Art and Technology

A New Paradigm for Virtual Reality &
Simulations for Education, Learning &
Creating
The Virtual Trillium Trail Project – Empirical Evidence on Design
Factors in Simulated Ecological Environments for Education and
Causal Impacts on Emotions, Learning, Priming, Transfer, and
Reinforcement
November 14, 2016
Institute for Simulation and Training
Modeling and Simulation Seminar
Maria C. R. Harrington, Ph.D.
Assistant Professor of Digital Media
School of Visual Arts & Design
University of Central Florida
Maria.harrington@ucf.edu
Maria C. R. Harrington, Ph.D. | Assistant Professor of Digital Media | School of Visual Arts & Design | University of Central Florida | 11/14/2016
The Virtual Trillium Trail, 2007

Biography
 Maria C. R. Harrington’s experience spans over 20 years, from art
and economics to information science.
 Assistant Professor in the School of Visual Arts and Design (SVAD)
at the University of Central Florida
 Ph.D. and a M.S. in Information Sciences from the School of
Information Sciences, University of Pittsburgh.
 B.S. in Economics with a minor in Art from Carnegie Mellon
University.
 Professional experience includes financial, strategic, information technology,
and human-computer interaction activities for global corporations and
institutions such as: PNC Financial Services Group, The Canadian Imperial
Bank of Commerce, Fidelity Investments, University of Toronto,
Alias/Wavefront-Autodesk, DataViews–GE Fanuc, and Federated Investors.
 From 2000 to 2008, she was an Adjunct Professor in human computer
interaction, user centered design, human factors in system design, and
introduction to information science at the School of Information Sciences,
University of Pittsburgh. Additionally, she was an Adjunct Professor in the Art
& Design Division of Chatham University, and has been an Assistant
Professor in the Department of Computer Science at Slippery Rock
University.

Research Focus
 Harrington, Maria, C. R. (2008). Simulated Ecological
Environments for Education (SEEE): A Tripartite Model
Framework of HCI Design Parameters for Situational
Learning in Virtual Environments, Dissertation Abstracts
International. July 17,2008. University of Pittsburgh
 Understanding and modeling the dynamic interaction of the:
 Human
 System
 Environment

“Can Simulated Ecological Environments
of nature inspire independent exploration,
an intrinsic desire to learn and acts of
creation for the child?”

Seminar Objectives
Objective 1 – Introduce Simulated Ecological
Environments for Education (SEEE)
Objective 2 – Review of Pilot Study Empirical
Evidence in Real-Virtual and Virtual-Real
learning
Objective 3 – Review of Main Study Empirical
Evidence on Design Factors in SEEE
Q&A

What are Simulated Ecological
Environments for Education (SEEE)?
A New Paradigm for Virtual Reality &
Simulations for Education, Learning & Creating
A Virtual Field Trip that is a Simulation, not a
Game, not a Cartoon, not Fantasy
SEEE require three sets of data:
 Environmental: Scientific data sets visualized in a
photorealistic game engine (Unreal & Torque)
 Ethnographic Activity and UX: Informal learning
activities offered by credible organizations (National Parks,
Audubon, Museums, and Science Centers)
 Informational: Scientists, Teachers, Libraries, Public
School Curriculum (standards for facts and concepts)

The Virtual Trillium Trail, 2005-2010
https://www.youtube.com/watch?v=CMIhdzsOlTk

Why are SEEEs Important?
Virtual increases the ROI of Real (Virtual-
Real-Virtual)
Prime, Transfer & Reinforce
Virtual Reality (photorealist and free
navigation) increase Knowledge Gained
 The Virtual Trillium Trail resulted in 37.44%
increase on knowledge gained for facts and concepts
after one hour of free play.
Before any other design factors (visualization
augmentation, gamification, and intelligent
tutoring)

Why are SEEEs Important?
Visual Fidelity increases inquiry
(change in navigation behavior from
terrestrial to informational)
We can design inquiry, important for
STEM education
Beauty correlated with Awe and Wonder
correlated with Learning, important for
STEAM as it impacts STEM

System & Research Design Matters
 The system is an artificial artifact, like a painting, it can be an
abstraction or it can be realistic
 If designed to be realistic, the Virtual can be used for
research into perception, feelings, behavior, and learning as
well as sharing and creating that can be extended back into
the Real
 Supports Real-Virtual and Virtual-Real comparison
 Counterbalanced, within-subject ANOVA
 One System used to Isolate, Control, and Measure outcomes
 Planned orthogonal contrast, and control over input factors --
Visual Fidelity (scaled from low to high levels of graphics quality),
and Navigational Freedom, (scaled from low to high levels of
user navigational style and freedom of choice).
 Rigorous statistical design (2X2 ANOVA), and to measure
outcomes with confidence.

Multidisciplinary Research
Research Ambient Perceptual-Semantic Array
and impacts on Perception, Emotions, and Creativity as
Action (AI of Creativity)
UX and Technical Design Frameworks for Collaboration
& Co-Creativity (frameworks that support work)
especially for scientists and educators, Creators
Dashboard
Landscapes as Art and Virtual Reality Experiences (Art
as Information and Emotion), to Extend Mathematical
Models of Communication to Understand the Role
of Beauty

Art, Science, and Technology
How to Create?
How to Frame?
How to Design?
How to Build?

Start with Observation

UX Design & Pilot Study
Examine the feasibility of an approach
Ethnographic work to document task analysis,
context, contextual inquiry, tools, and information
requirements
UCD to discover system design parameters
Develop a system prototype to solve technical
problems
Not looking to test hypothesis, but looking for
ideas, patterns in the data, evaluation relative to
established results in literature

Ethnographic Field Work

Automatic Terrain Generation, 2007
• USGS data (DEM files) of location to export
• Imported data to ESRI, transformed it to a grayscale
• Imported grayscale into Unreal, to generate terrain

Plant Plots Extrapolated
Retrieved from the web 11/6/2016:
http://kaliszlab.weebly.com/
The Virtual Trillium Trail application was developed
between 2005 and 2008. The first 2007 prototype ran on a
Dell XPS Gen 2 laptop and required a high-end NVIDIA
GeForce Go 6800 Ultra graphics card.

Automatic Plant Distribution

Photorealistic 3D Models
The Virtual Trillium Trail, 2007

Facts and Concepts Imbedded

Annotate with Story

Educational Content

Two Research Studies
Pilot -- Study One: Real vs. Virtual
 Baseline Control (n=12)*
 A one-way, within-subject ANOVA, with repeated
measurements, (n=12)
Main -- Study Two: 2x2 ANOVA
 Planed Orthogonal Contrast (n=64)
 Factor 1: Visual Fidelity (High & Low)
 Factor 2: Navigational Freedom (High & Low)
 One system with four condition states

Review of Pilot Study
Trillium Trail May 2007 and University of Pittsburgh May 2007

Framing the Design
 12 volunteer students from a local suburban public elementary
school. (Yes, this is problem)
 However, in-situ activity of map annotations is high (n = 85) and
post experience attitudes reported are sufficient (n = 28)
 Environment and Order - Real and Virtual Comparisons
 One-way, within-subject ANOVA, with repeated measures in
counterbalance design
 Each subject acted as his/her own control
 Learning activity – counts per cell (Each student’s in situ map
annotations)
 Transfer of Learning Activity
 Group 1 experiencing the Real-Virtual order
 Group 2 experiencing the Virtual-Real order
 Show transfer of learning activity in each direction
 Attitudes on Environments
 Post experience attitudinal survey that allowed direct comparison and contrast
of Real and Virtual

Study One Design
Harrington, Maria, C. R. (2011). Empirical Evidence of Priming, Transfer, Reinforcement, and Learning in the Real
and Virtual Trillium Trails. IEEE Transactions on Learning Technologies 2011 vol.4 Issue No.02 – April-June pp: 175-
186 [DOI: http://doi.ieeecomputersociety.org/10.1109/TLT.2010.20 | Research Gate | PDF ]

Study One Results
Harrington, Maria, C. R. (2011). Empirical Evidence of Priming, Transfer, Reinforcement, and Learning in the Real
and Virtual Trillium Trails. IEEE Transactions on Learning Technologies 2011 vol.4 Issue No.02 – April-June pp: 175-
186 [DOI: http://doi.ieeecomputersociety.org/10.1109/TLT.2010.20 | Research Gate | PDF ]

Environment Main Effect -Total Objects
 Total Objects: Real has more information than the
Virtual, results in higher learning activity:
 H1a: Mean Total Activity (Real) > Mean Total Activity (Virtual)
 Real (M = 4.5, SD = 2.71)
 Virtual (M = 2.83, SD = 3.43)
 F(1,11) = 4.68, p = 0.05

Environment Main Effect - Plant-Only
 Plant Only Objects: When Content in the Virtual is Identical to
the Real, there is no difference in learning activity
 H2o: Mean Plant-Only Activity(Real) = Mean Plant-Only Activity(Virtual)
 Real (M = 2.75, SD = 1.96)
 Virtual (M = 2.83, SD = 3.43)
 F(1,11) = 0.00, p = 0.95

Order Main Effect
 Second Field Trip, Real or Virtual, shows more Learning
Activity -- Practice Makes Perfect!
 H3a: Mean First Experience < Mean Second Experience
 Frist Total (M = 1.58, SD = 2.53)
 Second Total (M = 5.75, SD = 2.18)
 F(1,11) = 16.23, p = 0.002
 First Plant-Only (M = 0.75, SD = 1.42)
 Second Plant-Only (M = 4.82, SD = 2.12)
 F(1,11) = 49.00, p < 0.000

Virtual to Real Transfer for Total
 If there is no transfer of skill, in situ learning activity should be the
same. If there is a significant difference, then there is evidence of
transfer.
 The t-test for two independent groups (one-tail, post-hoc analysis)
was carried out to help explain the results.
 H4a: Total Activity: Mean Real (First) < Mean Real (After Virtual)
 Real (First) (M = 2.8, SD = 2.6)
 Real (After Virtual) (M = 5.8, SD = 1.9)
 t = -2.29, df = 10, p = 0.023 (one-tailed, α = 0.01)
 Prime the Real with the Virtual, as the mean value significantly
increased.

Virtual to Real Transfer for Plant
 If there is no transfer of skill, in situ learning activity should be the
same. If there is a significant difference, then there is evidence of
transfer.
 H6a: Plant-Only Activity: Mean Real (First) < Mean Real (After Virtual)
 Real (First) (M = 1.5, SD = 1.8)
 Real (After Virtual) (M = 4.0, SD = 1.26)
 t = -2.83, df = 10, p = 0.000 (one-tailed, α = 0.01)
 Prime the Real with the Virtual, as the mean value significantly
increased.

Real to Virtual Transfer for Total
 In the past, such an analysis was impossible for life-critical training -
- combat military training. Educational and learning applications, the
question is valid and viable.
 H5a: Total Activity: Mean Virtual (First) < Mean Virtual(After Real)
 Virtual (First) (M = 0.0, SD = 0.0)
 Virtual (After Real) (M = 3.8, SD = 2.4)
 t = -3.88, df = 10, p = 0.000 (one-tailed, α = 0.01)
 Reinforce the Real with the Virtual, as the mean value
significantly increased.

Real to Virtual Transfer for Plant
 In the past, such an analysis was impossible for life-critical training -
- combat military training. Educational and learning applications, the
question is valid and viable.
 H7a: Plant-Only Activity: Mean Virtual (First) < Mean Virtual(After Real)
 Virtual (First) (M = 0.0, SD = 0.0)
 Virtual (After Real) (M = 5.67 SD = 2.58)
 t = -5.38, df = 10, p = 0.000 (one-tailed, α = 0.01)
 Reinforce the Real with the Virtual, as the mean value
significantly increased.

Interaction?
Interaction? (Total Map Annotations by Group)
Total Activity: Interaction Groups x Order
 F(1, 5) = 30.69, p = 0.003
Thus, some factor is effecting the variables in a
non-constant way?
Something in the real world is having an interesting
impact

Environmental Salience - The
Salamander Effect as a “Salient Event”
 Some probability distribution of events that occur only in the
real environment, and result in highly salient, personally
meaningful recordings and memories, “Salient Events”
 Episodic “Teachable Moments” are critical design
features.

Results for Attitudes on Environments

Correlations
 Significant correlation between Beauty and Learning
Activity in the Virtual (Spearman Rank Order
Coefficient, r = 0.76, p = 0.00) was found, but this was not
found in the Real (Spearman Rank Order Coefficient, r =
0.00, p = 1.0), even though Beauty ranked identically in both
Environments
Presence and Beauty in the Virtual (Spearman
Rank Order Coefficient, r = 0.64, p = 0:03), a correlation not
found in the Real (Spearman Rank Order Coefficient, r =
0.49, p = 0.11)
 These results suggest Complex Interaction between
emotion, perception, and learning in Real and Virtual spaces,
interactions that are worthy of future analysis and research

Take away # 1 (Information Fusion)*
 The Real Environment shows more learning activity than the Virtual
Environment. The Salamander sighting was only observed in the Real, an
event that was outside of the simulation, not in the curriculum, yet impacted
learning of the curriculum in some way.
 When content in the Virtual matches the Real, the learning outcomes
are the same.
 There is evidence of Priming, Transfer, and Reinforcement, thus
indicating that the best practice is to use the Virtual first to prime, transfer to
the Real where the most learning occurs, and then reinforce with the Virtual,
for the highest overall learning gains and best ROI value.
 Environmental Salient Events trigger a Teachable Moment, if used,
leverage Episodic Memory to anchor entire curriculum

How to Design Such Systems?
 How to use Virtual Reality for Education?
 Virtual Reality as a Simulation to match the Real Field Trip?
 What are the UX and software design factors and what are
their impacts?
“Can Simulated Ecological Environments
of nature inspire independent exploration,
an intrinsic desire to learn and acts of
creation for the child?”

Review of Main Study --Empirical
Evidence on Design Factors in SEEE–
Study Two
Harrington, Maria, C. R. (2008). Simulated Ecological Environments for Education (SEEE): A Tripartite Model Framework of
HCI Design Parameters for Situational Learning in Virtual Environments, Dissertation Abstracts International. July 17,2008.
University of Pittsburgh

Research Questions
 What are the design parameters of these information
systems
 How do they impact learning and usability?
 What are the subjective emotional reactions to those
factors
 Impact user experience, usage rates, and effectiveness
 Can we fit data to, and design a priori empirical models to
forecast the effectiveness of such systems prior to
construction?

Framing the Design
One System used to Isolate, Control, and
Measure outcomes
Planned orthogonal contrast, and control over
input factors -- Visual Fidelity (scaled from low to
high levels of graphics quality), and Navigational
Freedom, (scaled from low to high levels of user
navigational style and freedom of choice).
Rigorous statistical design (2X2 ANOVA), and to
measure outcomes with confidence.

Study Two : 2x2 ANOVA
Independent Variables :
Visual Fidelity: Two Levels: Low (LF) and High (HF)
Navigational Freedom: Two Levels: Low (LN) and High (HN)
Dependent Variables :
Knowledge Gained
Salient Events
Fact Cards Inquired
Time in System
Emotional Reactions
Acts of Creation
Visual Fidelity
NavigationalFreedom Low Visual Fidelity
(LF)
High Visual Fidelity
(HF)
Low
Navigational
Freedom
(LN)
High
Navigational
Freedom
(HN)
Group LFHN
n=(16)
Group HFHN
n=(16)
Group HFLN
n=(16)
Group LFLN
n=(16)

Study Two Design
64 Volunteers, given a demographic survey, pre-test, randomly assigned to
one of the 4 conditions, then a post-test, microworld study, and a post-
experience attitude survey

Hypotheses
 The Two-way ANOVA tests main effects and interaction
effects for all variables under investigation in one system, in
a controlled Planned Orthogonal Contrast.
 HF= High Fidelity Condition
 LF = Low Fidelity Condition
 HN = High Navigational Freedom Condition
 LN = Low Navigational Freedom Condition
 For each of the Dependent Variables (DV): Salient Events,
Fact Inquiry, Time in System, and Knowledge Gain, three
statistical tests were possible for each of the Conditions
 H01: μ DVHF = μ DVLF
 Ha1: μ DVHF ≠ μ DVLF
 H02: μ DVHN = μ DVLN
 Ha2: μ DVHN ≠ μ DVLN
 H03: No Interaction
 Ha3: Interaction

Results on Learning *
Knowledge Gained (Test Score Gains)
• Visual Fidelity Significantly Impacts
 High Visual Fidelity (Μ = 30.95, SD = 14.76)
 Low Visual Fidelity (Μ =19.99, SD = 13.39)
 F(1,60) = 10.54, p = 0.0019 (p < 0.05)
• Navigational Freedom Trends an Impact
 High Navigational Freedom (Μ = 28.24, SD = 16.51)
 Low Navigation Freedom (Μ =22.69, SD =13.06)
 F(1,60) = 2.71, p = 0.105
• Strong and Significant Evidence of Interaction between Visual Fidelity
x Navigational Freedom
 F(1, 60) = 4.85, p = 0.0315 (p < 0.05)
 Highest Learning Gains on tests in the Condition with Both High
Fidelity and High Navigational Freedom (M = 37.44%, SD = 13.88)

Results on Learning Activity *
Salient Events (Evidence of Inquiry)
• Visual Fidelity Significantly Impacts
 High Visual Fidelity (Μ = 14.46, SD = 6)
 Low Visual Fidelity (Μ =11.31, SD = 6.37)
 F(1,60) = 4.35, p = 0.00413 (p < 0.05)
• Navigational Freedom Strong Trend Impact
 High Navigational Freedom (Μ = 14.25, SD = 6.99)
 Low Navigation Freedom (Μ =11.53, SD = 5.38)
 F(1,60) = 3.23, p = 0.0773
• No Evidence of Interaction between Visual Fidelity x Navigational
Freedom
 F(1,60) = 1.48, p = 0.2285
 Highest Rates of Inquiry in the Condition with Both High Fidelity
and High Navigational Freedom (M = 16.75, SD = 6.27)

Both Visual Fidelity and Navigational
Freedom required for highest learning
outcomes on Tests
 Visual Fidelity and Navigational Freedom and as Design
Factors:
 There is significant interaction, F(1,60) = 4.85, p = 0.0315,
between Visual Fidelity and Navigational Freedom on Knowledge
Gained, so both factors must be in the design for such
results
 The combined conditions of High Visual Fidelity x High
Navigational Freedom result in far superior Knowledge Gained
on tests, (cell mean = 37.44, SD = 13.88) when compared to Low
Visual Fidelity x Low Navigational Freedom condition, (cell mean
= 20.93, SD = 13.36)
 Visual Fidelity is strong and significant F(1,60) = 10.54, p =
0.0019
 Navigational Freedom shows a trend, F(1.60) = 2.71, p = 0.105

Visual Fidelity Impacts Behavior --
Exploration and Inquiry Learning Activity
 There is no interaction, F(1,60) = 1.48, p = 0.2285), between Visual
Fidelity and Navigational Freedom, on Salient Events.
 Salient Events measure the number of times student behavior changes
from exploration to deep inquiry.
 Visual Fidelity is strong and significant, F(1,60) = 4.35, p = 0.00413). It
alone, is responsible for significantly increasing learning activity.
 Navigational Freedom, as a factor, shows a strong trend, F(1,60) =
3.23, p = 0.0773. The data show that the High Visual Fidelity condition
(Row Mean = 14.46, SD = 6) resulted in more Salient Event counts than
did the Low Visual Fidelity condition (Row Mean=11.31, SD = 6.37).
 The more a virtual reality environment, simulation, or serious game
looks high fidelity and photo-realistic, the more times a child’s behavior
will change from exploration to inquiry. Thus, Visual Fidelity increases a
child’s desire to learn, to understand, and to stop in order to
independently and actively inquire.

Emotions
 Spearman's rho Correlations (N=64) Sig(2-tail)
 This shows the correlation and relationships between Beauty,
whatever it may be for the individual, the emotional reaction
of Awe and Wonder, and the empirical data on test scores,
Knowledge Gained
 Knowledge Gained is correlated to Awe and Wonder
 rho = 0.273, p = 0.032
 Beauty correlated with Awe and Wonder
 rho =0.506, p =0.000
 Beauty correlated with Total Attitudinal Survey ranking
 rho = 0.727, p = 0.000
 A major contribution shows empirical link between
Beauty and Learning.

“Empirical link between
Beauty and Learning”
~ Maria C. R. Harrington, Ph.D.

Take away # 1 (Information Fusion)
 The Real Environment shows more learning activity than the Virtual
Environment. The Salamander sighting was only observed in the Real, an
event that was outside of the simulation, not in the curriculum, yet impacted
learning of the curriculum in some way.
 When content in the Virtual matches the Real, the learning outcomes
are the same.
 There is evidence of Priming, Transfer, and Reinforcement, thus
indicating that the best practice is to use the Virtual first to prime, transfer to
the Real where the most learning occurs, and then reinforce with the Virtual,
for the highest overall learning gains and best ROI value.
 Environmental Salient Events trigger a Teachable Moment, if used,
leverage Episodic Memory to anchor entire curriculum

Take Away # 2
There is statistically strong and significant
interaction, F (1, 60) = 4.85, p = 0.0315, between
Visual Fidelity, as a design factor, and Navigational
Freedom, as a design factor, on Knowledge
Gained, (pre-posttest differences) thus proving that
both of these factors must be present to have the
greatest impact on learning.
The Virtual Trillium Trail resulted in 37.44%
increase on knowledge gained for facts and
concepts after one hour of free play.

Take Away # 3
 There is no interaction, F (1, 60) = 1.48, p = 0.2285, between
the two factors for Salient Events, (a measurement and count
of the changes in student navigational behavior from
terrestrial exploration to deep inquiry of information).
 The main effect of Visual Fidelity is statistically strong and
significant, F (1, 60) = 4.35, p = 0.00413, and thus, is a
critical software design factor to use to increase the
probability of learning activity. Navigational Freedom, as a
factor, shows a strong trend, F (1, 60) = 3.23, p = 0.0773.
 Implying that, "scientific inquiry," can be "designed" to have a
higher probability of occurrence in realistic simulations (High
Visual Fidelity), important for STEM educational applications.

Take Away # 4
 Interesting correlations between Beauty, Awe and Wonder,
Curiosity, Inquiry, and Knowledge Gained are present in the
data, so are high affective ratings of Desire to Share and
Desire to Create, thus hinting at the role the environment
(Real or Virtual) has on feelings, behavior, and emotion.
 Significant correlation between Beauty and learning activity in
the Virtual was found (Spearman Rank Order Coefficient, r =
0.76, p = 0.00), important for STEAM applications as they
relate to STEM education.

Conclusions*
 Real is better than Virtual, use Virtual to Prime, Transfer to
Real, then Virtual again to Reinforce
 Use Virtual-Environmental Salient Events, to open Teachable
Moments, and anchor Curriculum in Episodic Memory
 Salient Events become a software design feature desired in
the Virtual to increase learning activity
 Use combined Visual Fidelity (Photorealistic VR) and
Navigational Freedom (Free Choice) to increase Knowledge
Gained on tests
 Use Visual Fidelity (Photorealistic VR) to increase Salient
Events and change behavior from Exploration to Inquiry
 Use Beauty to spark a causal chain of reactions -- Awe and
Wonder, Inquiry, Learning, Desire to Share, & Desire to
Create

Questions?
 Invitation to collaborate? It starts with trust!

Cave Art or Training Simulator?
Lascaux Caves
France nearly 20,000 years
ago
Retrieved from the web
11/3/2016:
https://en.wikipedia.org/wiki/La
scaux

Framing Future Possibilities
 Connection between external world and internal feelings and
thoughts
 Art is the non-technical VR of the past
 It communicated information
 It changed what we knew
 It changed how we felt
 It moved us to take action
 VR is the 21st Century Art of impact if used as such
 A cultural artifact of high information density
 A new form of stories, theater, and opera
 A way to communicate brilliant ideas, vision, and beauty

What is Art?
Art is Beauty
Art is Information
Art is Story
Art is Paradox
Great Art Transcends Time…
and Culture

We know it when we see it
Sandro Botticelli. The Birth of Venus (Detail) 1480. Uffizi Gallery. Retrieved from
https://commons.wikimedia.org/wiki/File:The_Birth_of_Venus_%28Botticelli%29_detail.jpg
What do you see?
What is her
emotion?
What do you feel?

1452
Jean Fouquet. Virgin and Child Surrounded by Angels, Melun Diptych, (Right wing of the diptych), (1452). Royal
Museum of Fine Arts, Antwerp
Retrieved from https://en.wikipedia.org/wiki/Melun_Diptych#/media/File:Fouquet_Madonna.jpg

Undisputed
Leonardo da Vinci. Mona Lisa (1503-
1506). Louvre Museum. Retrieved from
https://commons.wikimedia.org/wiki/File:
Mona_Lisa,_by_Leonardo_da_Vinci,_fr
om_C2RMF_retouched.jpg

What is Technology?
 Tools applied to art or science as
a means to an end
 A collection of techniques, skills,
methods, and processes used for
artistic expression or in scientific
investigation
 Computers are used as tools, as
a kind of technology to achieve
goals in calculation or expression
Pedro Reyes, Imagine (Double Psaltery), 2012.
Retrieved from http://www.blog.pedroreyes.net/

Camera Obscura
Camera obscura, from a manuscript of military designs. 17th century, possibly Italian. Retrieved from
https://en.wikipedia.org/wiki/Camera_obscura#/media/File:Camera_obscura2.jpg

Game Engines

3D Models
 3D Model, 2D Art, joints rigged for motion and animation
 Character specific behaviors, expressions, or reactions
 UV mapping (https://en.wikipedia.org/wiki/UV_mapping)
http://www.turbosquid.com/3d-models/deer-animation-3d-model/542770

2D Art, Textures & Skins
https://www.adobe.com/products/photoshop.html

Rigged & Animated
 http://www.turbosquid.com/3d-models/deer-animation-3d-
model/542770

Open World Demo
https://www.youtube.com/watch?v=clakekAHQx0

Are Video Games ART?
https://www.youtube.com/watch?v=EuZdHTKzXa8
Is a Hot Dog a Sandwich?

VR as Art -- Char Davies. Osmose
(1995)
Retrieved from https://www.youtube.com/watch?v=54O4VP3tCoY

The Garden of Earthly Delights by
Hieronymus Bosch in VR – Art?
Burrell Durrant Hifle website (2016, March 16). VR Bosch. Retrieved from
http://www.bdh.net/work/boschvr/

Walk into a Dali with VR – Art?
The Dali Museum (2016, January 21). Dreams of Dali: Virtual Reality
Experience. Retrieved from https://www.youtube.com/watch?v=2nh1itve0AQ
The Dalí Museum in
St. Petersburg,
Florida, or at
dreamsofdali.org.
Opens January 23,
2016.
Go inside and
beyond Dalí’s
painting
Archeological
Reminiscence of
Millet’s Angelus
Experience Dreams
of Dalí in the special
exhibit Disney &
Dalí: Architects of
the Imagination

Artistic Experience in VR
http://vrse.com/

Future Research and Teaching

Future possibilities for research
and teaching
 Art & Science of Virtual Reality for Education (Virtual Field
Trips for K-12)
 Human Factors Research on the Ambient Array and impacts
on Perception, Emotions, and Creativity as Action (AI of
Creativity)
 Design Frameworks for Collaboration & Co-Creativity
(frameworks that support work)
 Future Cities, Space Stations, and Colonies co-designed and
tested in Virtual Reality (Scenario analysis & Emergent
Design & Consensus decision-making)
 Future Realities, Simulations, Data Visualizations
(Algorithms)
 Landscapes as Art and Virtual Reality Experiences (ART as
Information and Emotion)

SEEE -- User Centered Design, Human
Computer Interaction, UX of VR

Art & Science of Virtual Reality for
Education
 Virtual Field Trips for K-12

Ambient Perceptual Semantic Array
Human Factors Research on Spatial Ambient Array and
impacts on Perception, Emotions, and Creativity as
Action (AI of Creativity)
James J. Gibson, Ph.D. Ecological Psychology
 Visual perception
 Extend his idea of an Ambient optical array into meaning
Extend to creativity research
 How do we know we don’t know?
 How do we perceive the unknown?
 How do we create something new?
 What is Beauty and how does that trigger inquiry and creativity?

Measure Psychophysical

Measure the Geometry

Design Frameworks for
Collaboration & Co-Creativity
 Frameworks that support co-creativity
 3 large IRB studies: Investigation and Evaluation of
Virtual Worlds, 3D Environments, and Simulations for
Alternative Learning, Collaboration, Co-creating, and
Decision Support Formats:

How to use Virtual
Reality to co-create the
Future?

Pittsburgh Renaissance
© 2016 University Library System, University of Pittsburgh, “Frank Lloyd Wright’s concept for Point redevelopment,1947.”
Retrieved from http://digital.library.pitt.edu/pittsburgh/exhibits/thepoint/

Future Cities, Space Stations, and
Colonies in VR
NASA website (2016, January 16) Research & Technology Images. Retrieved
from https://www.nasa.gov/content/research-and-technology-image-gallery
Space-grant
University:
Founded in
1963, UCF
opened to
provide
personnel to
support the U.S.
space program
at the Kennedy
Space Center
and Cape
Canaveral Air
Force Station
on Florida's
Space Coast

NASA Concept Art to communicate
ideas, model, and inspire
NASA Ames summer
studies in the 1970s.
Colonies housing
about 10,000 people
were designed. A
number of artistic
renderings of the
concepts were made.
Bryan Yager, Toroidal Colonies, NASA ID NUMBER AC75-1086-1. Retrieved from
http://settlement.arc.nasa.gov/70sArt/art.html

Rama, by Eric Bruneton
3D animation of a
spacecraft freely
inspired from the
book "Rendez-vous
with Rama" from
Arthur C. Clarke.
Making of Rama:
http://www.youtube.c
om/watch?v=wKM2n
D...
See also:
http://ebruneton.free.
fr/rama3/rama.html
Bruneton, Eric. (2009, July 19) Rama. Retrieved from
https://www.youtube.com/watch?v=zBIQCm54dfY

Virtual Reality in the
hands of an Artist

Intersection of Art and Technology
 I create immersive, virtual landscapes that are dynamic and
responsive to past and future scientific data sets, in addition to
user interaction scenarios
 These tools offer a new medium and process for the artist to
explore and harness, resulting in new cultural artifacts relevant to
our era
 My artwork is an investigation of landscapes, much like the
Hudson River School of the past, Clayton Merrill and Clifford
Ross of the present
 It hints at a powerful connection between art and technology, the
land and the self – artifact form and function, internal mind and
external environment

Hudson River School
Thomas Cole. The Oxbow (The Connecticut River near Northampton) (1836)

Clayton Merrill
Fire and Sun Smoke, 2012

Clifford Ross
Clifford Ross, "Sopris Wall I," 2015 (Installation view) MASS MoCA

Artistic Statement
The intent of my art is to present
landscapes in different perspectives to
viewers. By doing so, I aim to
challenge them, intellectually and
emotionally, to reflect on their
personal relationships with nature and
with themselves, within the ideals of
truth and beauty.

Artistic Statement
My landscapes intentionally frame
views with impossible, almost
surrealistic perspectives that are
completely unreal, yet made real by
the visualization, and application of
simulation and virtual reality
technology.

Artistic Statement
Placing the viewer into a frame of
reference of the earth—using anything
from a leafy tree to a rippling river—
the viewer is cast in an impossible
role. But it feels natural because it is
perceived as real as the real world,
and thus very familiar.

Future possibilities for research
and teaching with Art
Human Factors Research on Ambient Perceptual
Semantic Array and impacts on Perception, Emotions,
and Creativity as Action (AI of Creativity)
Design Frameworks for Collaboration & Co-Creativity
(frameworks that support work)
Landscapes as Art and Virtual Reality Experiences (ART
as Information and Emotion), Extend Shannon and
Weaver’s Model of Communication to Beauty

What are you going to Create?

Art and Technology

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