Collaborative CBR-based Agents in the Preparation of Varied Training Lessons

Case-Based Reasoning (CBR) is widely used as a means of intelligent tutoring and e-learning systems. Indeed, course lessons are elaborated by analogy: this kind of system produces sets of exercises with respect to student level and class objective. Nevertheless, CBR systems always result in the same solution to a given problem description, whereas teaching requires that monotony be broken in order to maintain student motivation and attention. This is particularly true for sports where trainers must propose different exercises to practise the same skills for many weeks. We designed a CBR-based system that takes into account any previous lessons offered and designs new ones so as to vary the exercises each time: reference to prior lessons helps to avoid giving the same lesson twice. In addition, the system is based on collaborative agents, each taking into account the exercises proposed by others so that each activity is proposed only once during a lesson. Five qualified sports trainers tested and evaluated the ability of this system as a means to design varied aikido training lessons and proved that our system is capable of creating classroom activities that are diverse, changing, pertinent and consistent.