Post on 20-Jan-2016
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
1
USING VALUE-ADDED VISUALS IN E-LEARNING
2
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
OVERVIEW
This presentation introduces some ways to create value-added visuals for e-learning and to employ these in the Axio Learning™ / Course Management System. Some examples will include photorealistic as well as imaginary imagery; diagrams and plans; conceptual models; scanned images, and microscopy images. This presentation will involve some analytical cases; some fictional cases; an e-book; some branding endeavors, and designed online learning environments. Strategies for adding value to digital imagery include:
3
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
OVERVIEW (CONT.)
(1) strategic initial image captures (regarding still imagery color and size for proper perception; regarding sound and visual quality for video)
(2) the proper selection of imagery (3) textual annotations of imagery;
transcription and captioning of video (4) visual integration with the e-learning.
4
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
YOUR DIGITAL IMAGERY IN E-LEARNING
Your experiences? Your general uses? Some general questions?
5
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
HUMAN VISION
A “far sense” (vs. the near-senses of smell, taste, touch, and proprioception)
Capturing reflected light (off objects) and full spectrum light from above
Different wavelengths of light perceived as different colors based on the rods and cones in the
Diurnal (vs. nocturnal) humans (better vision in the day and worse in the night)
Saccadic eye movements Gists of a scene Attention and expectations, change blindness Intrinsic light Metamers
6
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
HUMAN PERCEPTION -> COGNITION -> LEARNING
Human Perception
Cognition Learning
AUTOMATIC•Capturing the sensory stimuli (in working memory)CONSCIOUS•Paying attention •Being motivated to focus on the senses •Rehearsing to push the perceptions into long-term memory
AUTOMATIC•Parsing sensory informationCONSCIOUS•Analyzing •Categorizing•Labeling•Assessing •Comparing and contrasting•Comparison with past learning•Classification•Verbal reportability •Metacognition
DISCIPLINES AND HABITS OF MIND •Reviewing •Selective exposure to particular information and experiences •Applying / work •Designing •Collaborating •Researching
7
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
WHAT INFORMATION IS COMMUNICATED THROUGH VISUALS?
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
8
WHAT INFORMATION IS COMMUNICATED THROUGH VISUALS?
Authenticity Humanizing and
personalization of others Visual signs / symptoms History and
remembrance The sparking of
imagination A context for social
engagement Branding Design and patterns Relationships
Trends Aesthetics Creativity Textures and
sensations
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
9
TYPES OF DIGITAL VISUALS
1D to 4D (dimensionality)
Can have mixed modes
Dimensionality
1D: pixel
2D: an image with length and width, along the x and y axes
3D: an image with length, width, and depth; along the x, y and z axes
4D: a 3D image with movement added
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
10
2D TYPES OF DIGITAL VISUALS (CONT.)
Drawings and sketches
Timelines Icons and symbols Screenshots Photographs Montages Photorealistic images Glyphs (visuals with
multiple data variables)
Non-photorealistic images
Cartoons Video grabs / screen
grabs Satellite imagery Acoustical imagery
11
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
3D TYPES OF DIGITAL VISUALS (CONT.)
3D metaworlds Fractals Haptic-visual interfaces Augmented reality Ambient or smart spaces 3D video Holography Digital sculpting 3D avatars Photogravure effects / simulated etching
12
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
4D TYPES OF DIGITAL VISUALS (CONT.)
Video Machinima (machine + cinema) Animated agents and avatars Live data-fed images Digital wetlabs Simulations Virtual fly-throughs of
landscapes and structures Scenarios Screencasts with motions Machine art Image maps
13
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
DIGITAL AFFORDANCES
Interactive knowledge structures
Multiple simultaneous visual channels
Information complexity Situated cognition /
contextual immersion (in persistent z-dimension)
Repeatable and reproducible images at virtually no cost
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
14
SOME FROM-LIFE EXAMPLES
15
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
PHOTOREALISTIC IMAGERY
Weather systems for flight Cross-sections of animals for radiography Plant pathogens as manifested on particular
plants in the field Photomosaics of large-size imagery (in
composites)
16
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
IMAGINARY IMAGERY / VISUALIZATIONS
3D spaces and avatars Live site analysis as a visualization / chart Geological time simulation NOAA
17
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
DIAGRAMS AND PLANS
Plans and blueprints (theoretical or proposed)
18
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
CONCEPTUAL MODELS
Abstract visualizations Relationships Knowledge structures Taxonomies
19
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
SCANNED IMAGES / LAB-CAPTURED IMAGES
In-field samples (alternaria alternata, a fungal plant pathogen, on a Nicotiana tabacum leaf)
20
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
MICROSCOPY
Grains in grain science Insects in entomology Tissue samples Pollen grains
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
21
INTEGRATED IMAGERY
22
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
ANALYTICAL CASES Digital storytelling Public health mystery Digital preservation of physical objects
(through scanned posters) Troubleshooting and problem-based learning
(PBL) Project-based learning (especially with
design) (PBL) The phases of an art or design or branding
project Digital laboratories Digital repositories / libraries / collections for
analysis
23
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
EBOOK
Replacements for physical objects used for learning and analysis
Optimally 3D and the most high-fidelity to the original
24
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
BRANDING
Look and feel of a site for stress reduction Public health and globalist imagery University Life Café and a caring
environment
25
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
DESIGNED ONLINE LEARNING ENVIRONMENTS
NASA in Second Life™ Enduring Legacies Native Cases
“Native Gaming in the US” (social, political, and economic)
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
26
FROM IMAGE CAPTURES TO DEPLOYMENT…
27
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
INITIAL IMAGE CAPTURES
Born-digital or from-world (representational) High-fidelity or low-fidelity Realistic or symbolic Low-stylized / raw or unprocessed or high-
stylized / processed Dynamic (moving) or static; continuous or
static Partial or holistic Extreme visualizations: nano-size /
mesoscale
28
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
GENERAL CAPTURE CONCEPTS
The importance of setting and lighting Sizing down is always preferable to sizing up, so
capture the most visual information (the highest resolution) at the beginning
Use the right equipment…go high end… Always test equipment (functions and settings)
for visuals and sound captures Practice with the equipment Bring extras (equipment and batteries) Always take multiple shots and captures for
processing later (the relatively low-cost of the digital recording devices and the high-cost of recreating the setting)
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
29
IMAGE CAPTURE EQUIPMENT AND SOFTWARE
Equipment Digital cameras Camcorders Scanners Camera-mounted
microscopes Remote sensing, and other Pen and tablets Mobile phones and devices Sensors and gauges Computational
photography (mix of sensors, optics, lighting, and combined strategies)
Software (stand-alone or embedded)
Drawing software / authoring tools
Equipment Software
30
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
IMAGE CAPTURE
Proper light Proper depth / sense of size High visual information / high resolution captures Clear focus Clear angle Inclusiveness of relevant visual information White color balance / true color saturation and
hue / the global adjustment of the intensities of the colors
Automated metadata (geolocation / more heavy-duty forensics on digital images); human-created metadata
31
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
IMAGE / VISUAL RENDERING
Saving of a raw (“least lossy”) set Naming protocols Proper resolution (ppi / dpi) Proper size (right-sizing) Color balance / color output (“jumping color”)
/ color curves Visual information preservation File output type for particular use
32
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
IMAGE PROCESSING WORKFLOW
33
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
THE SELECTION OF IMAGERY
Provenance of the imagery Raw (self-captured or open-source) and
processed (commercial, open-source) Multicultural / depictions Legal considerations (intellectual property,
privacy, libel, defamation, and accessibility) Information richness Learning context Purposive uses of the imagery Aesthetics
34
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
VISUAL INTEGRATION WITH E-LEARNING
Information overlays (maps, databases of information)
Context (analysis, problem-solving)
Analytical depth Sequencing of the learning Unit of delivery (story,
case, simulation, or environment?)
35
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
WHICH IMAGE IS MORE “VALUABLE” AND WHY?
Drought Risk Snow and Ice Cover Total Precipitable
Water
36
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
WHAT DOES “VALUE-ADDED” MEAN IN TERMS OF IMAGERY?
37
Usin
g V
alu
e-A
dded V
isuals in
E-Le
arn
ing
“VALUE-ADDED” MEANS…
Original imagery (unique or unavailable elsewhere) and perspective (point-of-view)
Clear provenance (origins) All legal and “clean” (unencumbered) Clear labeling and annotations (accessible) High resolution and information-rich for data
culling and analysis (visually informative) Purposive design (i.e. memory, learner priming,
reinforcement, emphasis, learning, experience, branding, storytelling, communications, analysis, and mood)
Image versatility for broad uses (such as cultural neutrality or cultural shaping)