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| Main Authors: | , |
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| Format: | Recurso educativo Open Access |
| Language: | en |
| Published: |
2011
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| Subjects: | |
| Online Access: | https://eric.ed.gov/?id=EJ1115400 |
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| _version_ | 1867181279674368000 |
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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 | Understanding Teacher Users of a Digital Library Service: A Clustering Approach Xu, Beijie Recker, Mimi Electronic Libraries Library Services Multivariate Analysis Electronic Learning Web Based Instruction Learning Activities Educational Technology Research Methodology Data Processing Nonparametric Statistics Statistical Analysis Teacher Behavior Teacher Characteristics Teacher Effectiveness Online Searching Users (Information) This article describes the Knowledge Discovery and Data Mining (KDD) process and its application in the field of educational data mining (EDM) in the context of a digital library service called the Instructional Architect (IA.usu.edu). In particular, the study reported in this article investigated a certain type of data mining problem, clustering, and used a statistical model, latent class analysis, to group the IA teacher users according to their diverse online behaviors. The use of LCA successfully helped us identify different types of users, ranging from window shoppers, lukewarm users to the most dedicated users, and distinguish the isolated users from the key brokers of this online community. The article concludes with a discussion of the implications of the discovered usage patterns on system design and on EDM in general. |
| format | Recurso educativo Open Access |
| id | eric_EJ1115400 |
| institution | ERIC Institute of Education Sciences |
| language | en |
| publishDate | 2011 |
| record_format | eric |
| spellingShingle | Understanding Teacher Users of a Digital Library Service: A Clustering Approach Xu, Beijie Recker, Mimi Electronic Libraries Library Services Multivariate Analysis Electronic Learning Web Based Instruction Learning Activities Educational Technology Research Methodology Data Processing Nonparametric Statistics Statistical Analysis Teacher Behavior Teacher Characteristics Teacher Effectiveness Online Searching Users (Information) Understanding Teacher Users of a Digital Library Service: A Clustering Approach Xu, Beijie Recker, Mimi Electronic Libraries Library Services Multivariate Analysis Electronic Learning Web Based Instruction Learning Activities Educational Technology Research Methodology Data Processing Nonparametric Statistics Statistical Analysis Teacher Behavior Teacher Characteristics Teacher Effectiveness Online Searching Users (Information) This article describes the Knowledge Discovery and Data Mining (KDD) process and its application in the field of educational data mining (EDM) in the context of a digital library service called the Instructional Architect (IA.usu.edu). In particular, the study reported in this article investigated a certain type of data mining problem, clustering, and used a statistical model, latent class analysis, to group the IA teacher users according to their diverse online behaviors. The use of LCA successfully helped us identify different types of users, ranging from window shoppers, lukewarm users to the most dedicated users, and distinguish the isolated users from the key brokers of this online community. The article concludes with a discussion of the implications of the discovered usage patterns on system design and on EDM in general. |
| title | Understanding Teacher Users of a Digital Library Service: A Clustering Approach |
| topic | Electronic Libraries Library Services Multivariate Analysis Electronic Learning Web Based Instruction Learning Activities Educational Technology Research Methodology Data Processing Nonparametric Statistics Statistical Analysis Teacher Behavior Teacher Characteristics Teacher Effectiveness Online Searching Users (Information) |
| url | https://eric.ed.gov/?id=EJ1115400 |