Business Intelligent in Digital Library Data
Keywords:
Educational Data Mining, Educational Web Mining, Clustering, K-means, Digital Libraries, Teacher UsersAbstract
eachers and students increasingly enjoy unprecedented access to abundant web resources and digital libraries to enhance and enrich their classroom experiences. However, due to the distributed nature of such systems, conventional educational research methods, such as surveys and observations, provide only limited snapshots. In addition, educational data mining, as an emergent research approach, has seldom been used to explore teachers’ online behaviours when using digital libraries. Building upon results from a preliminary study, this article presents results from a clustering study of teachers’ usage patterns while using an educational digital library tool. The clustering approach employed a robust statistical model called latent class analysis. In addition, frequent item sets mining was used to clean and extract common patterns from the clusters initially generated. The final clusters identified three groups of teachers in this tool: key brokers, insular classroom practitioners, and inactive islanders. Identified clusters were triangulated with data collected in teacher’s registration profiles. Results showed that increased teaching experience and comfort with technology were related to teachers’ effectiveness in using this tool.
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