SBIR Phase I: Student Assessment from Mobility Analysis in Virtual World Learning

This Small Business Innovation Research (SBIR) Phase I project will measure avatar mobility in an Internet virtual world learning environment to correlate with student educational psychology measures. The project seeks to develop learning experience modules in a virtual world for biology instruction and computer-assisted student assessment with minimal involvement by real world classroom instructors. The intellectual merit of this activity is to assess the extent that educational activities in a virtual environment mimic real world classroom behavior, and hence whether trace analysis of avatar mobility correlates with educational psychology measures of students. The learning modules to be developed can replace the need and expense of real world field trips, for instance, ecology plot surveys or island bio-geography studies. This would lower costs for the biology lab course, and allow the instructor to focus on individual assistance during the sessions. Many educational organizations are experiencing expanding enrollments and decreasing operational budgets in this economic recession. Efficiency and effectiveness are critical areas in managing public, private, and for-profit educational units. The project seeks to develop learning modules for biology instruction and computer-assisted student assessment with minimal overhead by teachers. The targets for these modules are high schools, undergraduate and technical school Biology programs that have associated labs for introductory, environmental, ecology, or genetics courses. The modules replace the need and expense of real world field trips, for instance, ecology, evolution or population growth studies. This project could have broad impact on computer- assisted learning in virtual world environments by providing databases of actual avatar traces. These mobility traces could then be compared with data from other Second Life experiences, simulated traces, or other mobility models such as cell phone, Bluetooth, or GPS traces to study social network models.