Developing and Evaluating Visual Analogies to Support Insight and Creative Problem Solving

The primary aim of this thesis is to gain a richer understanding of visual analogies for insight problem solving, and, in particular, how they can be better developed to ensure their effectiveness as hints. While much work has explored the role of visual analogies in problem solving and their facilitative role, only a few studies have analysed how they could be designed. This thesis employs a mixed method consisting of a practice-led approach for studying how visual analogies can be designed and developed and an experimental research approach for testing their effectiveness as hints for solving visual insight problems

Inference in classifier systems

Classifier systems (Css) provide a rich framework for learning and induction, and they have beenı successfully applied in the artificial intelligence literature for some time. In this paper, both theı architecture and the inferential mechanisms in general CSs are reviewed, and a number of limitations and extensions of the basic approach are summarized. A system based on the CS approach that is capable of quantitative data analysis is outlined and some of its peculiarities discussed

The persistence of analogies in design decision-making

Previous theories of design decision-making have discussed how designers use analogical reasoning to quickly scope the solution space down to one viable solution. This initial analogy forms the template of a possible action plan that is then modified to suit the unique properties of that particular problem. This use of analogical reasoning allows designers to quickly engage with the problem and generate a workable solution. Our findings indicate that this initial analogy actually persists across all stages of decision-making, and does not play a role solely during the first stage of scoping. This analogical persistence leads to poorer design decisions. This thesis presents a series of studies that adopt a mixed method approach to investigating the influence of analogies on the decision-making of Interaction Designers. We employed qualitative methods such as the Critical Decision Method for Eliciting Knowledge (Klein, 1989), which aided in identifying analogical persistence as a problem that leads to poorer quality decisions. We also employed quantitative methods such as the Design Fixation paradigm (Jansson & Smith, 1991) to investigate how different types of analogies (self-generated & provided Priming Analogies) can influence the expression of analogical persistence. Finally in an attempt to mitigate the potential pitfalls of analogical persistence, this thesis attempts to control it using principles from Design Rationale (Lee & Lai, 1991) and Reflection (Schon, 1983). Rather than seeing a decrease in analogical persistence, our manipulation actually increased fixation. A follow-up study identified that designers tend to poorly appraise the weaknesses in the initial analogy, which may have led to the aforementioned unexpected result. These findings challenge the notion that greater understanding of the design space will lead to higher quality design decisions

Object Recognition and Parsing with Weak Supervision

Object recognition is a fundamental problem in computer vision and has attracted a lot of research attention, while object parsing is equally important for many computer vision tasks but has been less studied. With the recent development of deep neural networks, computer vision researches have been dominated by deep learning approaches, which require large amount of training data for a specific task in a specific domain. The cost of collecting rare samples and making "hard" labels is forbiddingly high and has limited the development of many important vision studies, including object parsing. This dissertation will focus on object recognition and parsing with weak supervision, which tackles the problem when only a limited amount of data or label are available for training deep neural networks in the target domain. The goal is to design more advanced computer vision models with enhanced data efficiency during training and increased robustness to out-of-distribution samples during test. To achieve this goal, I will introduce several strategies, including unsupervised learning of compositional components in deep neural networks, zero/few-shot learning by preserving useful knowledge acquired in pre-training, weakly supervised learning combined with spatial-temporal information in video data, and learning from 3D computer graphics models and synthetic data. Furthermore, I will discuss new findings in our cognitive science projects and explain how the part-based representations benefit the development of visual analogical reasoning models. I believe this series of works alleviates the data-hungry problem of deep neural networks, and improves computer vision models to behave closer to human intelligence

Collaborative design : managing task interdependencies and multiple perspectives

This paper focuses on two characteristics of collaborative design with respect to cooperative work: the importance of work interdependencies linked to the nature of design problems; and the fundamental function of design cooperative work arrangement which is the confrontation and combination of perspectives. These two intrinsic characteristics of the design work stress specific cooperative processes: coordination processes in order to manage task interdependencies, establishment of common ground and negotiation mechanisms in order to manage the integration of multiple perspectives in design

Mixed media modelling of technological concepts in electricity, methods for supporting learning styles

The overarching objective of this research is to recognize the learning styles of engineering and technology students and to propose pedagogical methods for the comprehension of technological concepts in electricity. The topic of electrical resistor-capacitor (RC) circuits has been chosen because it is fundamental to engineering and technology courses. There is substantial evidence to suggest that students find such a concept difficult to grasp. The focus of the research lies in explicating undergraduate students cognitive structures about RC circuits, and proposing a method related to students learning styles of how these cognitive structures may be enhanced. The main thesis argument claims that the transfer of knowledge from familiar RC circuit configurations to unfamiliar RC circuit configurations does not occur easily even if the problem-space is kept identical. The methodology used in this research is a mixed-method approach employing qualitative and quantitative data-gathering and analysis processes. This research concludes that the reasons for lack of transfer of knowledge stem from conceptual and perceptual constraints. Constraints involve: (a) which analogical models are employed in relation to the RC circuit, (b) how the circuit schematic diagram is drawn, and (c) relations between analogy, circuit schematic diagram, voltage-time graphs and verbal jargon used to describe circuit behaviour. The research presents a variety of novel, custom-designed learning aids which are employed within the research methodology to rectify the lack of transfer of knowledge for the RC circuits considered in the study. The design of these learning aids is based on the concept of embodied cognition and mainly makes use of visual and kinaesthetic means to appeal to students who may have different learning styles. The use of such learning aids is proposed as a complementary teaching strategy. The approach taken in this research and its outcomes are significant because they continue to inform the research and educational communities about how human development may be fostered through engineering and technology education (Barak and Hacker, 2011)

The Telling Line: The Relationship between Cognitive Style and Fashion Design Sketching

This mixed-methods exploratory study addresses a gap in the literature by testing for links between cognitive style and the gestalt of sketches produced by college-level fashion design students. Students’ cognitive styles were appraised with the FourSight assessment, a measure of problem-solving preference gaining use in design schools. Then participants sketched fashion designs to complete a design brief. Panels of raters trained in FourSight reviewed the sketches to assess the cognitive styles of the sketchers. Quantitative analysis revealed a significant degree of interrater reliability, while qualitative analysis indicated emergent themes of selection, attitude, and innovation that aligned with FourSight types. The raters’ evaluations showed relationships between the sketches produced by fashion design students and the students’ cognitive style preferences, potentially affording designers additional insights in the problem-solving process. These findings support and extend FourSight theory and provide insights into the relationships between how people think and how they express their creativity through the concepts they produce

DESIGN THINKING: COGNITIVE PATTERNS IN ENGINEERING DESIGN DOCUMENTATION

Engineering design is an integral resource that on the surface uses creative, scientific, and process knowledge. Over the years many research driven improvements have been made to the methods and tools used for crafting the engineering design profession. Some progress has been made in exploring the cognitive processes, reading between the lines, and thinking about design thinking. This information is valuable to engineering designers in visualizing and performing the product development process. This dissertation is interdisciplinary in nature. The goal of this research is to apply cognitive research techniques to engineering design documentation to understand what happens in the mind during the design process. This research can be considered as an exploratory study of uncovering cognitive processes during design by developing a coding scheme that is applied to student and professional design journals. A successful cognitive coding scheme can be used in different domains and leads to development of new metrics for examining journal activities. This first study will enable future work aligned with the larger research goal of improving the understanding of design thinking. Engineering design documentation is one method of revealing insights into the mysteries of the mind. Design journals are used in this study combined with a Cognitive Coding Scheme created by the author to explore design thinking. This dissertation focuses on identifying patterns in cognitive behavior of engineering designers. Design documentation is also analyzed for insights on attitudes towards design journaling. This dissertation will make a contribution to the field of engineering design research by presenting a cognitive coding scheme capable of revealing insights into the mind of the designers

Sketching to Learn in High School Biology Classrooms

This pilot study tested the ability of the materials we created to elicit higher-order understanding and model-based reasoning (MBR) at three high schools. Participants completed iterative review sessions for an introductory biology topic, either through sketching or reading a text outline. After iterative review, participants responded to a single-question assessment. The question involved transfer of the information provided to students. The structure-behavior-function (SBF) coding structure used to analyze student answers distinguished levels of understanding in student responses (descriptive, explanatory, or integrative). However, grading written text responses alone did not provide adequate information to determine whether the student participants utilized MBR in developing their response. A later pilot or full study will utilize revised materials to continue to assess potential applications of sketching

How can neuroscience help understand design and craft activity? The promise of cognitive neuroscience in design studies

Peer reviewe