1998 Conference Attendees

The virtual reality interface has the potential to complement existing approaches to science instruction. By themselves becoming part of a phenomenon (e.g., a student becomes a point-mass undergoing collisions in an immersive virtual environment without gravity or friction), learners gain direct experience from which intuitions form about how the natural world operates. In particular, good instructional design can make those aspects of virtual environments that are useful in understanding scientific principles salient to learners' senses. For example, in two-dimensional Newtonian microworlds students often ignore objects' velocities, instead focusing on position. In our comparable immersive environment, NewtonWorld, learners "inside" a moving object are themselves moving; this three-dimensional, personalized frame of reference centers attention on velocity as a variable. In NewtonWorld, we heightened this saliency by using multisensory cues to convey multiple, simultaneous representations of relative speeds. As another example of the power of "perceptualization," learners who struggled with the concepts underlying our vector-field-based immersive environment, MaxwellWorld, reported that representations providing redundant data simultaneously through visual, auditory, and haptic stimuli aided their comprehension. Transducing data and abstract concepts (e.g., energy) into mutually reinforcing multisensory representations may be an important means of enhancing understanding of scientific models.

In addition, researchers are documenting that the social construction of knowledge among students in a shared, text-based virtual environment enables innovative, powerful types of collaborative learning. Adding immersive, multisensory representations to these textual "worlds" could potentially increase communicative and educational effectiveness. Overall, we believe that various aspects of multisensory immersion, when applied to scientific models, can provide learners with experiential metaphors and analogies that (1) aid in understanding complex phenomena remote from their everyday experience (e.g., relativity, quantum mechanics) and (2) help in displacing "common sense" misconceptions with alternative, more accurate mental models.

The tension between computer-based modeling activities versus real-world observation and laboratory experimentation.

The debate: In interacting with a model, learners are manipulating a representation of reality, one that can simplify complex scientific concepts and their interrelationships. However, unless carefully designed, models can oversimplify reality in a manner that later makes deeper understanding of phenomena harder to attain. Still, models that go beyond simulation to allow learners to change underlying variables and relationships--to illustrate how an idealized phenomenon functions by altering it in ways not possible in reality--can enable a kind of meta-understanding not possible via real world experimentation. Yet real world phenomena are more "real" to learners--more believable, more fully sensory. On the other hand, some complex scientific concepts (e.g., relativity, quantum mechanics) involve intangible phenomena unobservable in the everyday macroscopic settings to which learners have access. For these types of content, models are the only means by which students form non-abstract impressions of these phenomena. Given these relative strengths and limits, what should be the pedagogical balance between interacting with models and experiencing reality itself?

Our contribution: Models based on multisensory immersion give learners experiences closer to the perceptual aspects of reality than any other simulation medium. Our research suggests that virtual reality is a potentially powerful means of bridging the gap between models and real world experimentation through combining strengths of each: the sensorial, immersive involvement of real world experiences and the emphasis on crucial variables for understanding that models can provide (in our work, through perceptual saliency). In our research so far, we have not found that carefully designed "almost real" models induce new types of learner misconceptions. However, we do believe transitional learning experiences that gradually remove the affordances of models to reveal the full complexity and confusion of reality are important for generalizability and transferability of learning. The best pedagogical strategy may involve beginning with real world experiments to show the complexity and counter-intuitive nature of phenomena, then using models to simplify the situation and to enhance comprehension via interactive representations, and finally combining and extending the models to show how the complexity of real world behavior emerges from a multiplicity of simultaneous underlying causes.

The tension between modeling in science research versus modeling in science education.

The debate: This issue concerns the differences between modeling by experts and modeling by novices, in particular between the modeling tools used by scientists and those used by precollege students. Some researchers claim that, under the guidance of professionals, typical students (especially at the secondary school level) can learn scientific concepts by using the same models and supercomputing facilities used by research scientists. Others insist that all but the brightest high-school students need specially designed modeling tools and applications to introduce them to model-based inquiry.

Our contribution: In our design of representations for virtual reality, we have noted that part of the difficulty in mastering complex scientific concepts is the misleading representational formalisms and terminology that have emerged historically in science and that are now entrenched as standard professional notation. Students come to us with misconceptions that appear to be linked to these traditional representations. We find that, despite our best efforts to compensate for the shortcomings of these formalisms, students sometimes remain confused about how to relate conventional representations to reality and how to use standard scientific terminology to convey their ideas. Two examples from electrostatics illustrate this point.

First, from their prior physics instruction, many of our learners in MaxwellWorld have initial misconceptions linked to the "field line" representation. For experts, field lines are a quick way of ascertaining the direction of a vector field along a series of points. However, novices understandably develop several intuitive misconceptions through analogical reasoning: field lines illustrate the path an untethered test charge would take through the field, the force does not vary from point to point along the field line, field lines can cross, etc. Additionally, learners often have difficulties relating field lines to another common representation of force: test charge traces. In MaxwellWorld, we attempt to overcome the shortcomings of the traditional field line representation by adding several enhancements. First, field lines are colored according to the strength of the force along them, helping students visualize how the force varies from point to point. Second, our "enhanced" field lines can be continuously manipulated in 3-D. By grabbing a point on a field line and moving it, students can see how characteristics of the field line (both the shape and the strength of the field along it) change from point to point, and they can verify that field lines will never cross. Finally, by releasing a test charge on a field line, learners can see that the test charge moves along the field line only when the line does not curve.

Second, another example of a problematic representation is the "equipotential surface," which indicates a set of points across which a test charge's electric potential (or energy) would remain constant. In 2-D, this surface appears to be a line, creating difficulties for students in distinguishing equipotential surfaces from field lines. Further, the standard formalism for equipotential surfaces does not convey information about the magnitude of the surface's potential. In addition, this representation does not aid students in relating the concepts of potential and force on the surface (this is also a problem with field lines). Consequently, students have trouble remembering which representation tells them about electric field (or force) and which tells them about electric potential (or energy). For example, we have observed a number of students describing field lines when asked to describe equipotential surfaces and vice versa. At a deeper level, students have trouble distinguishing the concept of electric field (or force) from electric potential (or energy). For example, when students are asked whether the force on a test charge would vary or remain constant as they move it along an equipotential surface in a complex field, they most commonly predict that it will be constant. We have enhanced the equipotential surfaces displayed in MaxwellWorld to attempt to compensate for these shortcomings of the standard formalism.

In general, these traditional scientific representations share one thing in common: they fail to make salient information that may be obvious to the expert, but not to the novice. The missing data often is crucial in providing the foundation for understanding how these models represent reality. Our approach has been to enhance traditional representations, adding new information and affording an investigation of the interrelationships among them. However, we have sometimes found ourselves limited in the extent to which we can build on this version.