Towards general models of effective science inquiry in virtual performance assessments

Recent interest in online assessment of scientific inquiry has led to several new online systems that attempt to assess these skills, but producing models that detect when students are successfully practising these skills can be challenging. In this paper, we study models that assess student inquiry in an immersive virtual environment, where a student navigates an avatar around a world, speaking to in-game characters, collecting samples and conducting scientific tests with those samples in the virtual laboratory. To this goal, we leverage log file data from nearly 2000 middle school students using virtual performance assessment (VPA), a software system where students practice inquiry skills in different virtual scenarios. We develop models of student interaction within VPA that predict whether a student will successfully conduct scientific inquiry. Specifically, we identify behaviours that lead to distinguishing causal from non-causal factors to identify a correct final conclusion and to design a causal explanation about these conclusions. We then demonstrate that these models can be adapted with minimal effort between VPA scenarios. We conclude with discussions of how these models serve as a tool for better understanding scientific inquiry in virtual environments and as a platform for the future design of evidence-based interventions.