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  • × year_i:[2020 TO 2030}
  • × theme_ss:"Benutzerstudien"
  1. Zimmerman, N.: User study: implementation of OCLC FAST subject headings in the Lafayette digital repository (2023) 0.00 
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    Abstract
    Digital repository migrations present a periodic opportunity to assess metadata quality and to perform strategic enhancements. Lafayette College Libraries implemented OCLC FAST (Faceted Application of Subject Terminology) for its digital image collections as part of a migration from multiple repositories to a single one built on the Samvera Hyrax open-source framework. Application of FAST has normalized subject headings across dissimilar collections in a way that tremendously improves descriptive consistency for staff and discoverability for end users. However, the process of applying FAST headings was complicated by several features of in-scope metadata as well as gaps in available controlled subject authorities.
    Type
    a
  2. Sluis, F. van der; Broek, E.L. van den: Feedback beyond accuracy : using eye-tracking to detect comprehensibility and interest during reading (2023) 0.00 
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    Abstract
    Knowing what information a user wants is a paramount challenge to information science and technology. Implicit feedback is key to solving this challenge, as it allows information systems to learn about a user's needs and preferences. The available feedback, however, tends to be limited and its interpretation shows to be difficult. To tackle this challenge, we present a user study that explores whether tracking the eyes can unpack part of the complexity inherent to relevance and relevance decisions. The eye behavior of 30 participants reading 18 news articles was compared with their subjectively appraised comprehensibility and interest at a discourse level. Using linear regression models, the eye-tracking signal explained 49.93% (comprehensibility) and 30.41% (interest) of variance (p < .001). We conclude that eye behavior provides implicit feedback beyond accuracy that enables new forms of adaptation and interaction support for personalized information systems.
    Type
    a

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