وحـدة : MOCAH - Models and Tools in Knowledge Engineering for Human Apprenticeship
Axe : AID (👥👥).
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.
- F. Harrak, F. Bouchet, V. Luengo : “From Student Questions to Student Profiles in a Blended Learning Environment”, Journal of Learning Analytics, vol. 6 (1), pp. 54-84 [Harrak 2019c]
- Th. Carron, G. Houzet, H. Abed, Ph. Pernelle, P.‑J. Lainé, S. Talbot : “Teaching Digital Literacy: The Outcomes from a Learning Lab”, Journal of electrical engineering, vol. 6 (2), pp. 75-84 [Carron 2018]
- Y. Bourrier, J. Francis, C. Garbay, V. Luengo : “A Hybrid Architecture for Non-Technical Skills Diagnosis”, Intelligent Tutoring Systems (ITS 2018), vol. 10858, Lecture Notes in Computer Science, Montreal, Canada, pp. 300-305, (Springer) [Bourrier 2018b]
- F. Harrak, F. Bouchet, V. Luengo, P. Gillois : “Profiling Students from Their Questions in a Blended Learning Environment”, LAK '18 Proceedings of the 8th International Conference on Learning Analytics and Knowledge, Sydney, Australia, pp. 102-110, (ACM) [Harrak 2018]
- J. Alvarez, J.‑Y. Plantec, M. Vermeulen, Ch. Kolski : “RDU Model dedicated to evaluate needed counsels for Serious Game projects”, Computers and Education, vol. 114, pp. 38-56, (Elsevier) [Alvarez 2017]
- 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, (Springer Verlag) [Harley 2016]
- B. Monterrat, A. Yessad, F. Bouchet, E. Lavoué, V. Luengo : “MAGAM: A Multi-Aspect Generic Adaptation Model for Learning Environments”, Data Driven Approaches in Digital Education, vol. 10474, Lecture Notes in Computer Science, Tallinn, Estonia, pp. 139-152, (Springer International Publishing) [Monterrat 2017b]
- Ph. Dessus, O. Cosnefroy, V. Luengo : ““Keep Your Eyes on ’em all!”: A Mobile Eye-Tracking Analysis of Teachers’ Sensitivity to Students”, Adaptive and Adaptable Learning. Proc. 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”, Adaptive and Adaptable Learning, vol. 9891, Lecture Notes in Computer Science, Lyon, France, pp. 555-558, (Springer) [Bourrier 2016b]
- M. Muratet, A. Yessad, Th. Carron : “Framework for Learner Assessment in Learning Games”, Adaptive and Adaptable Learning, vol. 9891, Lecture Notes in Computer Science, Lyon, France, pp. 622-626, (Springer) [Muratet 2016a]
vanda.luengo (at) nulllip6.fr