Paper Info
Title
Sensing Affect to Empower Students: Learner Perspectives on Affect-Sensitive Technology in Large Educational Contexts
Abstract
Large-scale educational settings have been common domains for affect detection and recognition research. Most research emphasizes improvements in the accuracy of affect measurement to enhance instructors' efficiency in managing large numbers of students. However, these technologies are not designed from students' perspectives, nor designed for students' own usage. To identify the unique design considerations for affect sensors that consider student capacities and challenges, and explore the potential of affect sensors to support students' self-learning, we conducted semi-structured interviews and surveys with both online students and on-campus students enrolled in large in-person classes. Drawing on these studies we: (a) propose using affect data to support students' self-regulated learning behaviors through a "scaling for empowerment'' design perspective, (b) identify design guidelines to mitigate students' concerns regarding the use of affect data at scale, (c) provide design recommendations for the physical design of affect sensors for large educational settings.
Year
DOI
Venue
2020
10.1145/3386527.3405917
[email protected] '20: Seventh (2020) ACM Conference on Learning @ Scale Virtual Event USA August, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7951-9
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Qiaosi Wang112.05
Shan Jing211.38
David Joyner398.40
Lauren Wilcox4164.73
Hong Li516037.11
Thomas Ploetz6102074.42
Betsy DiSalvo79516.10