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- W4381956578 abstract "Facial expressions of learners are relevant to their learning outcomes. The recognition of their emotional status influences the benefits of instruction or feedback provided by the intelligent tutor in education. However, learners’ emotions expressed during interactions with the intelligent tutor are mostly detected by self-reports of learners or judges who observe them in manually. The automated Facial Emotion Recognition (FER) task has been a challenging problem for intelligent tutors. The state-of-art automated FER methods target six basic emotions instead of learning-related emotions (e.g., neutral, confused, frustrated, and bored). Thus our research contributes to training a machine learning (ML) model to recognise learning-related emotions for intelligent tutors automatically, based on an Affective Dynamics (AD) model. We implement the AD model into our loss function (AD-loss) to fine-tune the ML model. In the test scenario, the AD-loss method improves the performance of state-of-art FER algorithms." @default.
- W4381956578 created "2023-06-26" @default.
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- W4381956578 date "2023-01-01" @default.
- W4381956578 modified "2023-10-18" @default.
- W4381956578 title "Affective Dynamic Based Technique for Facial Emotion Recognition (FER) to Support Intelligent Tutors in Education" @default.
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- W4381956578 doi "https://doi.org/10.1007/978-3-031-36272-9_70" @default.
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