Modeling of Errors Realized by a Human Learner in Virtual Environment for Training

Abstract

This study focuses on the notion of erroneous actions realized by human learners in Virtual Environments for Training. Our principal objective is to develop an Intelligent Tutoring System (ITS) suggesting pedagogical assistances to the human teacher. For that, the ITS must obviously detect and classify erroneous actions produced by learners during the realization of procedural and collaborative work. Further, in order to better support human teacher and facilitate his comprehension, it is necessary to show the teacher why learner made an error. Addressing this issue, we firstly modeling the Cognitive Reliability and Error Analysis Method (CREAM). Then, we integrate the retrospective analysis mechanism of CREAM into our existing ITS, thus enable the system to indicate the path of probable cause-effect explaining reasons why errors have occurred.

PEGASE: A Generic and Adaptable Intelligent System for Virtual Reality Learning Environments

“…It can also follow the precise progress of the procedure being carried out in the team in which the learner plays one or more roles. It is therefore able to detect the learner's errors with respect to the order of the actions to be completed and compliance with the conditions defined in the procedure [11].…”

PEGASE, un système tutoriel intelligent générique et adaptatif en environnement virtuel

“…Il peut également connaître plus spécifiquement à quel endroit est rendue l'exécution de la procédure en cours de réalisation dans l'équipe dans laquelle l'apprenant joue un ou plusieurs rôles. Il est ainsi capable de détecter des erreurs de l'apprenant sur l'ordre des actions et sur le non respect des conditions exprimées dans la procédure (Trinh et al, 2009).…”

“…Nous avons intégré le mécanisme d'analyse rétrospective de CREAM pour que le système puisse indiquer les liens causaux les plus probables expliquant l'occurrence des erreurs. Pour plus de détails sur le modèle des erreurs le lecteur pourra se référer à (Trinh et al, 2009).…”

An Artificial Intelligence Based Model for Algebra Education

“…There is not a unique approach that defines it, nonetheless. The systems can define the model in terms of: the quantity of revised topics [15], the mistakes made [11], [22], the answer timing [17], and so on; it can even be a combination of any.…”

“…For this, the inference machine makes the variable appear with the main screen, which has these three options associated with it. Later, the following rule is evaluated: 2,3,20,21,22,23,24,25, 26} SL This is a Reevaluation rule and has the function of making non-monotonic reasoning possible. Then, whenever the system executes the Div1 task, options 1, 2, 3 will be able to be evaluated again.…”