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

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

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

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 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 Vattam1, Bryan Wiltgen1, Michael Helms1,2, Ashok K Goel1,2, and Jeannette Yen2

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 super-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

Fig 1 Example of a multi-modal model of a flamingo’s filter-feeding apparatus 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