|Title||Teaching critical thinking|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Holmes, NG, Wieman, CE, Bonn, DA|
Understanding and thinking critically about scientific evidence is a crucial skill in the modern world. We present a simple learning framework that employs cycles of decisions about making and acting on quantitative comparisons between datasets or data and models. With opportunities to improve the data or models, this structure is appropriate for use in any data-driven science-learning setting. This structure led to significant and sustained improvement in students' critical thinking behaviors, compared with a control group, with effects far beyond that of statistical significance. The ability to make decisions based on data, with its inherent uncertainties and variability, is a complex and vital skill in the modern world. The need for such quantitative critical thinking occurs in many different contexts, and although it is an important goal of education, that goal is seldom being achieved. We argue that the key element for developing this ability is repeated practice in making decisions based on data, with feedback on those decisions. We demonstrate a structure for providing suitable practice that can be applied in any instructional setting that involves the acquisition of data and relating that data to scientific models. This study reports the results of applying that structure in an introductory physics laboratory course. Students in an experimental condition were repeatedly instructed to make and act on quantitative comparisons between datasets, and between data and models, an approach that is common to all science disciplines. These instructions were slowly faded across the course. After the instructions had been removed, students in the experimental condition were 12 times more likely to spontaneously propose or make changes to improve their experimental methods than a control group, who performed traditional experimental activities. The students in the experimental condition were also four times more likely to identify and explain a limitation of a physical model using their data. Students in the experimental condition also showed much more sophisticated reasoning about their data. These differences between the groups were seen to persist into a subsequent course taken the following year.