Interactive computer simulations are powerful tools for (physics) instruction. Features that make them effective for learning include interactivity and direct feedback, the use of multiple representations, reducing complexity to focus on key ideas, and allowing students to easily change a wide range of parameters to explore relationships between quantities (Podolefsky et al, 2010).
We are specifically interested in the visualization aspect of interactive simulations. The type of visual representation can have a substantial impact on student thinking (Kohnle et al, 2014). They can facilitate the development of productive mental models, but may obstruct learning if the perceptual features are not aligned with the intended meaning (Chen&Gladding, 2014). In addition, students may have difficulties interpreting the visual information. The framing of simulations can also have a substantial impact on students’ interactions and learning success (Adams et al, 2008). From our own practice, we have anecdotal evidence that the manner of using simulations is not based on good pedagogic research, of which there is very little.
This raises a number of more practical questions: “How are students interpreting the visual information in the simulations? What are students actually learning compared with the intended learning goals? What are key factors in simulation use that impact learning? How are students building mental models by interacting with the simulations?”, which we are actively investigating as part of an Institute of Physics funded project.
In this contribution we will describe a project where we have investigated the effectiveness of sketching when working with interactive simulations. We carried out a multi-institutional study using two simulations from the QuVis project (Kohnle, 2016). We designed two sets of activities framing each simulation. One is text based, and the other visual. Students were assigned one of the activities, and worked on these either as a homework assignment, a computer classroom activity or an individual volunteer session with full screen capture and audio recording, including interview questions. We also collected data on students’ perceptions of the activities, observation notes, students’ written activity responses and a multiple choice pre- and post-test that can be used to assess learning gains.
We will present an initial analysis of the data. This will include measures of student learning as well as student engagement, and the effectiveness of different routes through the material. Initial indications are that there are interesting differences between the two ways of framing the simulations. We will present a combination of quantitative (learning gains) and qualitative measures (interview analysis).
Podolefsky, N.S., K. K. Perkins and W. K. Adams (2010), Phys. Rev. ST PER 6, 020117
Kohnle, A., C. Bailey and S. Ruby (2014), 2014 PERC Proceedings. 139
Chen, Z and Gladding, G., 2014. How to make a good animation: A grounded cognition model of how visual representation design affects the construction of abstract physics knowledge. Phys. Rev. ST PER 10, 010111
Adams, W.K., Paulson, A. and Wieman, C.E., 2008. What Levels of Guidance Promote Engaged Exploration with Interactive Simulations? 2008 PERC Proceedings, 59
Kohnle. A., 2016. QuVis. http://www.st-andrews.ac.uk/physics/quvis/. [Accessed 29 March 16].