Academic journal article Educational Technology & Society

Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data

Academic journal article Educational Technology & Society

Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data

Article excerpt


Increasingly, education and training are delivered beyond the constraints of the classroom environment, and the increasingly widespread availability of online repositories, educational digital libraries, and their associated tools are major catalysts for these changes (Borgman et al., 2008; Choudhury, Hobbs, & Lorie, 2002). Teachers, of course, are a primary intended audience of educational digital libraries. Studies have shown that teachers use digital libraries and web resources in many ways, including lesson planning, curriculum planning (Carlson & Reidy, 2004; Perrault, 2007; Sumner & CCS Team, 2010), and looking for examples, activities as well as illustrations to complement textbook materials (Barker, 2009; Sumner & CCS Team, 2010; Tanni, 2008). Less frequently mentioned ways are learning about teaching areas(Sumner & CCS Team, 2010; Tanni, 2008), networking to find out what other teachers do (Recker, 2006), and conducting research (Recker et al., 2007). These studies, however, were generally conducted in laboratory-like settings, using traditional research methods, such as interview, survey, and observation.

Due to the distributed nature of the Web, traditional research methods and data sources do not support a thorough understanding of teachers' online behaviors in large online repositories. In response, web-based educational applications are increasingly engineered to capture users' fine-grained behaviors in real-time, and thus provide an exciting opportunity for researchers to analyze these massive datasets, and hence better understand online users (Romero & Ventura, 2007).

These records of access patterns can provide an overall picture of digital library users and their usage behaviors. With the help of modern data mining techniques--the discovery and extraction of implicit knowledge from one or more large databases(Han & Kamber, 2006; Pahl & Donnellan, 2002; Romero & Ventura, 2007)--the data can further be analyzed to gain an even deeper understanding of users. Yet, despite the wealth of fine-grained usage data, data mining has seldom been applied to digital library user datasets, especially when studying teacher users.

The study reported in this article used a particular digital library tool, called the Instructional Architect (, which supports teachers in authoring and sharing instructional activities using online resources(Recker, 2006). The IA was used as a test bed for investigating how the data mining process in general, and clustering methods in particular, can help identify the different and diverse teacher groups based on their online usage patterns. This study built substantially on results from a preliminary study that also used a clustering approach(Xu & Recker, in press). In particular, both studies relied on a clustering approach that used a robust statistical model, latent class analysis (LCA). In addition, this study used more refined user feature space, and frequent item sets mining was used to clean and extract common patterns from the clusters initially generated. Lastly, as a means of validation the clustering results, we explored the relationship between teachers' characteristics(comfort level with technology and teaching experience) and the teacher clusters that emerged from the study.

This article is organized as follows. The literature review first describes the Knowledge Discovery and Data Mining (KDD) process, and several clustering studies conducted with educational datasets. This is followed by a brief introduction to the Instructional Architect tool. We then describe our data mining approach, starting from data collection and selection, through data analysis, interpretation, and inference. Finally, as part of the interpretation process, we triangulated data from teachers' registration profiles to validate the clustering results. We conclude with the implications, contributions, and limitations of this work. …

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