ArchiMate Modeling Mistakes : A Comparative Analysis of Student Assignments and Prior Research on EA Modeling Mistakes

Abstract
Enterprise Architecture is one of the core competencies of higher education IS programs and is widely regarded as one the most common ways to produce valuable and usable information for decision-makers regarding business-IT alignment. Prior research notes the limited perceived usefulness of EA visualizations, which are often characterized by their complexity, lack of focus, and inappropriate level of abstraction, which inhibits their effective use for decision-making. Despite this, research on teaching enterprise architecture modeling is scarce, and understanding the problems students face and the solutions to overcome these are lacking. This study reports findings from the analysis of roughly 300 student assignments, collected from an undergraduate course on EA. Our findings indicate that the mistakes made by the students are in line with the prior research, as the student's modeling errors aligned with limitations commonly associated with EA models, such as poor readability, unfit level of abstraction, and either lack of or excessive information in the model.
Main Authors
Format
Conferences Conference paper
Published
2024
Series
Subjects
Publication in research information system
Publisher
University of Hawaiʻi at Mānoa
Original source
https://hdl.handle.net/10125/107003
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202401311654Use this for linking
Parent publication ISBN
978-0-9981331-7-1
Review status
Peer reviewed
ISSN
1530-1605
Conference
Hawaii International Conference on System Sciences
Language
English
Published in
Proceedings of the Annual Hawaii International Conference on System Sciences
Is part of publication
Proceedings of the 57th Annual Hawaii International Conference on System Sciences (HICSS 2024)
Citation
  • Seppänen, V., & Nurmi, J. (2024). ArchiMate Modeling Mistakes : A Comparative Analysis of Student Assignments and Prior Research on EA Modeling Mistakes. In T. X. Bui (Ed.), Proceedings of the 57th Annual Hawaii International Conference on System Sciences (HICSS 2024) (pp. 5154-5163). University of Hawaiʻi at Mānoa. Proceedings of the Annual Hawaii International Conference on System Sciences. https://hdl.handle.net/10125/107003
License
CC BY-NC-ND 4.0Open Access
Copyright© Authors 2024

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