TY - GEN
T1 - Development and Field-Testing of a Non-intrusive Classroom Attention Tracking System (NiCATS) for Tracking Student Attention in CS Classrooms
AU - Sanders, Andrew Logan
AU - Boswell, Bradley
AU - Allen, Andrew
AU - Singh Walia, Gursimran
AU - Shakil Hossain, Md
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This Research to Practice Full Paper presents our Non-intrusive Classroom Attention Tracking System (NiCATS) and discusses the data collected through it. Academic instructors and institutions desire the ability to accurately and autonomously measure students' attentiveness in the classroom. Generally, college departments use unreliable direct communication from students, observational sit-ins, and end-of-semester surveys to collect feedback regarding their courses. Each of these methods of collecting feedback is useful but does not provide automatic feedback regarding the pace and direction of lectures. It has been widely reported that attention levels during passive classroom lectures generally drop after about ten to thirty minutes and can be restored to normal levels with regular breaks, novel activities, mini-lectures, case studies, or videos. Tracking these 'drops' in attention can be crucial for the accurate timing of these change-ups in activities. This allows for maximal attention and a greater amount of deeply learned material. Autonomously collected data can also be used either in real-time or post-hoc to alter the design and presentation of lectures. Keeping track of student attention is vital to having confidence in delivering material. Even if lectures do not break up presentation slides with attention-raising activities, they can still show more important information during periods of high attention and less important information during periods of low attention. This area of research has applications both in in-person classrooms and online learning environments. The long-term goals of this research can prove invaluable for large in-person classrooms or classrooms where students' faces are obscured, such as behind computer monitors.
AB - This Research to Practice Full Paper presents our Non-intrusive Classroom Attention Tracking System (NiCATS) and discusses the data collected through it. Academic instructors and institutions desire the ability to accurately and autonomously measure students' attentiveness in the classroom. Generally, college departments use unreliable direct communication from students, observational sit-ins, and end-of-semester surveys to collect feedback regarding their courses. Each of these methods of collecting feedback is useful but does not provide automatic feedback regarding the pace and direction of lectures. It has been widely reported that attention levels during passive classroom lectures generally drop after about ten to thirty minutes and can be restored to normal levels with regular breaks, novel activities, mini-lectures, case studies, or videos. Tracking these 'drops' in attention can be crucial for the accurate timing of these change-ups in activities. This allows for maximal attention and a greater amount of deeply learned material. Autonomously collected data can also be used either in real-time or post-hoc to alter the design and presentation of lectures. Keeping track of student attention is vital to having confidence in delivering material. Even if lectures do not break up presentation slides with attention-raising activities, they can still show more important information during periods of high attention and less important information during periods of low attention. This area of research has applications both in in-person classrooms and online learning environments. The long-term goals of this research can prove invaluable for large in-person classrooms or classrooms where students' faces are obscured, such as behind computer monitors.
KW - AI
KW - Attention
KW - Education
KW - Engagement
KW - Eye Metrics
KW - Eyetracking
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85143812321&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143812321&partnerID=8YFLogxK
U2 - 10.1109/FIE56618.2022.9962447
DO - 10.1109/FIE56618.2022.9962447
M3 - Conference contribution
AN - SCOPUS:85143812321
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - 2022 IEEE Frontiers in Education Conference, FIE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Frontiers in Education Conference, FIE 2022
Y2 - 8 October 2022 through 11 October 2022
ER -