No abstract

Producing content‐related gestures has been found to impact students’ learning, whether such gestures are spontaneously generated by the learner in the course of problem‐solving, or participants are instructed to pose based on experimenter instructions during problem‐solving and word learning. Few studies, however, have investigated the effect of (a) performing instructed gestures while learning concepts or (b) producing gestures without there being an implied connection between the gestures and the concepts being learned. The two studies reported here investigate the impact of instructed hand movements on students’ subsequent understanding of a concept. Students were asked to watch an instructional video—focused on the concept of statistical model—three times. Two experimental groups were given a secondary task to perform while watching the video, which involved moving their hands to mimic the placement and orientation of red rectangular bars overlaid on the video. Students were told that the focus of the study was multitasking, and that the instructed hand movements were unrelated to the material being learned. In the content‐match group the placement of the hands reinforced the concept being explained, and in the content‐mismatch group it did not. A control group was not asked to perform a secondary task. In both studies, findings indicate that students in the content‐match group performed better on the posttest, and showed less variation in performance, than did students in the content‐mismatch group, with control students falling in between. Instructed hand movement—even when presented as an unrelated, secondary task—can affect students’ learning of a complex concept.

Using multiple representations is an important part of learning and problem-solving in science, technology, engineering and mathematics fields. For students to acquire flexible knowledge of representations, they must attend to the structural information within each representation and practice making relational connections between representations. Most studies so far have only attempted to help students connect between multiple representations in the lab or short-term classroom interventions, with the intervention largely separated from students' authentic learning. The present study developed a representation-mapping intervention designed to help students interpret, coordinate, and eventually translate across multiple representations. We integrated the intervention into an online textbook being used in a college course, allowing us to study its impact in a real course over an extended period of time. The findings of this study support the efficacy of the representation-mapping intervention for facilitating learning and shed light on how to implement and refine such interventions in authentic learning contexts. Public Significance StatementThe study advances the idea that explicit representation-mapping can facilitate students' learning and transfer of statistics concepts. The findings provide important insights into college students' real learning behaviors and outcomes in an online environment. The method used in this study also guides the implementation of future theory-based interventions in authentic learning contexts.

Statistical modeling is typically seen as an advanced skill and rarely introduced to students at the introductory level. But in data science and its applications (e.g., public health, politics), modeling is a necessary component of data literacy. To address this need, we should teach introductory students modeling from the beginning, connecting the content to the modern world of data science. This may have the added benefit of bringing coherence to statistics. We teach this content using research-based pedagogy – the practicing connections framework – and utilize new technology (CourseKata.org) capable of conducting experiments to continuously improve instruction. We are thus able to test theories of how students learn difficult, time-consuming concepts, such as the concept of a statistical model. Together, the curriculum, pedagogical theory, and technology provide a process to make the book incrementally better at producing a modern, coherent, and flexible understanding of statistics.

No abstract