The use of Video Games as learning tool is becoming increasingly widespread. Indeed, these games are well known as educational games or serious games. They mainly aim at providing to the learner an interactive, motivational and educational environment at the same time. In order to better study the necessary characteristics for the development of an effective serious game (both motivational and educational), we evaluated the physiological responses of participants during their interaction with our serious game, called HeapMotiv. We essentially measured a physiological index of engagement through an EEG wifi headset and studied the evolution of this index with the different missions and motivational strategies of HeapMotiv. Focusing on the gaming aspects, the analysis of this engagement index behavior showed the significant impact of motivational strategies on skills acquisition and motivational experience.

In this paper we seek to model the users' experience within an interactive learning environment. More precisely, we are interested in assessing three extreme trends in the interaction experience, namely flow (a perfect immersion within the task), stuck (a difficulty to maintain focused attention) and off-task (a drop out from the task). We propose a hierarchical probabilistic framework using a dynamic Bayesian network to simultaneously assess the probability of experiencing each trend, as well as the emotional responses occurring subsequently. The framework combines three-modality diagnostic variables that sense the learner's experience including physiology, behavior and performance, predictive variables that represent the current context and the learner's profile, and a dynamic structure that tracks the temporal evolution of the learner's experience. We describe the experimental study conducted to validate our approach. A protocol was established to elicit the three target trends as 44 participants interacted with three learning environments involving different cognitive tasks. Physiological activities (electroencephalography, skin conductance and blood volume pulse), patterns of the interaction, and performance during the task were recorded. We demonstrate that the proposed framework outperforms conventional non-dynamic modeling approaches such as static Bayesian networks, as well as three non-hierarchical formalisms including naive Bayes classifiers, decision trees and support vector machines.

In this paper, we propose to introduce the self-esteem component within learning process. More precisely, we explore the effects of learner self-esteem conditioning in a tutoring system. Our approach is based on a subliminal priming method aiming at enhancing implicit self-esteem. An experiment was conducted while participants were outfitted with biofeedback device. Three physiological sensors were used to continuously monitor learners' affective reactions namely electroencephalogram, skin conductance and blood volume pulse sensors. The purpose of this work is to analyze the impact of self-esteem conditioning on learning performance on one hand and learners' emotional and mental states on the other hand.

It is very common that students fail their exam because of an excessive stress. However, an attitude with no stress at all can cause the same thing as stress can be beneficial in some learning cases. Studying the stress level variation can then be very useful in a learning environment. In this paper, the aim is predicting the stress level variation of the learner in relation to his electrical brain activity within an experiment over two days. To attain that goal, three personal and non-personal characteristics were used: gender, usual mode of study and dominant activity between the first and second day. 21 participants were recruited for our experiment. Results were very encouraging: an accuracy of 71% was obtained by using the ID3 machine learning algorithm.