Leveraging AI and network analysis to uncover learning trajectories of energy to Foster knowledge-in-use in science education

Abstract Science education aims to foster knowledge-in-use, which is supported by the integration of scientific ideas. To study knowledge integration effectively, network analysis provides a valuable tool for visualizing and understanding how ideas are connected. Successful knowledge integration requires following a learning progression that leads to increasingly sophisticated connections between ideas. However, traditional learning progression models have limitations, as they often fail to account for the nonlinear and individualized nature of learning. This study explores the potential of digital learning environments and AI techniques to address these limitations by enabling frequent, high-resolution data collection and analysis in order to uncover individual students’ learning trajectories at a high resolution. We analyze a case study of middle school students’ learning about energy to investigate patterns and variations in their learning trajectories. Additionally, we explore how different learning trajectories influence the development of knowledge-in-use, leading to either productive or unproductive learning outcomes. Our findings aim to guide instruction for teachers and instructional designers, providing insights on how to develop more effectively adaptive learning environments that support diverse student learning trajectories.