Comparison of human and machine-based educational standard assignment networks

Increasing availability of digital libraries of K-12 educational resources, coupled with an increased emphasis on standard-based teaching necessitates assignment of the standards to those resources. Since manual assignment is a laborious and ongoing task, machine-based standard assignment tools have...

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Bibliographic Details
Published in:International journal on digital libraries 2010-09, Vol.11 (3), p.209-223
Main Authors: Reitsma, René F., Diekema, Anne R.
Format: Article
Language:English
Subjects:
Academic standards
Artificial intelligence
Availability
Classifiers
Computer Science
Database Management
Digital libraries
Digital systems
Education
Gain
Information Systems and Communication Service
Library science
Networks
Tasks
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Summary:Increasing availability of digital libraries of K-12 educational resources, coupled with an increased emphasis on standard-based teaching necessitates assignment of the standards to those resources. Since manual assignment is a laborious and ongoing task, machine-based standard assignment tools have been under development for some time. Unfortunately, data on the performance of these machine-based classifiers are mostly lacking. In this article, we explore network modeling and layout to gain insight into the differences between assignments made by catalogers and those by the well-known Content Assignment Tool (CAT) machine-based classifier. To build the standard assignment networks, we define standards to be linked if they are jointly assigned to a learning resource. Comparative analysis of the topology and layout of the networks shows that whereas the cataloger-based network reflects the underlying curriculum, i.e., clusters of standards separate along lines of lesson content and pedagogical principles, the machine-based network lacks these relationships. This shortcoming is partially traced back to the machine classifier’s difficulties in recognizing standards that express ways and means of conducting science.
ISSN:1432-5012
1432-1300
DOI:10.1007/s00799-011-0074-8