Imène Jraidi
ABSTRACT
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.
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Index Terms
- A dynamic multimodal approach for assessing learners' interaction experience
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