Please use this identifier to cite or link to this item:
Title: Development of A Learning Model on Software Design Pattern Selection for Novice Developers
Authors: Masita Abdul Jalil 
Nurul Azarina Abd.Rahman 
Noraida Hj. Ali 
Shahrul Azman Mohd Noah 
Noor Maizura Mohamad 
Fatihah Mohd 
Keywords: Cognitive theory;design pattern;learning model;novice;design pattern selection.
Issue Date: 2020
Publisher: Association for Computing Machinery (ACM)
Conference: 2020 9th International Conference on Educational and Information Technology (ICEIT 2020) 
Design pattern is still actively discussed in software engineering academic research. The highlight of research that still gains attention is the use of design patterns as a learning medium to improve object-oriented design skills among fresh developers or novices. This paper focuses on the design of a learning model for design pattern selection. The main objective of the proposed model is to reduce the learning curve on design pattern application. Selected cognitive methods are implemented to minimize the cognitive complexity throughout the pattern selection process. This is aimed to assist novices in learning the process of matching the design problem to design pattern. This learning model will be utilized as a practice for novices to gain expert design skills from diverse design approaches through design patterns. The learning model simplifies the pattern selection process which comprises of three sub-processes; 1) Identify design strategy 2) Identify design scope and 3) Identify design intention. In each sub-process, potential words indicating design flaws are highlighted to guide novices in identifying the underlying design issues in the attended problem. The keywords highlight feature enables novices to highlight correct information within the design problem that leads to the identification of the right solution from design patterns.
ISBN: 978-1-4503-7508-5
Appears in Collections:Faculty of Entrepreneurship and Business - Proceedings

Files in This Item:
File Description SizeFormat
Masita proceeding ACM published.pdfpublished565.44 kBAdobe PDFView/Open
Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.