ASSESS — Automatic Self-Assessment Using Linked Data

Abstract

The Linked Open Data Cloud is a goldmine for creating open and low-cost educational applications: First, it contains open knowledge of encyclopedic nature on a large number of real-world entities. Moreover, the data being structured ensures that the data is both human- and machine-readable. Finally, the openness of the data and the use of RDF as standard format facilitate the development of applications that can be ported across different domains with ease. However, RDF is still unknown to most members of the target audience of educational applications. Thus, Linked Data has commonly been used for the description or annotation of educational data. Yet, Linked Data has (to the best of our knowledge) never been used as direct source of educational material. With ASSESS, we demonstrate that Linked Data can be used as a source for the automatic generation of educational material. By using innovative RDF verbalization and entity summarization technology, we bridge between natural language and RDF. We then use RDF data directly to generate quizzes which encompass questions of different types on user-defined domains of interest. By these means, we enable learners to generate self-assessment tests on domains of interest. Our evaluation shows that ASSESS generates high-quality English questions. Moreover, our usability evaluation suggests that our interface can be used intuitively. Finally, our test on DBpedia shows that our approach can be deployed on very large knowledge bases.

L. Bühmann, R. Usbeck—Both authors contributed equally to this work.

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