Team : MOCAH - Models and Tools in Knowledge Engineering for Human ApprenticeshipAxe : AID (👥👥).
Team leader :
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.
No event planned at present.Archives
- Ö. Yalçin, S. Lallé, C. Conati : “An Intelligent Pedagogical Agent to Foster Computational Thinking in Open-Ended Game Design Activities”IUI '22: 27th International Conference on Intelligent User Interfaces, Helsinki, Finland, pp. 633-645, (ACM)[Yalçin 2022]
- J. Hernandez, M. Muratet, M. Pierotti, Th. Carron : “Can We Detect Non-playable Characters’ Personalities Using Machine And Deep Learning Approaches?”European Conference on Game Based Learning, vol. 16 (1), Lisbonne, Portugal, pp. 271-279[Hernandez 2022a]
- A. Yessad : “Personalizing the Sequencing of Learning Activities by using the Q-Learning and the Bayesian Knowledge Tracing”17th European Conference on Technology-Enhanced Learning, Toulouse, France[Yessad 2022b]
- K. Oliver‑Quelennec, F. Bouchet, Th. Carron, K. Fronton Casalino, C. Pinçon : “Adapting Learning Analytics Dashboards by and for University Students”Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption, vol. 13450, Lecture Notes in Computer Science, Toulouse, France, pp. 299-309, (Springer International Publishing), (ISBN: 978-3-031-16290-9)[Oliver-Quelennec 2022c]
- E. Ferrier‑Barbut, Ph. Gauthier, V. Luengo, G. CANLORBE, M.‑A. Vitrani : “Measuring the quality of learning in a human-robot collaboration: a study of laparoscopic surgery”ACM Transactions on Human-Robot Interaction, (ACM)[Ferrier-Barbut 2022]
- R. Chaker, F. Bouchet, R. Bachelet : “How do online learning intentions lead to learning outcomes? The mediating effect of the autotelic dimension of flow in a MOOC”Computers in Human Behavior, vol. 134, pp. 107306, (Elsevier)[Chaker 2022]
- C. Canellas, F. Bouchet, Th. Arribe, V. Luengo : “Towards Learning Analytics Metamodels in a Context of Publishing”Proceedings of the 13th International Conference on Computer Supported Education, vol. 2, Prague (virtual), Czechia, pp. 45-54, (SciTePress)[Canellas 2021]
- M. Muratet, D. Garbarini : “Accessibility and serious games: What about Entity- Component-System software architecture?”GALA 2020, Laval, France[Muratet 2020a]
- 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, (David Publishing Company)[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]
- 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]
vanda.luengo (at) nulllip6.fr