Our comparative studies investigate the influence of different representations (i.e. formulas or graphical models and numeric formats) on the understanding of "big ideas" in stochastics (e.g. characteristics of probability, conditional probability, distribution, significance). We know from previous work (e.g. Sedlmeier & Gigerenzer, 2001) that special tree-representations combined with frequency-formats increase the performance in understanding dramatically. Another aspect of the experiments is the utility of different presenting-modes (e.g. static vs. dynamic, imitation vs. learning by doing). The pupils of age 15-19 receive a computer-based training with different representations resp. modes on basic probability tasks. The effects of the training are measured by subsequent tests. Thus we obtain insight, if they succeed easily in using the learned representations and if they benefit from it. The first results support the assumption that groups of pupils trained with frequency-representations have a better understanding of key-problems in stochastics.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education