Studying Educational Digital Library Users and the Emerging Research Methodologies

Studying Educational Digital Library Users and the Emerging Research Methodologies

Anne R. Diekema (Utah State University, USA) and Mimi M. Recker (Utah State University, USA)
DOI: 10.4018/978-1-4666-5888-2.ch485
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The recent widespread availability of educational resources on the World Wide Web holds great potential for transforming education. In recognition of this potential, several large-scale initiatives are developing repositories (or, digital libraries) of online learning resources, sometimes referred to as learning objects (Wiley, Recker, & Gibbons, 2000) or open education resources (Smith & Casserly, 2006). These learning resources can consist of innovative curricula, teacher-created lesson plans, as well as interactive tools such as visualizations and simulations that support educational uses of real-world datasets (McArthur & Zia, 2008).

Online resources stored in digital libraries are usually catalogued and described with metadata that supports teachers’ discovery (Weibel, 1995). Education-specific metadata records for learning resources contain basic catalog information about the object, including its general, technical, semantic, and pedagogical characteristics. In some implementations, it can also include more subjective information such as teaching tips or comments.

Increasingly, this technical infrastructure is combined with the social aspects of ‘Web 2.0’ functionality to produce a collaborative network not bounded by geography, time, or educational context. The intent is to support users (especially teachers and students) to access, create, connect, and share knowledge in ways that fundamentally transform practice in order to help improve the effectiveness and efficiency of education (Borgman et al., 2008; Computing Research Association, 2005).

The next section briefly describes several educational digital libraries and associated tools.

Key Terms in this Chapter

Data Mining: Using statistical algorithms to establish patterns in data.

Web Analytics: The analysis of web usage data captured in a system log or by page-tagging scripts (e.g., the number of (website) visits, unique visitors, and page views; geo location of visitor IP address, length of visit, page of origin (referrer), and bounce rate (visitors that leave the site for another).

Clustering: Statistical grouping of the phenomenon under study in different clusters or classes, based on member similarity within each cluster and dissimilarity of members across clusters.

Educational Digital Library: A library or repository of digital materials with an educational focus created to aid teaching and learning.

Knowledge Discovery in Databases: See Data mining.

Transaction Log: A log of the interactions between a user and the digital library and its content.

Query Log Analysis: The study of user searches to study information seeking behavior, system functionality, and search topic trends.

Social Network Analysis: The study of relationships or interactions between entities that are part of the same (social) network (e.g., department, school, digital library).

Triangulation: The analysis of related data that has been collected by different means (e.g., user search logs and user interview on searching) or across different populations.

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