Proceedings of the International Group for the Psychology of Mathematics Education · Volume 1
Timo Kosiol, Marielena Menzel, Sebastian Geisler, Stefanie Rach
OVGU Magdeburg
⬇ Download PDFValidating is considered an important process for successful modelling. It includes critically evaluating and, if necessary, revising (intermediate) models or results during a modelling process. In an experimental study with N=561 upper secondary students, we compare validation processes for students modelling the decay of beer foam using real data (either self-collected or provided) with those for students modelling smoothed data. We examine whether student characteristics (motivation and content knowledge) or task characteristics (the type of data) predict validation processes. Results indicate that a model’s critical evaluation is predicted by content knowledge, while the formulation of suggestions for improvement is predicted by the type of data. We discuss the role of student and task characteristics to stimulate validation processes.
data-based modellingvalidatingreal datamotivationcontent knowledge