Complete thesis - Murdoch University

• generic attributes (Lethbridge, 2000).The value of these skills is confirmed in other studies, both general (eg Scott and Yates(2002); Scott and Wilson (2002)) and specifically in RE (Kozar, 1989; Zowghi, 2004).However, despite a clear indication that there are issues in the education of RequirementsEngineers, very little research exists that addresses these.Learning for Requirements EngineeringAny software development project is seen as knowledge-intensive, with many conceptsdeveloped to ease or guide the processing of knowing (Robillard, 1999), and learning (Klemolaand Rilling, 2002). This complexity, in addition to case-to-case irregularity in the domainsof application (their ill-structured nature (Simon, 1973)), poses a serious challenge for thelearning of Requirements Engineering practice.However, the accepted view that a science/engineering approach to software developmentwould ensure quality influences the learning of RE: by implication a scientific/engineeringeducation was seen as the mechanism to train students to be competent practitioners. Outsidethe science/engineering academic faculties, the education of software developers alsomodelled scientific and engineering methodologies. Benson (2003) notes that within theemerging Information Systems (IS) discipline of the 1970s, academics were migrants to thediscipline, with an overwhelming majority having qualifications in other areas, most oftencomputer science. Practitioners also relied heavily on scientific, mathematic and engineeringdisciplines, with many migrating from engineering and manufacturing. In general, thereforethe normative professional education curriculum continues to be the basis for IT education.This challenge posed by the learning of RE may be described more generally as the natureof learning in complex and ill-structured domains (Spiro et al, 1991). Here an expert levelof cognitive flexibility is required: conceptual oversimplification leads to failures that takecommon, predictable forms and to an inability to apply knowledge to new cases (failures oftransfer). Cognitive flexibility includes• the ability to represent knowledge from different conceptual and case perspectives and,later, the ability to construct from these a knowledge ensemble tailored to the needsof the understanding or problem-solving at hand – the same items of knowledge needto be presented and learned in a variety of different ways and for a variety of differentpurposes• the development of cognitively flexible processing skills and the acquisition of knowledgestructures which can support this. Flexible environments are required to permit the7