Design Creativity 2010 part 13 pdf

Conceptual Design and Cognitive Elements of Creativity: Toward Personalized Learning Supports for Design Creativity 109 5 Results and Discussion Forty four senior or first-year graduate students from the Interdisciplinary Design course at the Sungkyunkwan University participated in the experiment. Figure 3 shows examples of pre-test (b) and post-test (c) performed by a student. This example shows one sample case of the enhanced design creativity: The average score over the 5 cognitive elements of the post-test was increased by 1.05 from that of the pre-test. The assigned score range is between 1 and 5 (inclusive). Four domain experts evaluated the conceptual design results. The Cohen’s Kappa value was computed from the assigned scores for inter-rater reliability. The overall Kappa value was 0.44 over the five cognitive elements and the significance of the acquired Kappa value is “moderate agreement.” The individual Kappa values were 0.35, 0.66, 0.34, 0.47, and 0.39, respectively for flexibility, fluency, originality, elaboration, and problem sensitivity respectively. Fluency is considered as strongly reliable, compared to other cognitive elements. Table 3. Paired t-test result with p-values between pre-test and post-test data t p-value Fluency -4.103 0.000 Flexibility -3.197 0.003 Originality -5.367 0.000 Elaboration -0.604 0.549 Problem Sensitivity -0.623 0.537 5.1 Enhanced Design Creativity As a result, 31 students out of 44 students showed the enhanced design creativity with regard to the 5 cognitive elements (70% increases), possibly indicating the effectiveness of the creativity exercise a. Conceptual design task b. A sample of pre-test c. A sample of post-test Fig. 3. Cenceptual design task , and two samples of pre-test and post-test acquired from a student. Conceptual design task is used for pre-test and post-test. Note that both samples of pre-test and post-test were evaluated by human experts 110 Y.S. Kim, J.H. Shin and Y.K. Shin program. The overall difference between pre-test and post-test are +0.86, +0.32, +0.65, +0.06 and +0.06, respectively for Fluency, Flexibility, Originality, Elaboration and Problem Sensitivity. Further investigation with the t-test results provided us that there were 3 cognitive elements (Fluency, Flexibility and Originality) which are significantly different between pre-test and post-test, indicating the enhancements in the abilities of Fluency, Flexibility and Originality are statistically significant enough (Table 3). On the other hand, Elaboration (t=- 0.604, p<0.549) and Problem Sensitivity (t=-0.623, p<0.537) scores are not significantly different between Fig. 4. Affective modeling with eight emotion elements 5.2 Affective Modeling and its Relation with Enhanced Design Creativity In order to measure dynamic characteristics of students, and to investigate its relationships with the 5 cognitive elements, we incorporated affective modeling in the creative exercise program. In the context of computer-assisted learning context of creative design capabilities, affective modeling of learners is being done using self-reporting format. Affective elements composed of joy, acceptance, apprehension, distraction, sadness, boredom, annoyance, and anticipation were identified based on the basic emotion categories proposed by Plutchik (Plutchik, 2010), which were used in the affective modeling of the study. The online form of dialog representing all the affective elements was devised and presented to students so that the participants can select one or more affective states during the experiment. Note that the affection capture diagram uses identical icons so that other influences than affective state selection could be isolated in the interaction of the diagram and the users as the diagram pops up and prompts affective state selection. We conducted the online creativity exercise program with the affective model which is displayed to students for selections. The affective self-reporting was done after the learning objectives were given, after the specific problem statements were given and after the student problem sessions were done. While students conduct the exercise program, they are asked to self-report their affective states using an affective model diagram as shown in Figure 4. The collected affective states were used for the investigation of relationships with the 5 cognitive elements of design creativity. A machine learning technique, Association Rules learning was used for this purpose. Table 4 shows the enhanced design creativity and its relationships with affective states. For example, if there is enhanced design creativity (post test > pre test) then students did not select the affective states of “Sadness” and “Apprehend” (Support: 0.66 with Confidence: 0.9). Generally speaking, the enhanced design creativity is reversely associated with negative affective states; students did not select negative affective states when there was enhancement in design creativity in the post-test. Rapidminer 5.0 was used in the study for running Table 4. Association rules between cognitive elements and affective model elements Premises Conclusion Support Confidence Distract = false, POST TEST > PRE TEST Apprehend = false 0.62 0.92 Distract = false, POSE TEST > PRE TEST Sadness = false, Apprehend = false 0.62 0.92 Sadness = false, Distract = false, POST TEST > PRE TEST Apprehend = fasle 0.62 0.92 POST TEST > PRE TEST Apprehend = false 0.66 0.90 POST TEST > PRE TEST Sadness = false, Apprehend = false 0.66 0.90 pre-test and post-test. Conceptual Design and Cognitive Elements of Creativity: Toward Personalized Learning Supports for Design Creativity 111 machine learning techniques, such as Association Rules. 6 Conclusion In the study, we identified the cognitive components of design creativity and proposed a creativity exercise program for cognitive elements of design creativity. This program could be used in helping students considering their individual needs and contexts, and enhance design creativity. Five cognitive components of design creativity were identified, and those are fluency, flexibility, originality, elaboration and problem sensitivity. The proposed exercise program for design creativity was composed of five different tasks such as making stories, negation, filling black box, sensitization and diverse classification. In making stories, the students were required to produce several different stories by changing order of three different pictures. The aim of this task was to improve flexibility, originality and elaboration. The negation asked students to compulsively negate the given objects and contrive their alternate purpose or usage. Accordingly, the students’ flexibility, originality and problem sensitivity could be enhanced. In filling black box, the students were supposed to logically connect given input and output concepts in as many possible ways within a limited time, and as a result, the fluency could be improved. The sensitization asked students to express their feelings on the given physical objects and abstract concepts according to five different senses. With this task, the problem sensitivity could be enhanced primarily and flexibility secondarily. In diverse classification, the students were asked to classify the given objects in several different ways. Therefore, flexibility was developed and problem sensitivity developed secondarily. We conducted an experiment to investigate the effectiveness of the exercise program for design creativity cognitive elements. The results show that there was enhanced creativity, 31 students out of 44 students (70% increases) in terms of the cognitive elements, after students conducting the proposed creativity exercise program. Also, the machine learning results with affective model provided that there are relations between enhanced creativity in terms of cognitive elements and negative affective states, such as Sadness, Apprehend, Distract:. For example students did not select negative affective states when there was enhanced creativity. More rigorous approach is desired to examine what cognitive elements could be effectively addressed in each task. This is challenging research because of uncertain factors and qualitative measurement of data. However, the research efforts would be helpful for design creativity education by considering individual's needs and contexts. As a future work, thorough investigation of user data would be helpful in discovering meaningful results with regard to static and dynamic characteristics of user. Also, investigation of causal relationships between enhanced creativity, cognitive elements and affective states, using machine learning techniques such as Bayesian learning, will be important for the identification of factors causing the enhanced design creativity. References de Bono E, (1992) Serious Creativity. Hrper-Collins, London Goel V, (1995) Sketches of thought. Cambridge, MA: MIT Press Guilford JP, Hoepfner R, (1971) The Analysis of Intelligence. New York: McGraw-Hill Isaksen SG, Dorval KB, Treffinger DJ, (1998) Toolbox for Creative Problem Solving. Dubuque, IA: Kendall & Hunt Kim MH, Kim YS, Lee HS, Park JA, (2007) An underlying cognitive aspect of design creativity: Limited commitment mode control strategy. Design Studies 28(6):585–604 Kim YS, Jin ST, Lee SW, (2010) Relations between design activities and personal creativity modes, Journal of Engineering Design, in print Kim YS, Kim MH, Jin ST, (2005) Cognitive characteristics and design creativity: An experimental study. In Proceedings of the ASME International Conference on Design Theory and Methodology, Long Beach Kraft U, (2005) Unleashing creativity. Scientific American Mind 16(1):17–23 Park JA, Kim YS, (2007) Visual Reasoning and Design Processes. In Proceedings of International Conference on Engineering Design (ICED), Paris Plutchik R, (2010) The Nature of Emotions by Plutchik [Online], http://www.fractal.org/Bewustzijns-Besturings- Model/Nature-of-emotions.htm Treffinger DJ, (1980) Encouraging Creative Learning for the Gifted and Talented. Ventura, CA: Ventura County Schools/LTI Urban KK, (1995) Creativity-A component approach model. A paper presented at the 11th World Conference on the Education for the Gifted and Talented, Hong Kong Analogical Design Computing DANE: Fostering Creativity in and through Biologically Inspired Design Swaroop Vattam, Bryan Wiltgen, Michael Helms, Ashok K. Goel and Jeannette Yen Development of a Catalogue of Physical Laws and Effects Using SAPPhIRE Model Srinivasan V. and Amaresh Chakrabarti Measuring Semantic and Emotional Responses to Bio-inspired Design Jieun Kim, Carole Bouchard, Nadia Bianchi-Berthouze and Améziane Aoussat Design of Emotional and Creative Motion by Focusing on Rhythmic Features Kaori Yamada, Toshiharu Taura and Yukari Nagai DANE: Fostering Creativity in and through Biologically Inspired Design Swaroop Vattam 1 , Bryan Wiltgen 1 , Michael Helms 1,2 , Ashok K. Goel 1,2 , and Jeannette Yen 2 1 Design & Intelligence Laboratory at Georgia Institute of Technology, USA 2 Center for Biologically Inspired Design at Georgia Institute of Technology, USA Abstract. In this paper, we present an initial attempt at systemizing knowledge of biological systems from an engineering perspective. In particular, we describe an interactive knowledge-based design environment called DANE that uses the Structure-Behavior-Function (SBF) schema for capturing the functioning of biological systems. We present preliminary results from deploying DANE in an interdisciplinary class on biologically inspired design, indicating that designers found the SBF schema useful for conceptualizing complex systems. Keywords: Design Creativity, Computational Design, Biologically Inspired Design, Biomimetic design 1 Introduction Biologically inspired design uses analogies to biological systems to derive innovative solutions to difficult engineering problems (Benyus 1997; Vincent and Mann 2002). The paradigm attempts to leverage the billions of biological designs already existing in nature. Since biological designs often are robust, efficient, and multifunctional, the paradigm is rapidly gaining popularity with designers who need to produce innovative and/or environmentally sustainable designs. By now there is ample evidence that biologically inspired design has led to many innovative - novel, useful, sometimes even unexpected - designs (e.g., Bar-Cohen 2006; Bonser and Vincent 2007). Despite its many successes, the practice of biologically inspired design is largely ad hoc, with little systematization of either biological knowledge from a design perspective or of the design processes of analogical retrieval of biological knowledge and transfer to engineering problems. Thus, a challenge in research on design creativity is how to transform the promising paradigm of biologically inspired design into a principled methodology. This is a major challenge because biology and engineering have very different perspectives, methods and languages. We study biologically inspired design from the perspectives of artificial intelligence and cognitive science. From our perspective, analogy is a fundamental process of creativity and models are the basis of many analogies. Biologically inspired design is an almost ideal task for exploring and exploiting theories of modeling and model-based analogies. We have previously conducted and documented in situ studies of biologically inspired design (Helms, Vattam, and Goel 2009). We have also analyzed extended projects in biologically inspired design (Vattam, Helms, and Goel 2009). In this paper we describe the development and deployment of an interactive knowledge-based design environment called DANE, which was informed by our earlier cognitive studies and that is intended to support biologically inspired design. DANE (for Design by Analogy to Nature Engine) provides access to a design case library containing Structure-Behavior-Function (SBF) models of biological and engineering systems. It also allows the designer to author SBF models of new systems and enter them into the library. We present initial results from deploying DANE in a senior-level class on biologically inspired design in which teams of engineers and biologists worked on extended design projects (Yen et al 2010). The preliminary results indicate that although we had developed DANE largely as a design library, in its current state of development, designers found DANE more useful as a tool for conceptualizing biological systems. 2 Related Work Biologically inspired design as a design paradigm has recently attracted significant attention in research on design creativity, including conceptual analysis of biologically inspired design (e.g., Arciszewski and Cornell 2006; Lenau 2009; Lindermann and Gramann 2004), cognitive studies of biologically inspired design (e.g., Linsey, Markman and Woods 2008; Mak and Shu 2008), interactive knowledge-based design tools for supporting biologically inspired design (e.g., Chakrabarti et al. 2005, Sarkar and Chakrabarti 2008; Chiu and Shu 2007; Nagle et al. 2008), and courses on biologically inspired design (e.g., Bruck et al. 2007). 116 S. Vattam, B. Wiltgen, M. Helms, A. K. Goel, and J. Yen Our work on DANE shares three basic features of similar interactive design tools such as IDEA- INSPIRE (Chakrabarti et al. 2005, Sarkar and Chakrabarti 2008). Firstly, both IDEA-INSPIRE and DANE provide access to qualitative models of biological and engineering systems. Secondly, both IDEA-INSPIRE and DANE index and access the models of biological and engineering systems by their functions. Thirdly, both IDEA-INSPIRE and DANE use multimedia to present a model to the user including structured schema, text, photographs, diagrams, graphs, etc. However, our work on DANE differs from IDEA- INSPIRE and similar tools in three fundamental characteristics. Firstly, the design and development of DANE is based on our analysis of in situ cognitive studies of biologically inspired design (Helms, Vattam, and Goel 2009; Vattam, Helms and Goel 2009). Secondly, insofar as we know, IDEA-INSPIRE has been tested only with focus groups in laboratory settings. In contrast, we have introduced DANE into a biologically inspired design classroom. This is important because from Dunbar (2001) we know that the analogy-making behavior of humans in naturalistic and laboratory settings is quite different: in general, humans make more, and more interesting, analogies in their natural environments. Thirdly, while IDEA- INSPIRE uses SAPPhIRE functional models of biological and engineering systems, DANE uses Structure-Behavior-Function (SBF) modeling (Goel, Rugaber and Vattam 2009). This is important because SBF models were developed in AI research on design to support automated analogical design (e.g., Bhatta and Goel 1996, Goel and Bhatta 2004). Thus, in the long term it should be possible to add automated inferences to DANE. An SBF model of a complex system (1) specifies the structure, functions, and behaviors (i.e., the causal processes that result in the functions) of the system, (2) uses functions as indices to organize knowledge of behaviors and structures, (3) represents behavior as a series of states and state transitions that are annotated with causal explanations, (4) organizes the knowledge in F  B  F  B …  F(S) hierarchy, and (5) provides an ontology for representing structures, functions and behaviors. Other researchers have developed similar functional models e.g., Kitamura et al. 2004 and Umeda et al. 1996. 3 The Design By Analogy to Nature Engine In the long term, DANE is intended to semi-automate analogical retrieval and transfer in biologically inspired design. Presently, DANE interactively facilitates biologically inspired design by (1) helping designers find biological systems that might be relevant to a given engineering design problem, (2) aiding designers in understanding the functioning of biological systems so that they can extract, abstract and transfer the appropriate biological design principles to engineering design problems, and (3) enabling designers to construct and refine SBF models of biological and engineering systems. DANE employs a client-server architecture with a centralized design repository on the server-side. Each client is a thin client whereby all data is stored, updated, and recalled from the server. This architecture supports simultaneous access by multiple users and allows users to browse or edit the most current version of the repository. DANE is a distributed Java application running on the Glassfish application server. Data is stored in a MySQL database, and we use EJB technology to handle persistence and connection pooling. Users access the application by going to a launch website that utilizes Java Web Start to both download and execute the application as well as apply any updates that have been made since the user last launched the application. DANE’s library of SBF models of biological and engineering systems is growing. In early fall of 2009, when we introduced the system into a biologically inspired design classroom, the library contained about forty (40) SBF models, including twenty two (22) “complete” models of biological systems and subsystems. The remaining were either SBF models of engineering systems or only partial models of biological systems. Biological systems in DANE were at several levels of scale from the sub-cellular to organ function to organism. Systems are indexed by system-function pairs and retrieved by function name (e.g., “flamingo filter-feeds self”), by subject (e.g., “flamingo”), and/or by verb (e.g., “filter-feeds”). Function names often include additional specificity with regard to the objects upon which the function acts. In this case the flamingo is feeding itself. Upon selecting a system-function pair, users are presented with a multi-modal representation of the paired system-function (e.g. the “flamingo filter- feeds self” SBF model). For example, in DANE a system can be represented in text descriptions and images, as well as through visualizations of behavior and structure models. Example text and image modalities for the “flamingo filter-feeds self” model can be seen in Figure 1. Briefly, this model describes how a flamingo uses its tongue to create negative pressure in its slightly open mouth to draw water in, closes its mouth, and then uses its tongue to force the water out through a filter-system composed of comb-like lamellae and DANE: Fostering Creativity in and through Biologically Inspired Design 117 mesh. The lamellae trap the food, which is then drawn into the flamingo’s esophagus in the next cycle. Behavior and structure parts of the SBF models are themselves represented as directed graphs, which may be annotated with text descriptions and images. The nodes and edges represent either structural elements and connections (for structure models) or states and transitions (for behavior models), respectively. We provide an example of a partial behavior model, this time for the system “kidney filters blood,” in Figure 2. Note that the annotations on the transitions in this figure are labeled with short-hand that denotes their type: [FN] X identifies that a transition occurs because of some sub-function X, and [STR_CON] X Y identifies that a transition occurs because of the connection between some structural component X and another structural component Y. This “kidney filters blood” partial behavior model (a component of the larger SBF model) describes the movement of blood through the kidney through smaller and smaller vessels until the blood arrives at the nephron, where the filtration process takes place. Although in DANE the complete behavior model would be displayed, due to space constraints we only show in our figure a few states and transitions in this behavior. The sub-function “nephron purifies blood” serves as an index to yet another SBF model that describes this complex lower-level process in more detail. This provides an example of how SBF models are nested through function. Additionally, each system is visually connected to other systems with which it shares a sub or super- function relationship. This functional hierarchy is represented as an interactive graph with nodes representing systems and edges representing the sub/super relationships. Users may navigate between systems by double-clicking on a node. Figure 3 illustrates the functional hierarchy graph for the system “sliding filament model” and shows the browsing window with a few systems displayed, including the flamingo filter-feeding self function. The “sliding filament model” describes how muscle fibers contract, and thus the model is connected to a number of higher level animal functions (e.g. “flamingo filter- feeds self” and “basilisk lizard walks on water”), and is connected to a number of lower level molecular functions related to myosin and ATP. We can see in this one example how SBF models operate and connect functions at many scales. By presenting complex systems in the SBF schema, which places an emphasis on the causal relationships within each system, and by making explicit the function/sub-function relationships between systems, we hypothesize that biologists and engineers will understand the systems in a way that (a) helps them identify systems that are relevant to their design problem and (b) is transferable to a design solution. For example, an engineer might scan models in DANE until he/she comes across a system that has a similar initial and objective state (a function) that matches his/her design problem. Then, by inspecting the structure and behavior of that system, the engineer might formulate a technological solution that implements a similar set of behaviors. While SBF models can represent systems across multiple levels of scale and abstraction, and across the two domains of biology and engineering, the issue of knowledge engineering remains problematic. In Fi g . 1. Exam p le of a m ulti-modal m odel of a flamin g o’s filte r -feedin g a pp aratus in DANE 118 S. Vattam, B. Wiltgen, M. Helms, A. K. Goel, and J. Yen particular, we found that constructing a “complete” SBF model of a complex biological system requires between forty (40) and one hundred (100) hours of work. The process of understanding the biological system (e.g. the kidney), modeling it in the SBF language, discovering faults in the model or in the modeler’s understanding, and iterating over this process consumed a large majority of the time. We estimate that just entering a complete model into DANE required somewhat less than 25% of the overall time cost. 4 Application Context We deployed DANE in the Fall 2009 semester session of ME/ISyE/MSE/PTFe/BIOL 4803, a project-based, senior-level, undergraduate course taught by biology and engineering faculty affiliated with Georgia Tech’s Center for Biologically Inspired Design (Yen et al. 2010). The class composition too was interdisciplinary, comprising of 15 biology students, 11 mechanical engineering students, and 14 students from a variety of academic disciplines including biomedical engineering, chemical engineering, industrial engineering, material science, mathematics, and a few other engineering fields. The course has three components: lectures, found object exercises, and a semester-long biologically inspired design team project. In the design project, teams of 4-6 students were formed so that each team would have at least one biology student and students from different schools of engineering. Each team was given a broad problem in the domain of dynamic, adaptable, sustainable housing such as heating or energy use. Teams are expected to refine the problem and then design a biologically inspired solution based on one or more biological sources to solve it. All teams presented their final designs during the end of the class and submitted a final design report. The class is taught without any aids for design or research. Students are encouraged to perform their own research on biological systems through resources Fig. 3. List of functions and a functional hierarchy for “Sliding Filament Model” in DANE. Fig. 2. Partial behavior model of “Kidney filters blood“ in DANE . aspect of design creativity: Limited commitment mode control strategy. Design Studies 28(6):585–604 Kim YS, Jin ST, Lee SW, (2010) Relations between design activities and personal creativity. inspired design, indicating that designers found the SBF schema useful for conceptualizing complex systems. Keywords: Design Creativity, Computational Design, Biologically Inspired Design, . study, we identified the cognitive components of design creativity and proposed a creativity exercise program for cognitive elements of design creativity. This program could be used in helping