TY - GEN
T1 - Classifying and visualizing students' cognitive engagement in course readings
AU - Yogev, Eran
AU - Gal, Kobi
AU - Karger, David
AU - Facciotti, Marc T.
AU - Igo, Michele
N1 - Publisher Copyright: © 2017 Association for Computing Machinery. All rights reserved.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - Reading material has been part of course teaching for centuries, but until recently students' engagement with that reading, and its effect on their learning, has been difficult for teachers to assess. In this article, we explore the idea of examining cognitive engagement-a measure of how deeply a student is thinking about course material, which has been shown to correlate with learning gains-as it varies over different sections of the course reading material. We show that a combination of automatic classification and visualization of cognitive engagement anchored in the text can give teachers-and not only researchers-valuable insight into their students' thinking, suggesting ways to modify their lectures and their course readings to improve learning. We demonstrate this approach with analyzing students' comments in two different courses (Physics and Biology) using the Nota Bene annotation platform.
AB - Reading material has been part of course teaching for centuries, but until recently students' engagement with that reading, and its effect on their learning, has been difficult for teachers to assess. In this article, we explore the idea of examining cognitive engagement-a measure of how deeply a student is thinking about course material, which has been shown to correlate with learning gains-as it varies over different sections of the course reading material. We show that a combination of automatic classification and visualization of cognitive engagement anchored in the text can give teachers-and not only researchers-valuable insight into their students' thinking, suggesting ways to modify their lectures and their course readings to improve learning. We demonstrate this approach with analyzing students' comments in two different courses (Physics and Biology) using the Nota Bene annotation platform.
UR - http://www.scopus.com/inward/record.url?scp=85051485390&partnerID=8YFLogxK
U2 - 10.1145/3231644.3231648
DO - 10.1145/3231644.3231648
M3 - Conference contribution
T3 - Proceedings of the 5th Annual ACM Conference on Learning at Scale, L at S 2018
BT - Proceedings of the 5th Annual ACM Conference on Learning at Scale, L at S 2018
T2 - 5th Annual ACM Conference on Learning at Scale, L at S 2018
Y2 - 26 June 2018 through 28 June 2018
ER -
