Information overload and online collaborative learning: insights from agent-based modeling

Abstract

This paper investigates information overload (IO) in large online courses by developing an Agent-based Model (ABM) of student interaction in a computer-supported collaborative learning (CSCL) environment. Student surveys provided ABM model parameters, and experimental results suggest unique visitor count to be a superior metric than user activity level for IO detection. ABM of synchronous/asynchronous platforms demonstrates how additional channels can be introduced to effectively combat IO. As work in progress, we look forward to validating model recommendations with activity data in online classrooms.