Estimation and Visualization of Digital Library Content Similarities

We present a semantic similarity-based recommender service. Our experimental application and validation domain consists of K-12 engineering learning resources. Given a learning resource, we must determine which educational standards it addresses and vice versa, find resources that align with a given standard. One approach to this problem suggests transitively inferring standard alignment from the semantic similarity of other, previously aligned resources. We investigate a bigram-based similarity estimator and a Sammon map-based user interface for visualizing the resulting similarity space. Validation was performed using resources in TeachEngineering.org, a K-12 STEM digital library. Target classifications were derived from author-generated tables of content for these resources. Testing shows good performance of the similarity measure, both in its correspondence to the collection’s table of contents and in the form of a two-dimensional Sammon map. The results provide evidence for the feasibility and practicality of using automated similarity measures in standards alignment and similar problems

Learning Theory And Instructional Design Using Learning Object..............................................5

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