Mary Hegarty - US grants

The University of California at Santa Barbara is developing a facility for computer based laboratory instruction in experimental psychology. Each work station has ancillary hardware which enables it to be used in specialized experimental exercises. The primary courses improved by this project are in upper level laboratory courses in the area of psychology known as Human Information Processing. Research in Human Information Processing is concerned with how humans perceive, learn, remember, and think about information. The three laboratory courses have the same general structure. Students work together in small groups to conduct a series of psychological experiments. As the course progresses, students take progressively more responsibility for the design of their experiments. The course concludes with students conducting an extended experimental project of their own design. Each of the three courses is focusing on a separate content area: one on memory, learning, and reasoning, one on attention and performance, and one on perception and psychophysics. In addition to teaching general laboratory techniques, a goal of these courses is to introduce students to the use of computers in modern research and data analysis. The ability to use computers to organize and analyze information is a necessary skill for many of today's professional jobs; therefore, it is an important part of the teaching mission of a university to acquaint students with the techniques available on personal workstations. For students who go in science, these courses are teaching students how to use of computers for data acquisition and analysis.

9815553
Montello
This award supports the PI, Daniel Montello, co-PI Mary Hegarthy, and a graduate student from the University of California at Santa Barbara as well as co-PI Barbara Tversky and a graduate student from Stanford University in a collaboration with Christian Freksa of the Department of Informatics and Cognitive Sciences at the University of Hamburg, Germany. The research focus is the description and explanation of mental processes and structures underlying behaviors such as navigation, spatial learning, and spatial language. The field of study is interdisciplinary and will involve geographers, psychologists, computer scientists, and linguists. The U.S. and German groups bring complementary capabilities to the enterprise. The German side specializes in formal and computational modeling and the U.S. group, in empirical human-subjects methods and theory. Each side's contribution will enhance the breadth of research of the other side, and will result in interactions between researchers in the several disciplines mentioned.

There is much excitement about the educational potential of interactive computer visualizations. Despite such optimism, however, we know relatively little about how learners interact with 3-D computer visualizations. Initial studies in medical education suggest that rather than augmenting cognition for all learners, 3-D interactive models may actually be disadvantageous to individuals with low spatial abilities.

This project will examine the effects of individual spatial abilities and visualization design factors on students' spatial comprehension, and use those results to improve the effectiveness of 3-D visualizations in medical education. The first objective of the project relates to the spatial abilities that have been shown to be important for developing 3-D mental models of human anatomy. We will explore the correlation between spatial ability and anatomy learning, and examine how this correlation is modulated by use of interactive computer visualizations and by domain knowledge. The second objective is to test the effectiveness of different aspects of computer visualizations (stereoscopic vs. monoscopic displays, active vs. passive control and haptic vs traditional interfaces) for learning anatomy. The third objective is to apply the resulting findings to medical education by developing and testing training programs that use the types of simulations that are shown to be most effective. The studies will use a combination of correlational and experimental methods to explore the relationships between individual differences and anatomy learning, and the effectiveness of different aspects of computer visualizations. Basic studies will initially be conducted with naive undergraduate students and later generalized to medical students.

The goal of the proposed research is to describe the interaction between visuo-spatial and analytical strategies during learning and problem solving in science. Recent studies have indicated that some scientific problems that had been assumed to require a spatial visualization strategy, in fact are often solved by analytic strategies. Therefore, the PIs propose a three-phase project to characterize the use of cognitive strategies during spatial problem solving in both general and organic chemistry. They aim to identify the differential use of visuo-spatial strategies and the availability of analytical strategies for scientific problem solving by students and instructors, individuals with high and low spatial ability, and men and women. They will also develop and evaluate methods of training these strategies. In Phase I of the project they will conduct task and protocol analyses of student and instructor problem solving in the domain of chemistry and in spatial ability measures. In Phase II, they will collect data on students spatial abilities, gender, and educational background to identify the determinants of strategy use in visuo-spatial problem solving, both generally and in the chemistry domain. In Phase III, they will train chemistry teaching assistants to teach alternative strategies to students in their class discussion sections. They will examine the effects of strategy training on problem solving by students of different levels of spatial abilities by assessing their strategies before an after instruction.

Organic chemists rely heavily on physical models of molecules, so it not surprising that models are also commonly employed to help teach organic chemistry. It has been assumed that when undergraduate students work with these models, ability to construct an internal representation of the molecular structures is enhanced. But few studies have examined this directly and empirically. In addition, virtual models (3-D computer visualizations) have replaced concrete models as powerful computers become available, inviting comparison between learning with real and virtual models. Researchers at the University of California-Santa Barbara and the University of Maryland will conduct a series studies to examine how undergraduate organic chemistry students use models to advance their understanding of molecular structures that require visualizations in three dimensions. This study investigates the benefits of students' use of concrete or virtual models, asking:
-Does model use improve students? ability to translate between different representations of molecular representations?
-What aspects of models are most effective for representation translation?
-Does instruction in model use alter the frequency and quality of model use?
-Do models support students of low spatial ability more or less effectively than students of high spatial ability?

This study uses experimental methods to systematically explore the uses of both concrete and virtual models in promoting meaningful learning in organic chemistry in a series of studies set in the controlled environment of the psychology lab. An important dependent variable for this study measures students' ability to translate between alternative diagrammatic representations of molecules, an essential skill that all students of organic chemistry must master. Measures include accuracy of the representations that students produce in representational translation and students' interactions (including gestures) with the concrete and virtual models during task performance. The study also tests its emergent theory of model use in university classroom settings by comparing the performance of undergraduates who are trained to use models with those who have had no such training.

This study is important because it systematically studies model use in organic chemistry. If students learn to use models more effectively, then they may find more success in undergraduate organic chemistry, a gatekeeper course for many advanced STEM professions. The ability to visualize molecules may be especially challenging for students with low spatial abilities, and the deliberate training in model use may allow more students to not only pass organic chemistry, but stimulate their ability to use models in eventual STEM professions. This study should result in a better understanding of the effectiveness and appropriateness of concrete and virtual models for enhancing student learning in chemistry and new instructional activities for use in chemistry classrooms.

The goal of this collaborative project (1212806, Ross T. Whitaker, University of Utah; 1212501, Donald H. House, Clemson University; 1212577, Mary Hegarty, University of California-Santa Barbara; 1212790, Michael K. Lindell, Texas A&M University Main Campus) is to establish the computational and cognitive foundations for capturing and conveying the uncertainty associated with predictive simulations, so that software tools for visualizing these forecasts can accurately and effectively present this information about to a wide range of users. Three demonstration applications are closely integrated into the research plan: one in air quality management, a second in wildfire hazard management, and a third in hurricane evacuation management. This project is the first large-scale effort to consider the visualization of uncertainty in a systematic, end-to-end manner, with the goal of developing a general set of principles as well as a set of tools for accurately and effectively conveying the appropriate level of uncertainties for a range of decision-making processes of national importance.

The primary impact of this work will be methods and tools for conveying the results of predictive simulations and their associated uncertainties, resulting in better informed public policy decisions in situations that rely on such forecasts. Scientific contributions are expected in the areas of simulation and uncertainty quantification, visualization, perception and cognition, and decision making in the presence of uncertainty. Results will be broadly disseminated in a variety of ways across a wide range of academic disciplines and application areas, and will be available at the project Web site (http://visunc.sci.utah.edu). The multidisciplinary nature of the research and the close integration of the participating research groups will provide a unique educational environment for graduate students and other trainees, while also broadening the participation in computer science beyond traditional boundaries.

Many strong claims have been made about the benefit of learning with physical and virtual (computer-based) models, especially in science, technology, engineering, and mathematics (STEM) disciplines. Chemistry is an ideal domain to investigate these issues because it is a spatially-rich domain that relies heavily on a variety of 2D (diagrams) and 3D (models) representations, and mastering these representations (i.e., developing representational competence) is challenging for students, but essential for their growth as chemists or scientists in other domains. A team of investigators from the University of California, Santa Barbara, will investigate (1) whether models might serve as a crutch or a scaffold for learning, (2) the extent to which interacting with models affects representational competence, (3) whether the ?medium? of the model (physical or virtual) is important in model-based learning, (4) which perceptual (i.e., visual and haptic) cues and cognitive factors contribute to meaningful learning, and (5) how gender and differences in spatial ability affect model-based learning. This project will yield research-based (a) practices for the development of curricula that integrate concrete and virtual models as effective learning aids and (b) principles for the design of meaningful model-based instruction.

The proposed project has the potential to inform teachers and curriculum designers about how better to use models and other representations in chemistry and other highly spatial STEM domains (e.g., geology, astronomy, anatomy, and mechanical engineering), by contributing to our basic understanding of what cognitive and perceptual factors affect learning. The knowledge gained from this project will inform the effective design and delivery of new media made possible by the explosive growth in availability of smart-phones and tablets in the classroom. Finally, results of this project will inform our understanding of how gender and differences in spatial ability contributes to learning with models in STEM disciplines.

A team of researchers from Northwestern University, the University of Illinois at Chicago, and the University of California - Santa Barbara will investigate spatial thinking in STEM fields. Students and scientists who are talented in STEM fields also tend to a high capacity for spatial imagination -- they score highly on tasks that ask them to imagine rotations of shapes, or predict how shapes will look when they are folded. But attempts to train these abilities have not translated to substantial improvement in STEM talents. This may be because current training focuses on rote practice, assuming that it is possible to improve the capacity of someone's spatial imagination. In contrast, this may be not possible -- even STEM experts may not have a substantially higher raw capacity for spatial imagination, compared to the average person. The research will test the exciting possibility that their available imagination 'machinery' is similar, but that experts have learned a set of strategies for using that same capacity far more efficiently. The studies will focus on the domain of chemistry, and will ask novices and experts to remember and transform objects that are both unfamiliar (abstract shapes) and familiar (molecules), in experiments designed to unpack the contributions of raw capacity versus a set of predicted strategies. If the studies can isolate the strategies that these STEM experts use to move beyond their capacity limits, then those strategies could be taught in chemistry classrooms. The same principles could extend to other domains as well, such as physics, geoscience, and algebra. This discovery would substantially enhance science and engineering education programs at all levels, strengthening the scientific and engineering research potential of our students. The project is funded by the EHR Core Research (ECR) program, which supports work that advances the fundamental research literature on STEM learning.

Success in STEM is correlated with spatial thinking ability, yet attempts to train spatial ability (e.g., with mental rotation or paper folding tasks) have led to little improvement in STEM outcomes. These spatial training programs may be ineffective because they are based on an impoverished model of the cognitive and visuospatial capacities processes underlying spatial thinking, both generally and in discipline-based education research. The present research will unpack spatial ability into three hypothesized mechanisms, to isolate where training might be best focused, using a set of controlled laboratory tasks that ask novices (undergraduates) and experts to encode and transform both unfamiliar/abstract and molecular stimuli. With chemistry as a case study, this project will unravel the relative contribution of three potential mechanisms for visuospatial representation and transformation: domain-specific chunking (using long-term memory representations of frequently-encountered chunks), domain-general compression skills (recognizing and leveraging redundancies such as repeated identities or planes of symmetry), and raw visuospatial capacity (the ability to store and transform any abstract set of points or shapes). A deeper understanding of the mechanisms involved in spatial thinking would lead directly to better pedagogy and curriculum design for teaching spatial thinking in kindergarten through undergraduate STEM classrooms.