Smart Data, Smarter Minds: Cognitive Load Theory and Data Collection

This presentation will focus on the critical role that Cognitive Load Theory plays in data collection and its interpretation. Data plays a crucial role in shaping educational decisions by providing insights into student progress, guiding instructional strategies, and allocating resources effectively. However, if misinterpreted or used without a clear understanding of what is being measured, data can lead to harmful consequences, such as reinforcing inequities, mislabeling students' abilities, or promoting ineffective interventions.

In 2017, Dylan Wiliam stated that ‘Cognitive Load Theory is the single most important thing for teachers to know’. There are three types of cognitive load: intrinsic, extraneous, and germane types. The following are examples from mathematics: Intrinsic load hinges on element interactivity: geometric shapes (low); solving equations (moderate); calculus (high).

Extraneous load is needless working memory strain, which can be reduced via strategies like worked examples and eliminating redundant information.

Germane load is the “good load”, as it directly affects learning. This is what we need to make sure to measure as we gather data on students. It is important for teachers to focus on measuring germane load—the productive cognitive effort that fosters learning—ensuring that assessments capture meaningful progress.