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| Main Authors: | , |
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| Format: | Recurso educativo Open Access |
| Language: | en |
| Published: |
2012
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| Subjects: | |
| Online Access: | https://eric.ed.gov/?id=EJ992506 |
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| _version_ | 1867181570693005312 |
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| author | Xu, Beijie Recker, Mimi |
| author_facet | Xu, Beijie Recker, Mimi Xu, Beijie Recker, Mimi |
| collection | Education Resources Information Center |
| contents | Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data Xu, Beijie Recker, Mimi Electronic Libraries Instructional Design Mixed Methods Research Use Studies Federal Aid Experienced Teachers Beginning Teachers Teaching Experience Research Methodology Educational Research Computer Uses in Education Internet Discussion Pattern Recognition Data Information Retrieval Data Analysis Statistical Analysis Teacher Developed Materials 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. (Contains 5 tables and 1 figure.) |
| format | Recurso educativo Open Access |
| id | eric_EJ992506 |
| institution | ERIC Institute of Education Sciences |
| language | en |
| publishDate | 2012 |
| record_format | eric |
| spellingShingle | Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data Xu, Beijie Recker, Mimi Electronic Libraries Instructional Design Mixed Methods Research Use Studies Federal Aid Experienced Teachers Beginning Teachers Teaching Experience Research Methodology Educational Research Computer Uses in Education Internet Discussion Pattern Recognition Data Information Retrieval Data Analysis Statistical Analysis Teacher Developed Materials Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data Xu, Beijie Recker, Mimi Electronic Libraries Instructional Design Mixed Methods Research Use Studies Federal Aid Experienced Teachers Beginning Teachers Teaching Experience Research Methodology Educational Research Computer Uses in Education Internet Discussion Pattern Recognition Data Information Retrieval Data Analysis Statistical Analysis Teacher Developed Materials 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. (Contains 5 tables and 1 figure.) |
| title | Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data |
| topic | Electronic Libraries Instructional Design Mixed Methods Research Use Studies Federal Aid Experienced Teachers Beginning Teachers Teaching Experience Research Methodology Educational Research Computer Uses in Education Internet Discussion Pattern Recognition Data Information Retrieval Data Analysis Statistical Analysis Teacher Developed Materials |
| url | https://eric.ed.gov/?id=EJ992506 |