Exploring Potential Contributions of Social Learning to Adaptive Learning Systems

Sanjana Gautam, Mary Beth Rosson, Mahir Akgun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationCHI 2023 - Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450394222
DOIs
StatePublished - Apr 19 2023
Event2023 CHI Conference on Human Factors in Computing Systems, CHI 2023 - Hamburg, Germany
Duration: Apr 23 2023Apr 28 2023

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
Country/TerritoryGermany
CityHamburg
Period4/23/234/28/23

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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