文章基本信息

标题：Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data
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作者：Beijie Xu ; Mimi Recker
期刊名称：Educational Technology and Society
印刷版ISSN：1176-3647
电子版ISSN：1436-4522
出版年度：2012
卷号：15
期号：3
页码：103-115
出版社：IFETS - Attn Kinshuck
摘要：Teachers 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 behaviors 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, called the Instructional Architect. The clustering approach employed a robust statistical model called latent class analysis. In addition, frequent itemsets mining was used to clean and extract common patterns from the clusters initially generated. The final clusters identified three groups of teachers in the IA: key brokers, insular classroom practitioners, and inactive islanders. Identified clusters were triangulated with data collected in teachers’ registration profiles. Results showed that increased teaching experience and comfort with technology were related to teachers’ effectiveness in using the IA.
关键词：Educational data mining; Latent class analysis; Teacher usage patterns