A Hybrid Architecture for Non-Technical Skills Diagnosis

Yannick Bourrier (1, 2) , Jambon Francis (3) , Catherine Garbay (2) , Vanda Luengo (1)

1 MOCAH - Modèles et Outils en ingénierie des Connaissances pour l'Apprentissage Humain
2 AMA - Analyse de données, Modélisation et Apprentissage automatique [Grenoble]
3 LIG - Laboratoire d'Informatique de Grenoble

Our Virtual Learning Environment aims at improving the abilities of experienced technicians to handle critical situations through appropriate use of non-technical skills (NTS), a high-stake matter in many domains as bad mobilization of these skills is the cause of many accidents. To do so, our environment dynamically generates critical situations designed to target these NTS. As the situations need to be adapted to the learner’s skill level, we designed a hybrid architecture able to diagnose NTS. This architecture combines symbolic knowledge about situations, a neural network to drive the learner’s performance evaluation process, and a Bayesian network to model the causality links between situation knowledge and performance to reach NTS diagnosis. A proof of concept is presented in a driving critical situation.

Mots clés: Ill-defined domains; Neural networks; Bayesian networks

Domaines: Environnements Informatiques pour l'Apprentissage Humain

Yannick Bourrier, Jambon Francis, Catherine Garbay, Vanda Luengo. A Hybrid Architecture for Non-Technical Skills Diagnosis. Intelligent Tutoring Systems (ITS 2018), Jun 2018, Montreal, Canada. pp.300-305, ⟨10.1007/978-3-319-91464-0_31⟩. ⟨hal-01774652⟩