一句话：通过分析概念数据建模中的常见错误，提出学术与产业培训互鉴的改进思路。
论文关注概念数据建模培训中学术与产业之间的鸿沟。作者指出，尽管硕士生与企业受训者在背景与目标上不同，但他们在创建数据模型时会犯相似的基础错误。基于此，文章比较了两类培训路径的差异与共性，分析新手和专业人员常见的概念建模错误，并提出通过针对这些错误进行教学来提升双方培训质量。文章还建议在学术与产业培训之间进行方法论交换，以相互借鉴。作为一篇 idea paper，其贡献在于提出改进方向与合作设想，而非提供大规模实证评估。
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.
DOI 原文 ·
@inproceedings{paperbot10948,
title = {Academic and Industry Training for Data Modelling: Ideas for Mutual Benefit},
author = {Daria Bogdanova and Monique Snoeck},
booktitle = {ICSE},
year = {2022},
doi = {10.1109/icse-seet55299.2022.9794226}
}