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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)
The aim of this review was to assess the effectiveness of specific exercise types on pulmonary functions, aerobic and functional capacity in patients with ankylosing spondylitis (AS). A systematic search of Cochrane Database of Systematic Review, MEDLINE (EBSCO), Physiotherapy Evidence Database (PEDro), CINAHL (EBSCO), PUBMED, AMED, EMBASE (OVID) was(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)
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)
Recently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant attention. In this study, we focused on the Named Entity Recognition (NER) problem on informal text types for Turkish. We utilized a semi-supervised learning approach based on neural(More)
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