.png){kind=icon}
返回
Using a knowledge learning framework to predict errors in database design
.png){kind=icon}
PRE
AI
DOI：10.1016/j.is.2013.08.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Conceptual data modeling is a critical but difficult part of database development. Little research has attempted to find the underlying causes of the cognitive challenges or errors made during this stage. This paper describes a Modeling Expertise Framework (MEF) that uses modeler expertise to predict errors based on the revised Bloom's taxonomy (RBT). The utility of RBT is in providing a classification of cognitive processes that can be applied to knowledge activities such as conceptual modeling. We employ the MEF to map conceptual modeling tasks to different levels of cognitive complexity and classify current modeler expertise levels. An experimental exercise confirms our predictions of errors. Our work provides an understanding into why novices can handle entity classes and identifying binary relationships with some ease, but find other components like ternary relationships difficult. We discuss implications for data modeling training at a novice and intermediate level, which can be extended to other areas of Information Systems education and training. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Database design
Modeling expertise
Entity Relationship modeling
Revised Bloom's taxonomy
Analysis of errors
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
2.8K
被引数:
1.8K
机构
引用论文
The impact of data models and task complexity on end-user performance: an experimental investigation
OAAI
OAAI分享收藏A new approach to ITS-curriculum and course authoring: the authoring environment.png)
COMPUTERS & EDUCATION
COMPUTERS & EDUCATIONIF10.5
PREAI
分享收藏分享收藏Natural Root Cellular Variation in Responses to Osmotic Stress in Arabidopsis thaliana AccessionsGenesOAAI
GenesIF0
OAAI分享收藏
查看更多内容