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EADMS: A systemic approach to map emotions with Bloom's Affective Domain
Journal article   Open access   Peer reviewed

EADMS: A systemic approach to map emotions with Bloom's Affective Domain

E.Z. Ansari, A.M. Sajith and J.D. Stevens
International Journal of Inspired Education, Science and Technology (IJIEST), Vol.3(1), pp.21-32
2021
DOI: https://doi.org/10.48293/IJIEST-62
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Abstract

The quality of education depreciates as in-person classes were quickly replaced with virtual classes amidst the global pandemic. With the rise of the virtual classroom environment, educators lose the opportunity to interact with students and tailor the teaching style that best suits them. Educators use students' facial expressions and emotional responses to the content to predict the understanding levels subjectively. This paper proposes the Emotion-Affective Domain Mapping System (EADMS) as an alternative tool. The EADMS captures students' facial data during online classes in the form of a video and uses AI to determine emotions like contempt, anger, fear, happiness, disgust, surprise, and neutral state of emotion. The system breaks the video recording into three parts: the start of the class, between class, and the end of class to retrieve facial data and translate it to emotional data. The emotional data is mapped with the 'Affective Domain' of Bloom's Taxonomy to generate a graphical chart that plots the understanding level over the three periods. The EADMS successfully extracted information from videos on the internet and was reasonably reliable when tested with one of the authors.

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