using value-added visuals in e-learning

Using Value-Added Visuals in E-Learning

1

USING VALUE-ADDED VISUALS IN E-LEARNING

2


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


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


YOUR DIGITAL IMAGERY IN E-LEARNING Your experiences? Your general uses? Some general questions?

5


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


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


WHAT INFORMATION IS COMMUNICATED THROUGH VISUALS?


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


9

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


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



3D metaworlds Fractals Haptic-visual interfaces Augmented reality Ambient or smart spaces 3D video Holography Digital sculpting 3D avatars Photogravure effects / simulated etching

12



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


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


14

SOME FROM-LIFE EXAMPLES

15


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


IMAGINARY IMAGERY / VISUALIZATIONS 3D spaces and avatars Live site analysis as a visualization / chart Geological time simulation NOAA

17


DIAGRAMS AND PLANS Plans and blueprints (theoretical or proposed)

18


CONCEPTUAL MODELS Abstract visualizations Relationships Knowledge structures Taxonomies

19


SCANNED IMAGES / LAB-CAPTURED IMAGES In-field samples (alternaria alternata, a

fungal plant pathogen, on a Nicotiana tabacum leaf)

20


MICROSCOPY Grains in grain science Insects in entomology Tissue samples Pollen grains


21

INTEGRATED IMAGERY

22


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


EBOOK Replacements for

physical objects used for learning and analysis

Optimally 3D and the most high-fidelity to the original

24


BRANDING Look and feel of a site for stress reduction Public health and globalist imagery University Life Café and a caring

environment

25


DESIGNED ONLINE LEARNING ENVIRONMENTS NASA in Second Life™ Enduring Legacies Native Cases

“Native Gaming in the US” (social, political, and economic)

http://www.evergreen.edu/tribal/cases/collection/economics.htm


26

FROM IMAGE CAPTURES TO DEPLOYMENT…

27


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


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)


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


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


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


IMAGE PROCESSING WORKFLOW

33


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


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


WHICH IMAGE IS MORE “VALUABLE” AND WHY? Drought Risk Snow and Ice Cover Total Precipitable

Water

36


WHAT DOES “VALUE-ADDED” MEAN IN TERMS OF IMAGERY?

37


“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)

using value-added visuals in e-learning

Documents

elearningyour digital

elearningyour experiences

imagery color

d image

proper selection of

imaginary imagery diagrams

online learning environments

spectrum light