCAH is planning to reinforce its research in the following themes:
- Tracking, diagnosis and feedback taking into account data heterogeneity coming from various sources (activity from simulator, serious game, LMS, MCQ, etc.) and not only actions but also perceptions (eyetracking, haptic...). We are interested in exploring the potential of new paradigms such as connected objects (IoT), mobile learning, virtual and augmented reality, and open collaborative platforms.
- Working in merging knowledge extracted from data with the ones built from humans.
- Learning analytics methods and tools to help stakeholders, in particular teachers, to adapt and personalize learning. This involves in particular the use of techniques from Natural Language Processing to analyze students’ interactions with teachers and among themselves to build better knowledge models and/or propose adapted activities like students’ groups.
Technology Enhanced learning, Artificial intelligence in education, Learning analytics, E-Learning, cognitive diagnosis, learner modeling, adaptive feedback, serious gaming, authoring tools, metadata.
Actualmente, no hay ninguna manifestación planeada.
- Jason M. Harley, Cassia K. Carter, N. Papaioannou, F. Bouchet, Ronald S. Landis, R. Azevedo, L. Karabachian : “Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments”, User Modeling and User-Adapted Interaction, vol. 26 (2), pp. 177-219, (ISBN: 0924-1868) [Harley 2016]
- J.‑Ch. Marty, Th. Carron, Ph. Pernelle, S. Talbot, G. Houzet : “Mixed Reality Games”, International Journal of Game-Based Learning (IJGBL), vol. 5 (1), I. Patrick Felicia (Waterford Institute of Technology (Ed.), IGI-Global (ed.), pp. 32-47, (Patrick Felicia) [Marty 2015]
- F. Bouchet, J. Harley, R. Azevedo : “Can Adaptive Pedagogical Agents' Prompting Strategies Improve Students' Learning and Self-Regulation?”, 13th International Conference Intelligent Tutoring Systems:, Intelligent Tutoring Systems, Zagreb, Croatia, pp. 368-374 [Bouchet 2016]
- Ph. Dessus, O. Cosnefroy, V. Luengo : ““Keep Your Eyes on ’em all!”: A Mobile Eye-Tracking Analysis of Teachers’ Sensitivity to Students”, 11th European Conf. on Technology Enhanced Learning (EC-TEL 2016), Lyon, France, pp. 72-84, (Springer) [Dessus 2016]
- Y. Bourrier, J. Francis, C. Garbay, V. Luengo : “An Approach to the TEL Teaching of Non-technical Skills from the Perspective of an Ill-Defined Problem”, 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, vol. 9891, Lecture Notes in Computer Science, Lyon, France, pp. 555-558, (Springer) [Bourrier 2016]
- M. Muratet, A. Yessad, Th. Carron : “Framework for Learner Assessment in Learning Games”, 11th European Conference on Technology Enhanced Learning, vol. 9891, Lecture Notes in Computer Science, Lyon, France, pp. 622-626, (Springer) [Muratet 2016a]
- J. Melero, N. El‑Kechaï, A. Yessad, J.‑M. Labat : “Adapting Learning Paths in Serious Games: An Approach Based on Teachers' Requirements”, chapter in Computer Supported Education - 7th International Conference, CSEDU 2015, Lisbon, Portugal, May 23-25, 2015, Revised Selected Papers, vol. 583, Communications in Computer and Information Science, pp. 376-394, (Springer) [Melero 2016]
- B. Marne, J.‑M. Labat : “Model and Authoring Tool to Help Teachers Adapt Serious Games to their Educational Contexts”, International Journal of Learning Technology, vol. 9 (2), L. Uden, J.-Ch. Marty, Th. Carron (Eds.), pp. 161-180, (ISBN: 1477-8386) [Marne 2014]
- A. Yessad, I. Mounier, Th. Carron, F. Kordon, J.‑M. Labat : “Formal Framework to improve the reliability of concurrent and collaborative learning games”, EAI Endorsed Transactions on Serious Games, vol. 14 (2), pp. e4, (ISBN: 2034-8800) [Yessad 2014b]
- M. Taub, R. Azevedo, F. Bouchet, B. Khosravifar : “Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments?”, Computers in Human Behavior, vol. 39, pp. 356-367, (ISBN: 0747-5632) [Taub 2014a]
vanda.luengo (at) nulllip6.fr