This study investigated novel pedagogies for helping teachers infuse inquiry into a standards‐based science curriculum. Using a multi‐user virtual environment (MUVE) as a pedagogical vehicle, teams of middle‐school students collaboratively solved problems around disease in a virtual town called River City. The students interacted with ‘avatars’ of other students, digital artefacts and computer‐based ‘agents’ acting as mentors and colleagues in a virtual community of practice set during the time period when bacteria were just being discovered. This paper describes the results from three implementations of the River City virtual environment in 2004–05 with approximately 2000 students from geographically diverse urban areas. The results indicated that students were able to conduct inquiry in virtual worlds and were motivated by that process. However, the results from the assessments varied depending on the assessment strategy employed.
Abstract:One-size-fits-all educational innovations do not work because they ignore contextual factors that determine an intervention's efficacy in a particular local situation. Identifying variables within the intervention's setting that represent important conditions for success and summarizing the extent to which the impact of the intervention is attenuated by variation in them can provide prospective adopters of the innovation a better sense of what level of effectiveness they are likely to enjoy in their own particular circumstances. This study presents a research framework on how to conduct such an analysis and how to design educational innovations for scalability through enhancing their adaptability for effective usage in a wide variety of settings. The River City MUVE, a technology-based curriculum designed to enhance engagement and learning in middle school science, is presented as a case study.
Key discussion points or questions for session:• What other conditions of success are important in technology innovations?• What types of research studies will help to determine the full range of conditions for success? • What are some strategies for collecting contextual data and determining conditions for success across various projects? • What are other heuristics for robust design?• As a field, how can we share heuristics for robust design and scalability?
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