Data-Driven Support Infrastructure for Iterative Team-Based Learning

Abstract

Iterative team-based learning (TBL) is a common educational strategy for collaborative learning that involves sequential phases of individual and group learning activities. The advent of digital learning platforms, with the accumulation of learning log data, presents an opportunity to leverage data-driven techniques to enhance TBL practices. However, applying data-driven approaches in iterative TBL scenarios has received limited exploration in existing literature. Through a review of initial studies in this domain, data-driven iterative TBL emerges as a promising area. To explore this topic, we introduce a novel framework, drawing from the GLOBE framework for group learning, aimed at integrating data-driven designs into iterative TBL settings. This framework is proposed to guide data and activity design within iterative TBL, comprising four phases of group learning activity workflow and three essential steps of data flow. Additionally, we present two authentic instances supported by empirical evidence, offering insights into how educators can implement data-driven designs across different phases of TBL. Within the data-driven environment, we also uncover potential impacts and challenges of data-driven iterative TBL, to identify avenues for future research that can further expand our understanding of the possibilities in this domain.

Keywords

• Team-based learning (TBL)
• collaborative learning
• group formation
• peer evaluation
• rater reliability
• data-driven support