DATA SENSEMAKING: A THEORETICAL FRAMEWORK FOR SCIENTIFIC EXPLANATION AND KNOWLEDGE GENERATION

Sensemaking research has extensively examined how people construct explanations and generate knowledge, yet the role of data in these processes remains undertheorized. Data science education focuses largely on how learners construct and represent data, whereas science education often treats data as given, with insufficient attention to how data contributes to the development of scientific knowledge. This paper proposes a theoretical framework for Data Sensemaking as a process that centers the role of data in sensemaking and identifies four cognitive constructs that shape the process: schemas, anchors, frames, and hypotheses. Building on these conceptual foundations, the framework specifies five dimensions of Data Sensemaking, five associated developmental levels, and four instructional principles for science instruction. The paper concludes by examining the framework’s limitations and highlighting directions for future research.