TY - GEN
T1 - Exploring Potential Contributions of Social Learning to Adaptive Learning Systems
AU - Gautam, Sanjana
AU - Rosson, Mary Beth
AU - Akgun, Mahir
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/4/19
Y1 - 2023/4/19
N2 - Adaptive learning systems aim to emulate how skilled educators seek to provide every student the best possible learning experience. We investigate how such systems might be enriched by activities and indicators of social learning - an aspect of learning that focuses on the influences of learners' social context and interactions. In this paper we describe a pilot study aimed at exploring the potential for including social learning in an adaptive system. Our analysis of the social learning scale demonstrates its validity and usefulness for our ongoing work. Our qualitative analysis of students' learning demonstrates how social learning vary among students. We discuss how the rating scale results and observations of social learning can be integrated within a student model needed to drive an adaptive system. More generally, our work illustrates how theories of learning can contribute to the design of adaptive learning systems.
AB - Adaptive learning systems aim to emulate how skilled educators seek to provide every student the best possible learning experience. We investigate how such systems might be enriched by activities and indicators of social learning - an aspect of learning that focuses on the influences of learners' social context and interactions. In this paper we describe a pilot study aimed at exploring the potential for including social learning in an adaptive system. Our analysis of the social learning scale demonstrates its validity and usefulness for our ongoing work. Our qualitative analysis of students' learning demonstrates how social learning vary among students. We discuss how the rating scale results and observations of social learning can be integrated within a student model needed to drive an adaptive system. More generally, our work illustrates how theories of learning can contribute to the design of adaptive learning systems.
UR - http://www.scopus.com/inward/record.url?scp=85158073501&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85158073501&partnerID=8YFLogxK
U2 - 10.1145/3544549.3585758
DO - 10.1145/3544549.3585758
M3 - Conference contribution
AN - SCOPUS:85158073501
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2023 - Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
Y2 - 23 April 2023 through 28 April 2023
ER -