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Academic and industry training for data modelling: ideas for mutual benefit

Published: 17 October 2022 Publication History

Abstract

Finding a common ground between academic and industry training is a challenging task. However, despite all the differences between the two worlds, there is one important common aspect for the master students and industry trainees in the field of conceptual data modelling: the mistakes they make when creating data models. This idea paper describes the existing differences and similarities between two academic and industrial training approaches for teaching conceptual data modelling. We propose ideas for improvement of training quality on both sides by analysing and tackling the ground errors in conceptual data modelling made both by novices and professionals. Additionally, we propose the ideas for methodology exchanges between the two training types.

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cover image ACM Conferences
ICSE-SEET '22: Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Software Engineering Education and Training
May 2022
292 pages
ISBN:9781450392259
DOI:10.1145/3510456
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 17 October 2022

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Author Tags

  1. academic training
  2. conceptual modelling errors
  3. database modelling
  4. industrial training
  5. software architecture

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  • KU Leuven Research Council

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ICSE '22
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ICSE 2025

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