Improving Learners’ Assessment and Evaluation in Crisis Management Serious Games: An Emotion-based Educational Data Mining Approach

Abstract

For several years, there has been growing interest in the development and use of serious games to improve individuals' quality of life and behavior. In particular, Crisis Management Serious Games (CMSG) have shown their potential for teaching people both technical and soft skills related to managing crises in a safe environment while reducing training costs. To improve their effectiveness, several evaluation approaches of CMSGs have been proposed. However, despite its interest, the learner's emotional state is often neglected. As a result, learners may end up with a deep frustration or boredom. Therefore, we propose an Educational Data Mining (EDM) approach to evaluate learners' affective states in collaborative CMSGs. This approach is applied to assess learners' engagement during a game-based evacuation scenario aiming to train and to raise awareness among students of a university on evacuating the present persons during a fire emergency situation. The experimental results confirm the major predictions of learners' affective dynamics and uncover novel findings namely the presence of transitions from boredom to frustration and from frustration to confusion. Our study shows that combining gaming and emotion aspects under an EDM approach to evaluate CMSGs is interesting since it gives reliable results in a less invasive way.