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Comparison of human and machine-based educational standard assignment networks

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Abstract

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.

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Authors and Affiliations

  1. College of Business, Oregon State University, Corvallis, OR, USA

    René F. Reitsma

  2. Instructional Technology and Learning Sciences, Utah State University, Logan, UT, USA

    Anne R. Diekema

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  1. René F. Reitsma
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Correspondence to René F. Reitsma.

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Reitsma, R.F., Diekema, A.R. Comparison of human and machine-based educational standard assignment networks. Int J Digit Libr 11, 209–223 (2010). https://doi.org/10.1007/s00799-011-0074-8

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