Jonathan M. Vitale

Direct manipulation is better than passive viewing for learning anatomy in a three-dimensional virtual reality environment

Abstract With the advancement of virtual reality (VR) technologies, medical students may now study complex anatomical structures in three-dimensional (3-D) virtual environments, without relying solely upon high cost, unsustainable cadavers or animal models. When coupled with a haptic input device, these systems support direct manipulation and exploration of the anatomical structures. Yet, prior studies provide inconclusive support for direct manipulation beyond passive viewing in virtual environments. In some cases, exposure to an “optimal view” appears to be the main source of learning gains, regardless of participants’ control of the system. In other cases, direct manipulation provides benefits beyond passive viewing. To address this issue, we compared medical students who either directly manipulated a virtual anatomical structure (inner ear) or passively viewed an interaction in a stereoscopic, 3-D environment. To ensure equal exposure to optimal views we utilized a yoked-pair design, such that for each participant who manipulated the structure a single matched participant viewed a recording of this interaction. Results indicate that participants in the manipulation group were more likely to successful generate (i.e., draw) the observed structures at posttest than the viewing group. Moreover, manipulation benefited students with low spatial ability more than students with high spatial ability. These results suggest that direct manipulation of the virtual environment facilitated embodiment of the anatomical structure and helped participants maintain a clear frame of reference while interacting, which particularly supported participants with low spatial ability.

Integrating intuitive and novel grounded concepts in a dynamic geometry learning environment

The development of geometry knowledge requires integration of intuitive and novel concepts. While instruction may take many representational forms we argue that grounding novel information in perception and action systems in the context of challenging activities will promote deeper learning. To facilitate learning we introduce a grounded integration pattern of instruction, focusing on (1) eliciting intuitive concepts, (2) introducing novel grounding metaphors, and (3) embedding challenges to promote distinguishing between ideas. To investigate this pattern we compared elementary school children in two conditions who engaged in variations of a computer-based dynamic geometry learning environment that was intended to elicit intuitive concepts of shapes. In the grounded integration condition children performed a procedure of explicitly identifying defining features of shapes (e.g. right angles) with the assistance of animated depictions of spatially-meaningful gestures (e.g. hands forming right angles). In a numerical integration condition children identified defining features with the assistance of a numerical representation. Children in the grounded integration were more likely to accurately identify target shapes in a posttest identification task. We discuss the relevancy of the grounded integration pattern on the development of instructional tools.

Distinguishing complex ideas about climate change: knowledge integration vs. specific guidance

ABSTRACT We compared two forms of automated guidance to support students’ understanding of climate change in an online inquiry science unit. For specific guidance, we directly communicated ideas that were missing or misrepresented in student responses. For knowledge integration guidance, we provided hints or suggestions to motivate learners to analyze features of their response and seek more information. We guided both student-constructed energy flow diagrams and short essays at total of five times across an approximately week-long curriculum unit. Our results indicate that while specific guidance typically produced larger accuracy gains on responses within the curriculum unit, knowledge integration guidance produced stronger outcomes on a novel essay at posttest. Closer analysis revealed an association between the time spent revisiting a visualization and posttest scores on this summary essay, only for those students in the knowledge integration condition. We discuss how these gains in knowledge integration extend laboratory results related to ‘desirable difficulties’ and show how autonomous inquiry can be fostered through automated guidance.

Designing Automated Guidance to Promote Productive Revision of Science Explanations

Supporting students to revise their written explanations in science can help students to integrate disparate ideas and develop a coherent, generative account of complex scientific topics. Using natural language processing to analyze student written work, we compare forms of automated guidance designed to motivate productive revision and help students integrate their understanding of science. Research shows the benefit of providing timely, transparent guidance to students and identifies some challenges. Specifically, (a) students often believe online guidance is generic rather than adapted to their response; and (b) students do not always engage effortfully with online guidance to improve their written responses. We conducted two studies to address these challenges. In Study 1, we created transparent guidance that clarified how the computer personalizes guidance based on the student response. We hypothesized that transparent guidance would be especially valuable for low prior knowledge students who might expect the computer guidance to be too difficult. We found that transparent guidance had a greater impact than typical guidance on low prior knowledge student revisions, suggesting that student beliefs about how guidance is designed influence their performance. In Study 2, implemented in six schools, we compared two specific guidance strategies: revisiting evidence and planning writing changes. We found that both revisiting and planning guidance resulted in significant improvement in student knowledge integration, although neither guidance strategy showed a significant advantage over the other. In addition, we found that the form of guidance interacted with school, suggesting that teacher practices could reinforce a specific guidance strategy. These results illustrate ways to design guidance to strengthen student understanding of science. They raise important questions about when to encourage revisiting, how to design instruction focused on planning, and how to instill a lifelong practice of engaging in iterative refinement of scientific explanations.

Predicting Student Learning using Log Data from Interactive Simulations on Climate Change

Interactive simulations are commonly used tools in technology enhanced education. Simulations can be a powerful tool for allowing students to engage in inquiry, especially in science disciplines. They can help students develop an understanding of complex science phenomena in which multiple variables are at play. Developing models for complex domains, like climate science, is important for learning. Equally important, though, is understanding how students use these simulations. Finding use patterns that lead to learning will allow us to develop better guidance for students who struggle to extract the useful information from the simulation. In this study, we generate features from action log data collected while students interacted with simulations on climate change. We seek to understand what types of features are important for student learning by using regression models to map features onto learning outcomes.

Designing Virtual Laboratories to Foster Knowledge Integration: Buoyancy and Density

In this chapter, we report upon the iterative development of an online instructional unit featuring virtual laboratory activities that target the physical science concepts of density and buoyancy. We introduce a virtual laboratory activity that was designed to facilitate exploration of the relationship of mass and volume to buoyancy. We evaluate the virtual laboratory by measuring the extent to which it fosters meaningful experimentation, appropriate interpretation of evidence, and discovery of new ideas. In the first revision, we simplified the exploratory tools. This revision supported better interpretation of evidence related to a specific claim, but limiting potential for discovery of new ideas. In the second revision, we introduced an intuitive graph-based interface that allowed students to specify and rapidly test properties of virtual materials (i.e., mass and volume). This revision facilitated meaningful exploration of students’ ideas, thereby supporting both valid interpretations of evidence related to false claims and discovery of new ideas. We discuss the role that virtual laboratories can play in the design of all laboratory activities by tracking student strategies and offering opportunities to easily test new features.