Abstract
Emotions have a ubiquitous role in education and play a key role in learning and motivation. A motivated student learns in a better way than an indifferent student. There is evidence that tutors look at and react to the emotional state of students to motivate them and improve their learning. As regards computers, they have made a contribution in education. There are programs to teach almost any subject matter, but the real challenge consists in providing personalized support to human learning in view of previous knowledge and affective states to achieve an adaptive and intelligent educational-learning system. We have developed an affective behavior model that considers the affect and the knowledge state to provide students with an adaptive and intelligent instruction. The affective behavior model has been integrated into an environment to learn robotics. The instruction is presented by an animated intelligent agent. The affective behavior model maintains an intelligent representation of the student’s affect state to adapt the instruction by means of a dynamic Bayesian network (DBN). The affect diagnosis is based on the Cognitive Model of Emotions (CME) and on the five-factor model of personality. The model was evaluated and the results show a high precision in the affective student model and on students learning. We present the model to endow educational environments with affective behavior wherein students’ affect is reflected on the user-system interactions. Our affective student model sets an intelligent representation of the student. We present results from the model evaluations.
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Hernández, Y., Sucar, L.E., Arroyo-Figueroa, G. (2013). Affective Modeling for an Intelligent Educational Environment. In: Peña-Ayala, A. (eds) Intelligent and Adaptive Educational-Learning Systems. Smart Innovation, Systems and Technologies, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30171-1_1
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DOI: https://doi.org/10.1007/978-3-642-30171-1_1
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