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This article is an introduction to the problem of interoperability in subject searching and browsing in distributed digital libraries. The aim is to provide brief advice of practical value to those tackling the problem themselves. A general overview of problem areas is given and some current projects tackling key issues are described.

The purpose of this paper is to propose a taxonomy of artificial intelligence (AI) literacy to support AI literacy education and research.

This study makes use of the facet analysis technique and draws upon various sources of data and information to develop a taxonomy of AI literacy. The research consists of the following key steps: a comprehensive review of the literature published on AI literacy research, an examination of well-known AI classification schemes and taxonomies, a review of prior research on data/information/digital literacy research and a qualitative and quantitative analysis of 1,031 metadata records on AI literacy publications. The KH Coder 3 software application was used to analyse metadata records from the Scopus multidisciplinary database.

A new taxonomy of AI literacy is proposed with 13 high-level facets and a list of specific subjects for each facet.

The proposed taxonomy may serve as a conceptual AI literacy framework to support the critical understanding, use, application and examination of AI-enhanced tools and technologies in various educational and organizational contexts.

The proposed taxonomy provides a knowledge organization and knowledge mapping structure to support curriculum development and the organization of digital information.

The proposed taxonomy provides a cross-disciplinary perspective of AI literacy. It can be used, adapted, modified or enhanced to accommodate education and learning opportunities and curricula in different domains, disciplines and subject areas.

The proposed AI literacy taxonomy offers a new and original conceptual framework that builds on a variety of different sources of data and integrates literature from various disciplines, including computing, information science, education and literacy research.

The purpose of this paper is to propose an evidence-based environmental scanning model that will provide a methodological framework for conducting community-engaged and community-focused research, with a particular emphasis on northern communities in Canada.

The study has adopted a multifaceted environmental scanning approach to understand the Inuvialuit Settlement Region communities. The research design is informed by various environmental models as discussed in literature from a broad range of domains such as business, library and information science (LIS), and a sophisticated multimethod data gathering approach that included field trips, observations, surveys, as well as informal methods of community engagement.

The paper proposes an environmental scan model as a novel approach to community-focused digital library (DL) development. The paper identifies both macro- and micro-environmental landscapes as applicable to the development of a DL for communities in Canada’s North. The macro-environmental landscapes include: geographical, historical and sociocultural, political and regulatory, economic, technological, competition, and human resource. The micro-environmental landscapes include: stakeholder and community, linguistic, information resource, and ownership.

The environmental scanning model and its key components presented in this paper provide a novel and concrete example of a project that aims to organize information for increased access and to create value through the design and implementation of an infrastructure for a cultural heritage DL. The environmental scan model will also contribute to both research and practice in the field of Library and Information Science (LIS), particularly in the area of DL development for rural, remote, and indigenous communities.

The purpose of this paper is to elaborate the long tail of topical search queries, including the influence of current events, posed to a large, urban public library discovery system.

Search queries from the months of June, July, August and September 2014 (1,488,339 total queries) were collected from the Edmonton Public Library’s BiblioCommons database using Google Analytics and exported to Excel. The data were then analyzed using descriptive statistics, frequency counts and textual analysis to explicate the long tail of search, (including the most popular searches) and to explore the relationship between topical search queries and current events.

The findings support the long tail theory, as the aggregate tail of topical search queries comprised the vast majority of the total searches and current events exert some influence on the nature and frequency of topical searches.

Data collection was limited to four months of the year; thus, comparisons across the year cannot be made. There are practical implications for public libraries in terms of marketing and collections, as well as for improving catalogue functionality, to support user search behaviour.

Not much research attention has been focused on the nature of topical search queries in public libraries compared to academic libraries and the Web. The findings contribute to developing insight into the divergent interests of intergenerational public library users and the topics of materials they are searching for.

Many higher education institutions are investigating the possibility of developing predictive student success models that use different sources of data available to identify students that might be at risk of failing a course or program. The purpose of this paper is to review the methodological components related to the predictive models that have been developed or currently implemented in learning analytics applications in higher education.

Literature review was completed in three stages. First, the authors conducted searches and collected related full-text documents using various search terms and keywords. Second, they developed inclusion and exclusion criteria to identify the most relevant citations for the purpose of the current review. Third, they reviewed each document from the final compiled bibliography and focused on identifying information that was needed to answer the research questions

In this review, the authors identify methodological strengths and weaknesses of current predictive learning analytics applications and provide the most up-to-date recommendations on predictive model development, use and evaluation. The review results can inform important future areas of research that could strengthen the development of predictive learning analytics for the purpose of generating valuable feedback to students to help them succeed in higher education.

This review provides an overview of the methodological considerations for researchers and practitioners who are planning to develop or currently in the process of developing predictive student success models in the context of higher education.

This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student performance in different courses? Which machine-learning classifiers tend to perform consistently well across different courses? Can the authors develop a general model for use in multiple courses to predict student performance based on LMS data?

Three mandatory undergraduate courses with large class sizes were selected from three different faculties at a large Western Canadian University, namely, faculties of science, engineering and education. Course-specific models for these three courses were built and compared using data from two semesters, one for model building and the other for generalizability testing.

The investigation has led the authors to conclude that it is not desirable to develop a general model in predicting course failure across variable courses. However, for the science course, the predictive model, which was built on data from one semester, was able to identify about 70% of students who failed the course and 70% of students who passed the course in another semester with only LMS data extracted from the first four weeks.

The results of this study are promising as they show the usability of LMS for early prediction of student course failure, which has the potential to provide students with timely feedback and support in higher education institutions.

The purpose of this paper is to present conceptual definitions for digital object use and reuse. Typically, assessment of digital repository content struggles to go beyond traditional usage metrics such as clicks, views or downloads. This is problematic for galleries, libraries, archives, museums and repositories (GLAMR) practitioners because use assessment does not tell a nuanced story of how users engage with digital content and objects.

This paper reviews prior research and literature aimed at defining use and reuse of digital content in GLAMR contexts and builds off of this group’s previous research to devise a new model for defining use and reuse called the use-reuse matrix.

This paper presents the use-reuse matrix, which visually represents eight categories and numerous examples of use and reuse. Additionally, the paper explores the concept of “permeability” and its bearing on the matrix. It concludes with the next steps for future research and application in the development of the Digital Content Reuse Assessment Framework Toolkit (D-CRAFT).

The authors developed this model and definitions to inform D-CRAFT, an Institute of Museum and Library Services National Leadership Grant project. This toolkit is being developed to help practitioners assess reuse at their own institutions.

To the best of the authors’ knowledge, this paper is one of the first to propose distinct definitions that describe and differentiate between digital object use and reuse in the context of assessing digital collections and data.

This article provides an overview of recent developments relating to the application of thesauri in information organisation and retrieval on the World Wide Web. It describes some recent thesaurus projects undertaken to facilitate resource description and discovery and access to wide‐ranging information resources on the Internet. Types of thesauri available on the Web, thesauri integrated in databases and information retrieval systems, and multiple‐thesaurus systems for cross‐database searching are also discussed. Collective efforts and events in addressing the standardisation and novel applications of thesauri are briefly reviewed.

The purpose of this paper is to examine and provide an insight into Canadian post‐secondary institutional repositories (IRs) with respect to user interface features and knowledge organization systems (KOS) used.

The approach is to explore all Canadian post‐secondary institutions and their user interfaces to establish the type of searching and browsing features they have used and whether or not they have made use of KOS such as subject heading lists or classification schemes.

A directory of 27 IRs in Canada is created. Incorporation of KOS in institutional repository is evaluated. The examination is focuses on accessibility, searching, KOS use, and retrieval. Evaluation shows that few IRs have incorporated complex KOS such as controlled vocabularies. Browsing and searching options are available, but user interfaces are usually not modified to enhance information retrieval.

This is the first paper examining Canadian IRs from the perspectives of searching, browsing and the use of KOS.