A cross-disciplinary academic team at Emporia State University is currently in the process of developing and utilizing a mobile-based augmented reality application in the context of library information search. Specifically, the team is researching the use of mobile applications that can generate multi-sensory information retrieval relative to archives and special collections. Using this application, student and faculty researchers can physically point their mobile devices at an archival object that has been specifically marked with a photo-generated “tag” and, using specially designed software, access videos, photos, music, text, and other data that is germane to the object. This allows the archivist to preserve the object behind protective glass or other physical barriers, while allowing the information seeker to learn more about the object using embedded multimedia. This minimizes the potential for damage while providing extra dimensions of information. Of the many virtualizations currently under development are videos related to a rare novel and music compositions relative to rare sheet music – both currently housed within Special Collections at Emporia State University.
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Information search has an interesting history – especially as it relates to libraries and user interactions when engaging with information and data retrieval systems. Aside from advances in the mechanics of information search and retrieval, several scholars have researched the evolving psychology of how users engage in information seeking strategies within libraries and media centers. Notable researchers in this field include Carol Kuhlthau who crafted the Information Search Process (ISP) model (2001); Brenda Dervin whose Sense Making Strategies are still widely used in information retrieval (1976); Marcia Bates who introduced Browsing and Berry Picking Techniques related to information search behaviors among library patrons (2005); The Big Six Information Literacy Process designed for school library media specialists by Michael Eisenberg and Bob Berkowitz; and Nick Belkin whose Anomalous States of Knowledge, also known as the ASK model, provided the construct for many contemporary search engines, including “ASK Jeeves” (1980). Emerging systems such as faceted search, voice-assisted information retrieval, QR Codes, and semantic-based search engines, including Wolfram Alpha, Hakia, Swoogle, Powerset and other similar systems promise enormous potential for retrieving information from the corpus (Radhakrishnon, 2009).
The two primary taxonomies related to information seeking strategies are known as Information Science and Information Retrieval. Information Science is a field of study that is primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval, movement, and dissemination of information. In short, this is the field of study most closely approximating traditional library science. Information Retrieval, on the other hand, is involved with obtaining information resources relevant to an information need and related to a collection or body of resources. This field of study most closely approximates Human-Computer Information Retrieval (HCIR) and human-factors psychology. For many years, these fields of study were considered completely independent of one another. Librarians were exposed to principals of Information Science in their graduate programs, whereas computer scientists were focused on the Information Retrieval systems and how information seekers extracted data through various interfaces and human-computer interaction systems.
However, these two fields of research and inquiry are beginning to converge. Increasingly, the field of Information Science is intersecting with the field of Information Retrieval. For example, the dissemination of information is often managed through an information retrieval system – usually a computer or mobile application of some sort. We see these systems each time we enter a library and ask personnel at the Reference Desk for search assistance. However, the majority of these systems (OCLC, WorldCat, LexisNexis, Academic Search Complete, etc.) remain primarily text-based. Queries must still be manually keyed using the correct syntax in order to retrieve a data set that is relative to the query.