Exploring Potential Contributions of Social Learning to Adaptive Learning Systems

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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.

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Gautam, S., Rosson, M. B., & Akgun, M. (2023). Exploring Potential Contributions of Social Learning to Adaptive Learning Systems. In Conference on Human Factors in Computing Systems - Proceedings. Association for Computing Machinery. https://doi.org/10.1145/3544549.3585758

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