Eda Okur

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Existing Intelligent Tutoring Systems (ITSs) are unable to track affective states of learners. In this paper, we focus on the problem of emotional engagement, and propose to detect important affective states (i.e., 'Satisfied', 'Bored', and 'Confused') of a learner in real time. We collected 210 hours of data from 20 students through authentic classroom(More)
There are some implementations towards understanding students' emotional states through automated systems with machine learning models. However, generic AI models of emotions lack enough accuracy to autonomously and meaningfully trigger any interventions. Collecting self-labels from students as they assess their internal states can be a way to collect(More)
Affective states play a crucial role in learning. Existing Intelligent Tutoring Systems (ITSs) fail to track affective states of learners accurately. Without an accurate detection of such states, ITSs are limited in providing truly personalized learning experience. In our longitudinal research, we have been working towards developing an empathic autonomous(More)
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