Using Value-Added Visuals in E-Learning
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USING VALUE-ADDED VISUALS IN E-LEARNING
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Using Value-Added Visuals in E-Learning
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:
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Using Value-Added Visuals in E-Learning
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.
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Using Value-Added Visuals in E-Learning
YOUR DIGITAL IMAGERY IN E-LEARNING Your experiences? Your general uses? Some general questions?
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Using Value-Added Visuals in E-Learning
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
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Using Value-Added Visuals in E-Learning
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
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Using Value-Added Visuals in E-Learning
WHAT INFORMATION IS COMMUNICATED THROUGH VISUALS?
Using Value-Added Visuals in E-Learning
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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
Using Value-Added Visuals in E-Learning
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TYPES OF DIGITAL VISUALS 1D to 4D
(dimensionality) Can have mixed
modes
Dimensionality1D: pixel2D: an image with length and width, along the x and y axes3D: an image with length, width, and depth; along the x, y and z axes 4D: a 3D image with movement added
Using Value-Added Visuals in E-Learning
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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
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Using Value-Added Visuals in E-Learning
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
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Using Value-Added Visuals in E-Learning
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
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Using Value-Added Visuals in E-Learning
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
Using Value-Added Visuals in E-Learning
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SOME FROM-LIFE EXAMPLES
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Using Value-Added Visuals in E-Learning
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)
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Using Value-Added Visuals in E-Learning
IMAGINARY IMAGERY / VISUALIZATIONS 3D spaces and avatars Live site analysis as a visualization / chart Geological time simulation NOAA
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Using Value-Added Visuals in E-Learning
DIAGRAMS AND PLANS Plans and blueprints (theoretical or proposed)
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Using Value-Added Visuals in E-Learning
CONCEPTUAL MODELS Abstract visualizations Relationships Knowledge structures Taxonomies
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Using Value-Added Visuals in E-Learning
SCANNED IMAGES / LAB-CAPTURED IMAGES In-field samples (alternaria alternata, a
fungal plant pathogen, on a Nicotiana tabacum leaf)
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Using Value-Added Visuals in E-Learning
MICROSCOPY Grains in grain science Insects in entomology Tissue samples Pollen grains
Using Value-Added Visuals in E-Learning
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INTEGRATED IMAGERY
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Using Value-Added Visuals in E-Learning
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
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Using Value-Added Visuals in E-Learning
EBOOK Replacements for
physical objects used for learning and analysis
Optimally 3D and the most high-fidelity to the original
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Using Value-Added Visuals in E-Learning
BRANDING Look and feel of a site for stress reduction Public health and globalist imagery University Life Café and a caring
environment
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Using Value-Added Visuals in E-Learning
DESIGNED ONLINE LEARNING ENVIRONMENTS NASA in Second Life™ Enduring Legacies Native Cases
“Native Gaming in the US” (social, political, and economic)
Using Value-Added Visuals in E-Learning
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FROM IMAGE CAPTURES TO DEPLOYMENT…
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Using Value-Added Visuals in E-Learning
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
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Using Value-Added Visuals in E-Learning
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)
Using Value-Added Visuals in E-Learning
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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
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Using Value-Added Visuals in E-Learning
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
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Using Value-Added Visuals in E-Learning
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
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Using Value-Added Visuals in E-Learning
IMAGE PROCESSING WORKFLOW
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Using Value-Added Visuals in E-Learning
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
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Using Value-Added Visuals in E-Learning
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?)
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Using Value-Added Visuals in E-Learning
WHICH IMAGE IS MORE “VALUABLE” AND WHY? Drought Risk Snow and Ice Cover Total Precipitable
Water
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Using Value-Added Visuals in E-Learning
WHAT DOES “VALUE-ADDED” MEAN IN TERMS OF IMAGERY?
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Using Value-Added Visuals in E-Learning
“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)
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