Stammdaten

Titel: Identification of Dependencies Between Learning Outcomes in Computing Science Curricula for Primary and Secondary Education - On the Way to Personalized Learning Paths
Untertitel:
Kurzfassung:

The multitude of curricula and competency models poses great challenges for primary and secondary teachers due to the wealth of descriptions. Defining optimal (or personalized) learning paths is thus impeded. This paper now takes a closer look at 7 curricula from 6 different countries and presents an approach for the identification of learning outcomes and dependencies (requires and expands) between them in order to support the identification of learning paths. The approach includes different strategies from natural language processing, but it also makes use of a refined and simplified version of Bloom's Taxonomy to iden- tify dependencies between the learning outcomes. It is shown that the identification of similar learning outcomes works very well compared to expert opinions. The identification of dependencies, however, only works well for detecting learning outcomes that refine other learning outcomes. The detection of learning outcomes which build on each other is, on the other hand, still heavily dependent on the definition of dictionaries and a computing science topics ontology.

Schlagworte: Primary and Secondary Education, Learning Outcomes, Computing Science, Natural Language Processing
Publikationstyp: Beitrag in Proceedings (Autorenschaft)
Erscheinungsdatum: 06.11.2020 (Print)
Erschienen in: Informatics in Schools. Engaging Learners in Computational Thinking
Informatics in Schools. Engaging Learners in Computational Thinking
zur Publikation
 ( Springer; K. Kori, M. Laanpere )
Titel der Serie: Lecture Notes in Computer Science
Bandnummer: 12518
Erstveröffentlichung: Ja
Seite: S. 185 - 196

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Erscheinungsdatum: 06.11.2020
ISBN:
  • 978-3-030-63211-3
ISSN: -
Homepage: -

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Sachgebiete
  • 503015 - Fachdidaktik Technische Wissenschaften
Forschungscluster
  • Bildungsforschung
Peer Reviewed
  • Ja
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  • Science to Science (Qualitätsindikator: I)
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Arbeitsgruppen
  • Informatikdidaktik

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