Abstract

Ask Dr. Discovery is an NSF-funded study addressing the need for ongoing, large-scale museum evaluation while investigating new ways to encourage museum visitors to engage deeply with museum content. To realize these aims, we are developing and implementing a mobile app with two parts: (1) a front-end virtual scientist called Dr. Discovery (Dr. D) for use by museum visitors that doubles as an unobtrusive data-gatherer and (2) a back-end analytics portal to be mined by museum staff, evaluators, and researchers. With the aid of our museum partners, we are developing this app to function as a platform for STEM informal education, research, and data-driven decision-making by museum staff. The Dr. D app has been designed to engage museum visitors, while connecting with an analytic system to make sense, in real time, of the large amounts of data produced by visitors’ use of the app. The analytic system helps museum staff access and interpret ongoing evaluation data, regardless of experience or museum resources, informing the practice of professionals at the front lines of informal STEM education in diverse communities. The design of the Dr. D app incorporates open-source analytic tools that make the gathering and interpretation of contextual information from visitors’ app use accessible to museum staff and educators, building their capacity for using data in their day-to-day work. The same tools are being used by our research team to probe questions about informal learning and motivation, effective application of large datasets for museum evaluation, and ways to encourage and understand use of mobile virtual experiences. In this paper, we describe our theory-based design of the Dr. D app and data analytics and describe findings from initial user testing with our museum partners.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalTechnology, Knowledge and Learning
DOIs
StateAccepted/In press - Mar 20 2017

Fingerprint

Museums
Data mining
museum
Data Mining
Real-time
Evaluation
Application programs
evaluation
staff
Design
time
Education
informal learning
Data-driven
Large Data Sets
Open Source
education
experience
data analysis
Probe

Keywords

  • Data-mining
  • Informal STEM learning
  • Instructional design
  • Museum evaluation

ASJC Scopus subject areas

  • Mathematics (miscellaneous)
  • Education
  • Human-Computer Interaction
  • Computer Science Applications

Cite this

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