The purpose of this paper is to report results of a formative usability study that investigated first-year student use of an optical character recognition (OCR) mobile application (app) designed to help students find resources for course assignments. The app uses textual content from the assignment sheet to suggest relevant library resources of which students may not be aware.
Formative evaluation data are collected to inform the production level version of the mobile application and to understand student use models and requirements for OCR software in mobile applications.
Mobile OCR apps are helpful for undergraduate students searching known titles of books, general subject areas or searching for help guide content developed by the library. The results section details how student feedback shaped the next iteration of the app for integration as a Minrva module.
This usability paper is not a large-scale quantitative study, but seeks to provide deep qualitative research data for the specific mobile interface studied, the Text-shot prototype.
The OCR application is designed to help students learn about availability of library resources based on scanning (e.g. taking a picture, or “Text-shot”) of an assignment sheet, a course syllabus or other course-related handouts.
This study contributes a new area of application development for libraries, with research methods that are useful for other mobile development studies.
The author acknowledges the Campus Research Board of the University of Illinois at Urbana-Champaign and the University of Illinois Library Research and Publications Committee, which provided support for the completion of this research. Many thanks to Chris Diaz, Residency Librarian, Scholarly Communications and Collections, University of Iowa, for help with participant recruitment, observation and interviewing support in the user studies; Mayur Sadavarte, Graduate Student in Computer Science at the University of Illinois and Nate Ryckman, Graduate Student in Information Systems Management at Carnegie Mellon University for Optical Character recognition programming support; Yinan Zhang, PhD Candidate in Computer Science at the University of Illinois; Sherry (Mengxue) Zheng, Graduate Student in Computer Science, for help developing the search and suggestion functionality of the Deneb near-semantic index; Maria Lux, Graphic Designer, for laying out the polished recommendations and prototyping Text-shot integration as a Minrva module.
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