Degree Name

Doctor of Philosophy


Faculty of Education


In a problem-based, computer-intensive learning environment, what is the nature of the interaction between student characteristics, computers and cognition? The question is examined in the context of an intensive study of 19 students of Architecture undertaking a 6 month problem-based course in which they were required to work collaboratively on the design and construction of interactive 3D models using a range of software in a Silicon Graphics laboratory. The research method was predominantly naturalistic and data-driven, employing video observation, interviewing, mind mapping and mental modelling. The computer tool used to organize, search and report on the data was NUD.IST (Non-numeric Unstructured Data - Indexing, Searching & Theorizing). The research strongly supported the constructivist paradigm of learning and isolated a range of factors which are relevant to successful cognitive construction in computer-rich environments: approach to learning, as measured on the Study Process Questionnaire; declarative, procedural and contextual knowledge of computing; the ability to make connections between computing and domain concepts; metacognitive awareness, in particular the conscious use of distributed cognitions; and recognition of the "affordances" of the computer system. The highest achieving students exhibited an overall deep approach to learning (with above average scores on deep motive) and a high level of contextual computing knowledge and structural integration of domain and computing concepts. Follow-up interviews were conducted 6 and 12 months after the course and these provided some evience of what Salomon (1993) describes as "cognitive residue" or long-term effects of working with intelligent tools.

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