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A Pattern Mining Method for Teaching Practices

Author

Listed:
  • Bernhard Standl

    (Institute for Informatics and Digital Education, Karlsruhe University of Education, Bismarckstrasse 10, 76133 Karlsruhe, Germany)

  • Nadine Schlomske-Bodenstein

    (Institute for Informatics and Digital Education, Karlsruhe University of Education, Bismarckstrasse 10, 76133 Karlsruhe, Germany)

Abstract

When integrating digital technology into teaching, many teachers experience similar challenges. Nevertheless, sharing experiences is difficult as it is usually not possible to transfer teaching scenarios directly from one subject to another because subject-specific characteristics make it difficult to reuse them. To address this problem, instructional scenarios can be described as patterns, which has already been applied in educational contexts. Patterns capture proven teaching strategies and describe teaching scenarios in a unified structure that can be reused. Since priorities for content, methods, and tools are different in each subject, we show an approach to develop a domain-independent graph database to collect digital teaching practices from a taxonomic structure via the intermediate step of an ontology. Furthermore, we outline a method to identify effective teaching practices from interdisciplinary data as patterns from the graph database using an association rule algorithm. The results show that an association-based analysis approach can derive initial indications of effective teaching scenarios.

Suggested Citation

  • Bernhard Standl & Nadine Schlomske-Bodenstein, 2021. "A Pattern Mining Method for Teaching Practices," Future Internet, MDPI, vol. 13(5), pages 1-14, April.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:5:p:106-:d:542432
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    References listed on IDEAS

    as
    1. Albert Weichselbraun & Philipp Kuntschik & Vincenzo Francolino & Mirco Saner & Urs Dahinden & Vinzenz Wyss, 2021. "Adapting Data-Driven Research to the Fields of Social Sciences and the Humanities," Future Internet, MDPI, vol. 13(3), pages 1-22, February.
    2. Miguel Diogo & Bruno Cabral & Jorge Bernardino, 2019. "Consistency Models of NoSQL Databases," Future Internet, MDPI, vol. 11(2), pages 1-19, February.
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