Adding sensor-free intention-based affective support to an Intelligent Tutoring System

• Published in: Knowledge-based systems Vol. 132; pp. 85 - 93
• Main Authors: Arevalillo-Herráez, Miguel, Marco-Giménez, Luis, Arnau, David, González-Calero, José A.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.09.2017; Elsevier Science Ltd
• Subjects: Academic achievement; Adaptation; Affective states; Arousal; Autonomy; Classifiers; Emotion detection; Emotional factors; Emotions; Intelligent systems; Intelligent Tutoring Systems; Personalization; Self assessment; Word problem solving; Affective states; Intelligent Tutoring Systems; Personalization; Word problem solving; Emotion detection; Adaptation
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2017.06.024

•A sensor-free affect-aware Intelligent Tutoring System (ITS) is presented.•The ITS is able to adapt feedback to positively affect pleasure, arousal or dominance.•The system does not use a specific emotion detection system during operation.•Results show that the level of scaffolding influences the subject’s affective state.•An experiment in a real educational setting led to statistically significant results. Emotional factors considerably influence learning and academic performance. In this paper, we validate the hypothesis that learning platforms can adjust their response to have an effect on the learner’s pleasure, arousal and/or dominance, without using a specific emotion detection system during operation. To this end, we have enriched an existing Intelligent Tutoring System (ITS) by designing a module that is able to regulate the level of help provided to maximize valence, arousal or autonomy as desired. The design of this module followed a two-stage methodology. In the first stage, the ITS was adapted to collect data from several groups of students in primary education, by providing a random level of help and adding an emotional self-report based on Self Assessment Manikins. Then, the collected data was used to learn a series of classifiers. In operation, self-reporting was removed and the classifiers were used to choose the most convenient help level in order to positively affect the target variables. The effectiveness of the system has been extensively evaluated in a real educational setting, showing that the added module is successful at acting on the chosen target variable in a controlled way.