A Framework for Non-intrusive Assessment of Student Attention Level in Classrooms
Conference Tracks
Teaching with Technology – Research
Abstract
Learning complex ideas in STEM involves not only cognitive skills, but is strongly influenced by the affective responses, such as attention. Understanding student attention can benefit the learning process. Progress in several technologies, like eye tracking, and Artificial Intelligence (AI) helps to determine student affect in classrooms non-intrusively. In this work, we present a preliminary study on determining student attentiveness in a computer-equipped classroom using off-the-shelf eye tracking equipment. The data provides insights into student behavior in a typical classroom. We also proposed a predictive model, which can assess student attentiveness based on the eye movements during instructions.
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Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
De, Pradipta; Veliyath, Narayanan; Hodges, Charles B.; Mitra, Aniruddha; and Allen, Andrew, "A Framework for Non-intrusive Assessment of Student Attention Level in Classrooms" (2019). SoTL Commons Conference. 49.
https://digitalcommons.georgiasouthern.edu/sotlcommons/SoTL/2019/49
A Framework for Non-intrusive Assessment of Student Attention Level in Classrooms
Posters
Learning complex ideas in STEM involves not only cognitive skills, but is strongly influenced by the affective responses, such as attention. Understanding student attention can benefit the learning process. Progress in several technologies, like eye tracking, and Artificial Intelligence (AI) helps to determine student affect in classrooms non-intrusively. In this work, we present a preliminary study on determining student attentiveness in a computer-equipped classroom using off-the-shelf eye tracking equipment. The data provides insights into student behavior in a typical classroom. We also proposed a predictive model, which can assess student attentiveness based on the eye movements during instructions.
