Gleaning museum visitors’ behaviors by analyzing questions asked in a mobile app

Luis E. Pérez Cortés, Jesse Ha, Man Su, Brian Nelson, Catherine Bowman, Judd Bowman

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This study explores the feasibility of forming detailed inferences about museum visitor behavior based on analysis of data collected via Dr. Discovery—a mobile question-and-answer app. We analyzed 5656 questions asked by 795 visitor groups recorded by Dr. Discovery across two museums in the American Southwest. Analysis of this data supported the act of intuiting visitor movement through museum exhibit halls without the use of costly tracking or location technology by leveraging question keyword content, knowledge of exhibit hall layout, and question timestamp information. Additionally, data on question topic frequency enabled us to infer visitor engagement levels with specific exhibit hall content. We conclude that analysis of seemingly limited app-based data carries implications for the practice of museum evaluation since evaluators can gain evidence-based insight into visitor behaviors as well as illustrate helpful and promising technology-supported alternatives for conducting affordable, dependable, and scalable evaluations.

Original languageEnglish (US)
Pages (from-to)1209-1231
Number of pages23
JournalEducational Technology Research and Development
Issue number3
StatePublished - Jun 2023


  • App-based data collection
  • Data-driven behavioral analysis
  • Museum evaluation

ASJC Scopus subject areas

  • Education


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