DVL Overview - EDUCAUSE.edu

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Interdisciplinary Skills for the
21st Century Learner
EDUCAUSE – May 6, 2008
John Gibson, Oris Friesen, & Florence Martin
Maricopa Community Colleges, Arizona
Agenda
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•
•
•
•
•
Project Origins
Introduction to DVL
DVL Modules
Instructional Design & Assessment
Excel Module Example
YOUR Ideas: DVL’s Potential Uses?
2
Project Origins
• 3-year, $600,000 National Science Foundation
Advanced Technological Education Grant
• Original Partnership between Brown University &
Maricopa Community Colleges
• Final Year – Focus on Dissemination
• GOAL: Introduce Digital Visual Literacy modules
into introductory community college courses:
–
–
–
–
Educate teachers
Use DVL modules in existing courses
Perform assessment
Disseminate results
3
What is DVL?
4
•
DVL is a set of vital interdisciplinary skills that
enable students and teachers to function in an
increasingly digital and visual workplace
•
DVL builds on previous Visual Literacy efforts,
but integrates new research material, as well
•
Examples:
• Visual Language: Global Communication for the 21st Century
(Robert E. Horn, 1999)
• Visual Thinking (Rudolf Arnheim, 2004)
• Visual Literacy: Learn to See, See to Learn (Lynell Burmark,
2002)
What Interdisciplinary Skills?
•
The ability to…
1. Critically evaluate digital visual materials (2D
and 3D, static and moving)
2. Make decisions using digital visual
representations of data and ideas
3. Use computers to create effective visual
communications
• Across the curriculum!
5
6
Why? Images Are Everywhere!
• Graphical interfaces make easy
computing possible
• Photorealistic CGI (computer
generated images) for movies
and simulations
• Large data sets can be
visualized (weather, etc.)
• Visualization lets doctors look
inside your body
• Industrial design depends on
CAD (computer-aided design)
• Simulations affect most areas of
science, from nanotechnology
to biology and beyond
A Historic Trend
• Images used to be displayed
in only a few places, such as
churches, but are now
ubiquitous
• Television was introduced
only 60 years ago
• By HS graduation US
children will have spent
more time in front of the TV
than in the classroom (AACAP)
7
Computer Technology
Has Accelerated this Change
1900: Kodak’s “brownie” camera
1900s (early): first movie theaters
1980s bulky, low quality VCR a
“wow” item
1984: ATT breakup
1990: color printers ~$10K
19th century studio camera, with bellows for focusing
8
2006: Kodak sells no film cameras
2006: desktop movie making
2006 tiny iPOD has HD video
2006: cell phones have megapixel+
camera, video, even scanners
2006: given as bank “gifts”
Bell Howell Keychain Digital Camera
9
Skill Sets Are Changing
• You can’t believe everything you see
• Role of design in business is widely recognized
• Accelerating demand for graduates with visual,
holistic thinking
Daniel Pink’s
agriculture to
industrial age
to knowledge
work to age
of conceptual
thinking
From “A Whole New Mind: Why Right-Brainers Will Rule The Future” by Daniel Pink, 2006
Profound Implications…
“If students aren’t taught the language of …
images, shouldn’t they be considered as
illiterate as if they left college without being
able to read or write?” (George Lucas)
Think about the “literacy” we teach…
Convergence, 21st Century Skills, &
“Complex Tacit Interactions”
Is the 4th R a V?
10
11
An Integration of Disciplines!
• Academics
– Sciences
– Humanities
– Business
• Application areas
(medicine, military,
etc.)
• Little conversation
between practitioners
in these fields
Contributing Disciplines
12
1. Visual Culture: Critical discussion of visual materials
and history, including topics in art history, media culture,
visual history, and philosophy.
2. Art and Design: Basic concepts in 2D, 3D, and time3.
based visual art and design
Vision Science: Basic concepts in neuroscience,
perception, and cognitive science
4. Computer Graphics and Visualization (CS):
5.
Basic concepts in the science of computer graphics
Image Economy: The economic implications of
creating, distributing, purchasing, exchanging visual
(largely digital) materials
Best Delivered As “Modules”
• FREE!!!
• Strengthens Textbooks
– with supplementary materials
•
•
•
•
•
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Complete Instructional Design
Small, flexible, & tested
Downloadable from website
You can adapt original files!
Can be used in any course
12 modules a beginning…
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14
Beginning Modules
Introduction to Digital Visual Literacy
Anne Spalter – Brown
University
Practical Visual Copyright
John Gibson – Glendale
Community College
Visual Rhetoric for Blog
Janet Brooks – Glendale
Community College
Visual Dialog in Ecommerce
Linda Offenberg –
Phoenix College
Graphics Literacy
Gail Korkames – Phoenix
College
3D Graphics
John Roberts – Glendale
Community College
Practical Visual Copyright
15
• Laws balancing financial gain and freedom
of use
• Protecting and licensing your images
• How to find
images and
assess their
copyright status
• Resolving
workplace Issues
BLOG Module
• How do we “read” images?
Similarities with text.
• How is visual meaning
created?
• Synergy between text and
images in establishing an
explanation or a line of
argument.
16
Visual Dialog in E-commerce
• New business models
and strategies inspired by
digital visual
communication…
– Kodak Easy Share, flickr,
etc.
• Compare company
product photos with user
uploaded ones on
Amazon
You can now add a picture
of a product to Amazon…
17
18
Graphics Literacy Module
• Overview of basic graphics
terms, concepts, and tools
• An introduction to basic skills
using MS Paint
• Designed to promote student
awareness of the technology
and talent involved in
creating computer graphics
19
3D Graphics Module
• What is 3D graphics?
• How are businesses taking
advantage?
MS Office Modules
Kay Gaisford – Mesa Community College
Visual Display of Information Using Word
• 2003
• 2007
Visual Display of Information Using PowerPoint
• 2003
• 2007
Influencing Decisions with Charts – Excel
• 2003
• 2007
20
Word and PowerPoint
21
• Better visual design in Word and PowerPoint
• Addresses basic design principles and vocabulary
Better
Design
Through
1. Contrast
2. Repetition
3. Alignment
4. Proximity
Excel Charts and Graphs
22
• Excel can make your numbers look pretty.
But is the result meaningful?
• How to analyze and create…
Two Excel Charts Made
From One Set of Data,
but They Convey Very
Different Messages
• Pyramid shape makes top
portion look smaller than the
data it represents
• 3-D effect make it difficult to
accurately read the numbers
DVL Brochure (outside)
23
DVL Brochure (inside)
24
Development of the Instructor
Guides
25
Instructor Guide Sample
Pages
Instructor Guide
Summary
Presentation with
Instructor Notes
26
Grading Rubric
ADDIE Process in DVL
•
•
•
•
•
Analysis
Design
Development
Implementation
Evaluation
Instructor Guide Summary Templates
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Instructional Design Matrix – Using Excel 2007 to Design Charts that Influence and Inform
Goal: The student will

Demonstrate knowledge of the power of visual representations of numerical data.

Be able to determine which type of chart best supports accurate, effective communication of data.

Analyze and interpret numeric data by creating charts.

Analyze charts to determine accuracy of data representation.
Objectives
Information/Examples
Practice with
Feedback
Assessment
1.
Describe the difference in ease of
comprehension between the numerical data
and the visual representation of that data.
Excel-2007-DVL-Presentation.ppt
Excel-2007-Chart-Types-andDesign.doc
In class
discussion
Excel-2007-QuizWithAnswers.doc
2.
Explain reasons for the widespread use of
charts and graphs in popular media as well
as business communications.
Excel-2007-DVL-Presentation.ppt
Excel-2007-Chart-Types-andDesign.doc
In class
discussion
Excel-2007-QuizWithAnswers.doc
3.
Select appropriate chart type that will best
visually represent the data based on the chart
type rubric.
Excel-2007-DVL-Presentation.ppt
Excel-2007-Chart-Types-andDesign.doc
In class
discussion
Projects 1
and 2
Excel-2007-Project1.doc
Excel-2007-Project2.doc
Excel-2007-QuizWithAnswers.doc
4.
Describe criteria for displaying and formatting
chart elements.
Excel-2007-DVL-Presentation.ppt
Excel-2007-Chart-Types-andDesign.doc
In class
discussion
Excel-2007-QuizWithAnswers.doc
5.
Describe design principles that will help make
charts more attractive and effective.
Excel-2007-DVL-Presentation.ppt
Excel-2007-Chart-Types-andDesign.doc
In class
discussion
Projects 1
and 2
Excel-2007-QuizWithAnswers.doc
29
Knowledge of DVL - based on student perception
4.50
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
Before DVL Module
W
or
d
(N
G
ra
=1
ph
1)
ic
s
(N
C
=1
op
3)
y
rig
Ec
ht
om
(N
m
=9
er
ce
)
(N
=6
3)
3D
N=
33
)
t(
in
we
rp
o
Po
Ex
ce
Bl
og
l(
N=
2
=5
2
)
0)
After DVL Module
(N
Knowledge of DVL on Likert
Scale
Before & After
Modules
Rating Scale: 5 = very knowledgeable 4 = Knowledgeable about
3 = Uncertain 2 = Somewhat knowledgeable 1 = not knowledgeable about
Charts are Computer Graphics
30
Apply Graphic Design Principles - Contrast
▲ Contrast is the use of
differences to create
interest, excitement,
show importance of
different items.
Used Auto Sales Drop Off Friday Evenings During Football Season
Sales Average Sales September-November 2006
40
35
30
25
20
15
10
5
-
▲ Spot color contrasts
with other colors.
Market share has improved 12 points since 2003
New Models Boosted Sales beginning in 2006
▲ Bold columns (color,
width) contrast with
white background.
33%
23%
24%
25%
2003
2004
2005
2006
35%
2007
Lying With Charts
This Charity Keeps Administrative Costs Low
Lying with Poor Chart Choices
▲ Misleading charts may
result in errors in
decision-making
▲ Charts are often
designed in ways that
hide what the data
might tell us, or that
distract the reader from
quickly discerning the
meaning of the chart.
Adapted from Charts and Graphs for Microsoft
Excel 2007, Bill Jelen, Que Books, 2007.
Wedges at
the front of a
3-D pie chart
appear larger
than in the
back
Administrative Costs
Other Expenses
Administrative Costs Take a Big Chunk of Funds Raised
Administrative Costs
Other Expenses
31
a Through the Roof
Marketing Costs Are Going
Misleading Charts
350
300
250
Using a Chart Type that Obscures the Truth
200
150
100
50
▲ the Area chart to the right
above shows that marketing
costs are rising sharply.
0
2003
2004
2005
2006
c
This Stacked Area Chart Obscures
Cost Trends
1000
800
▲ The Stacked Area chart to the
right obscures this trend
600
400
200
0
▲ The clustered column chart
shows each data point
separately so that trends can
be examined for all 4 expense
areas.
2003
2004
Manufacturing
2005
Marketing
R&D
2006
Sales
This Clustered Column ChartcShows Data Points Clearly
350
300
250
200
150
100
50
32
Adapted from Charts and Graphs for Microsoft
Excel 2007, Bill Jelen, Que Books, 2007.
0
2003
2004
Manufacturing
2005
Marketing
2006
R&D
Sales
Chart Types – Column
The best chart type is the one that gets
your message across most effectively
▲ Column charts are good for up to 12 data
points. Otherwise, use bar or line chart.
▲ Clustered column chart shows each data
point separately (easy to interpret)
▲ 100% Stacked Column chart compares the
percentage each value contributes to a total
across categories
▲ Cylindars, Cones, and Pyramids are
similar to column charts with more dramatic
shapes (sometimes distort perception).
▲ 3-D effects use more ink, more space,
and distort perception
33
DVL Wiki
• DVL Wiki: http://dvl.mc.maricopa.edu
34
DVL – The 4th R is a V!
• New skills are needed to succeed in a digital
visual workplace
• DVL modules can help:
– Strategically introduce this emerging field in your
classes
– Strengthen your current offerings
• JOIN US!!!
35
YOUR Ideas: DVL’s Potential Uses?
36
Decision Theatre
(Arizona State University)
•
•
•
How could you apply Digital Visual Literacy in your
curriculum?
Do you have any thoughts about future DVL modules
and new initiatives?
Interest so far: Universities, community colleges, high
schools, middle schools, and new partnerships/grants
For More Information
37
Digital Visual Literacy Project
Maricopa Community Colleges
http://dvl.mc.maricopa.edu/
(480) 731-8124
dvl@domail.maricopa.edu
This material is based upon work supported by the National Science
Foundation Grant No. 0501965. Any opinions, findings and conclusions or
recommendations expressed in this material are those of the author(s) and do
not necessarily reflect the views of the National Science Foundation (NSF).
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