Pupils’ Performance within the Domain of Data Interpretation, with Particular Reference to Pattern Recognition

Abstract

The ability to read data in graphical forms, and to interpret them by looking for general patterns or trends is an important skill in children's science education. When viewed from a procedural basis, such skills become key areas of competence in science. The framework developed by the Assessment of Performance Unit (APU) recognised the importance of reading and interpreting data, but saw them as discrete aspects of ‘process’ science. However, secondary analysis of APU performance data suggests that this separation is not reflected in the way children respond to assessment items. Indeed, pupil performance on different components of the same data‐handling question is often very variable, indicating that these questions carry ‘hidden’ cognitive demands.