The nature of complexity facing novice designers in a constraint satisfaction task

This paper examines the effects of different types of complexity facing novice designers in constraint satisfaction tasks. The nature of the complexity in a design task was varied by manipulating different aspects of the extrinsic constraints, which refer to restrictions concerning how design components can be assembled. We investigated the effect of the number of constraints (Study 1) and the number of different types of constraint (Study 2) in a simulated office design task. Results indicated that tackling a design task with a greater number of constraints, or more types of constraint, resulted in decrements in performance. Study 3 examined the effect of reasoning about constraints that involved a fixed location in the office layout and those that did not. It was found that having a higher proportion of constraints that referenced a fixed location led to better design performance. The theoretical and practical aspects of these results are discussed. Practitioner summary: This paper identifies sources of constraint complexity facing the novice designer in an office design task. Three features of constraints proved problematic: the number of constraints, the number of types of constraint and whether the constraint involved a specific location. Training and decision support solutions are discussed.

[1]  Henk G. Schmidt,et al.  The effect of self-explanation and prediction on the development of principled understanding of chess in novices , 2007 .

[2]  S. Phillips,et al.  Relational knowledge: the foundation of higher cognition , 2010, Trends in Cognitive Sciences.

[3]  A. Garnham,et al.  Thinking and Reasoning , 1994 .

[4]  John S. Gero,et al.  To sketch or not to sketch? That is the question , 2006 .

[5]  John Millar Carroll,et al.  Presentation and Representation in Design Problem Solving. , 1980 .

[6]  R. Weisberg,et al.  Following the wrong footsteps: fixation effects of pictorial examples in a design problem-solving task. , 2005, Journal of experimental psychology. Learning, memory, and cognition.

[7]  Mary E. Ahlum-Heath,et al.  The effect of conscious controlled verbalization cognitive strategy on transfer in problem solving , 1986, Memory & cognition.

[8]  Herbert A. Simon,et al.  Information-processing models of cognition , 1981, J. Am. Soc. Inf. Sci..

[9]  J. Flavell Metacognition and Cognitive Monitoring: A New Area of Cognitive-Developmental Inquiry. , 1979 .

[10]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[11]  Nigel Cross,et al.  Solution driven versus problem driven design: strategies and outcomes , 2006 .

[12]  D. Broadbent,et al.  On the Relationship between Task Performance and Associated Verbalizable Knowledge , 1984 .

[13]  Dianne C. Berry,et al.  The combination of explicit and implicit learning processes in task control , 1987 .

[14]  Iris Vessey,et al.  The Role of Cognitive Fit in the Relationship Between Software Comprehension and Modification , 2006, MIS Q..

[15]  Dennis F. Galletta,et al.  Cognitive Fit: An Empirical Study of Information Acquisition , 1991, Inf. Syst. Res..

[16]  John Patrick,et al.  Components of Fault-Finding: Symptom Interpretation , 1989 .

[17]  W. Hacker,et al.  Reflective verbalization improves solutions: The effects of question-based reflection in design problem solving , 2004 .

[18]  Hironari Nozaki,et al.  Effects of self-explanation as a metacognitive strategy for solving mathematical word problems† , 2007 .

[19]  John S. Gero,et al.  Representational affordances in design, with examples from analogy making and optimization , 2012 .

[20]  J. Sweller Element Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load , 2010 .

[21]  Donald R. Jones,et al.  Choosing and Translating between Problem Representations , 1995 .

[22]  S. Phillips,et al.  Processing capacity defined by relational complexity: implications for comparative, developmental, and cognitive psychology. , 1998, The Behavioral and brain sciences.

[23]  Shaaron Ainsworth,et al.  The effects of self-explaining when learning with text or diagrams , 2003, Cogn. Sci..

[24]  Jonathan Evans,et al.  Problem-solving Strategies and Expertise in Engineering Design. , 1997 .

[25]  Shaaron Ainsworth,et al.  The impact of text coherence on learning by self-explanation , 2007 .

[26]  Winfried Hacker,et al.  Question-answering-technique to support freshman and senior engineers in processes of engineering design , 2010 .

[27]  J. Bain,et al.  PSYCHOLOGICAL SCIENCE Research Article How Many Variables Can Humans Process? , 2022 .

[28]  Linden J. Ball,et al.  Spontaneous analogising in engineering design: a comparative analysis of experts and novices , 2004 .

[29]  Willemien Visser,et al.  Designing as Construction of Representations: A Dynamic Viewpoint in Cognitive Design Research , 2006, Hum. Comput. Interact..

[30]  Anne Römer,et al.  External Support of Problem Analysis in Design Problem Solving , 2000 .

[31]  R. L. Dominowski,et al.  Metacognition and problem solving : A process-oriented approach. , 1995 .

[32]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[33]  R. A. Carlson,et al.  External support and the development of problem-solving routines. , 1999 .

[34]  H. Simon,et al.  Why are some problems hard? Evidence from Tower of Hanoi , 1985, Cognitive Psychology.

[35]  T. Gog,et al.  Effects of concurrent monitoring on cognitive load and performance as a function of task complexity , 2011 .

[36]  John Patrick,et al.  Training: Research and practice. , 1992 .

[37]  Vincent Aleven,et al.  An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor , 2002, Cogn. Sci..

[38]  Laura R. Novick,et al.  Spatial diagrams: Key instruments in the toolbox for thought , 2000 .

[39]  Eugene C. Freuder,et al.  Configuration as Composite Constraint Satisfaction , 1996 .