Towards Improving Conceptual Modeling: An Examination of Common Errors and Their Underlying Reasons

Persistent Link:
http://hdl.handle.net/10150/195586
Title:
Towards Improving Conceptual Modeling: An Examination of Common Errors and Their Underlying Reasons
Author:
Currim, Sabah
Issue Date:
2008
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Databases are a critical part of Information Technology. Following a rigorous methodology in the database lifecycle ensures the development of an effective and efficient database. Conceptual data modeling is a critical stage in the database lifecycle. However, modeling is hard and error prone. An error could be caused by multiple reasons. Finding the reasons behind errors helps explain why the error was made and thus facilitates corrective action to prevent recurrence of that type of error in the future. We examine what errors are made during conceptual data modeling and why. In particular, this research looks at expertise-related reasons behind errors. We use a theoretical approach, grounded in work from educational psychology, followed up by a survey study to validate the model. Our research approach includes the following steps: (1) measure expertise level, (2) classify kinds of errors made, (3) evaluate significance of errors, (4) predict types of errors that will be made based on expertise level, and (5) evaluate significance of each expertise level. Hypotheses testing revealed what aspects of expertise influence different types of errors. Once we better understand why expertise related errors are made, future research can design tailored training to eliminate the errors.
Type:
text; Electronic Dissertation
Keywords:
conceptual modeling; Bloom's taxonomy; databases; learning; training; ER modeling
Degree Name:
PhD
Degree Level:
doctoral
Degree Program:
Management Information Systems; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Ram, Sudha
Committee Chair:
Ram, Sudha

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleTowards Improving Conceptual Modeling: An Examination of Common Errors and Their Underlying Reasonsen_US
dc.creatorCurrim, Sabahen_US
dc.contributor.authorCurrim, Sabahen_US
dc.date.issued2008en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractDatabases are a critical part of Information Technology. Following a rigorous methodology in the database lifecycle ensures the development of an effective and efficient database. Conceptual data modeling is a critical stage in the database lifecycle. However, modeling is hard and error prone. An error could be caused by multiple reasons. Finding the reasons behind errors helps explain why the error was made and thus facilitates corrective action to prevent recurrence of that type of error in the future. We examine what errors are made during conceptual data modeling and why. In particular, this research looks at expertise-related reasons behind errors. We use a theoretical approach, grounded in work from educational psychology, followed up by a survey study to validate the model. Our research approach includes the following steps: (1) measure expertise level, (2) classify kinds of errors made, (3) evaluate significance of errors, (4) predict types of errors that will be made based on expertise level, and (5) evaluate significance of each expertise level. Hypotheses testing revealed what aspects of expertise influence different types of errors. Once we better understand why expertise related errors are made, future research can design tailored training to eliminate the errors.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectconceptual modelingen_US
dc.subjectBloom's taxonomyen_US
dc.subjectdatabasesen_US
dc.subjectlearningen_US
dc.subjecttrainingen_US
dc.subjectER modelingen_US
thesis.degree.namePhDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineManagement Information Systemsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorRam, Sudhaen_US
dc.contributor.chairRam, Sudhaen_US
dc.contributor.committeememberDurcikova, Alexandraen_US
dc.contributor.committeememberBrown, Sueen_US
dc.identifier.proquest2568en_US
dc.identifier.oclc659749587en_US
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