A recommendations-based reading list system prototype for learning and resource management

Chowdhury, Gobinda and Koya, Kushwanth and Bugaje, Maryam (2021) A recommendations-based reading list system prototype for learning and resource management. Journal of Information Science. pp. 1-16. ISSN 0165-5515

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    Abstract

    A reading list is a list of reading items recommended by an academic to assist students' acquisition of knowledge for a specific subject. Subsequently, the libraries of higher education institutions collect and assemble reading lists according to specific courses and offer the students the reading lists service. However, the reading list is created based on localised intelligence, restricted to the academic’s knowledge of their field, semantics, experience and awareness of developments. This investigation aims to present the views and comments of academics, and library staff, on an envisaged aggregated reading lists service, which aggregates recommended reading items from various higher education institutions. This being the aim, we build a prototype, which aggregates reading lists from different universities and showcase it to nineteen academics and library staff in various higher education institutions to capture their views, comments and any recommendations. In the process we also showcase the feasibility of collecting and aggregating reading lists, in addition to understanding the process of reading lists creation at their respective higher education institutions. The prototype successfully showcases the creation of ranked lists of reading items, authors, topics, modules and courses. Academics and library staff indicated that aggregated lists would collectively benefit the academic community. Consequently, recommendations in the form of process implementations and technological applications are made to overcome and successfully implement the proposed aggregated reading list service. This proof-of-concept demonstrates potential benefits for the academic community and identifies further challenges to overcome in order to scale it up to the implementation stage.