Abstract
Let one need to select appropriate textbooks for a given course or different parts of a course presented by their limited lists of keywords. When such a selection is based only on correspondence between the contents of textbooks and course description then the problem solution reduces to procedures of Information Retrieval. Here, the former can be considered as a database of documents and the latter as a query. In the paper we show the possibilities of two IR methods: (1) a spreading activation method (SAM) using semantic network related to textbooks, and (2) a coverage-based method (CBM) using a simple formal comparison of vocabularies. Unlike the usual applications of SAM and CBM we use: the criterion of term specificity for building the vocabulary of textbooks and the normalized measure of network activation. The experimental data includes two examples from technical and humantitarian sciences: the course of “Database Management” in the Catholic University of San Pablo in Peru, and the course of “Spanish Lexicology” in the Autonomous University of Barcelona in Spain. The results of the application of both methods are compared to the manual assessments of experts. The presented research is a Pilot study.
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Authors thank the reviewers from the MICAI-2017 Program Committee for their attention to this research and their valuable recommendations.
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Stefanovskiy, D., Alexandrov, M., Catena, A., Danilova, V., Tejada, J. (2018). Courses Select Textbooks: Comparison of Two Methods. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Computational Intelligence. MICAI 2017. Lecture Notes in Computer Science(), vol 10633. Springer, Cham. https://doi.org/10.1007/978-3-030-02840-4_18
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DOI: https://doi.org/10.1007/978-3-030-02840-4_18
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