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Using Value-Added Visuals in E-Learning

Using Value-Added Visuals in E-Learning. Overview.

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Using Value-Added Visuals in E-Learning

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  1. Using Value-Added Visuals in E-Learning 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: Using Value-Added Visuals in E-Learning

  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. Using Value-Added Visuals in E-Learning

  4. Your digital imagery in e-learning • Your experiences? • Your general uses? • Some general questions? Using Value-Added Visuals in E-Learning

  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 Using Value-Added Visuals in E-Learning

  6. Human Perception -> Cognition -> Learning Using Value-Added Visuals in E-Learning

  7. What information is communicated through visuals? Using Value-Added Visuals in E-Learning

  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 Using Value-Added Visuals in E-Learning

  9. Types of digital visuals • 1D to 4D (dimensionality) • Can have mixed modes Using Value-Added Visuals in E-Learning

  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 Using Value-Added Visuals in E-Learning

  11. 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 Using Value-Added Visuals in E-Learning

  12. 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 Using Value-Added Visuals in E-Learning

  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 Using Value-Added Visuals in E-Learning

  14. Some from-life examples Using Value-Added Visuals in E-Learning

  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) Using Value-Added Visuals in E-Learning

  16. Imaginary imagery / visualizations • 3D spaces and avatars • Live site analysis as a visualization / chart • Geological time simulation • NOAA Using Value-Added Visuals in E-Learning

  17. Diagrams and plans • Plans and blueprints (theoretical or proposed) Using Value-Added Visuals in E-Learning

  18. Conceptual models • Abstract visualizations • Relationships • Knowledge structures • Taxonomies Using Value-Added Visuals in E-Learning

  19. Scanned images / lab-captured images • In-field samples (alternariaalternata, a fungal plant pathogen, on a Nicotianatabacumleaf) Using Value-Added Visuals in E-Learning

  20. microscopy • Grains in grain science • Insects in entomology • Tissue samples • Pollen grains Using Value-Added Visuals in E-Learning

  21. integrated imagery Using Value-Added Visuals in E-Learning

  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 Using Value-Added Visuals in E-Learning

  23. ebook • Replacements for physical objects used for learning and analysis • Optimally 3D and the most high-fidelity to the original Using Value-Added Visuals in E-Learning

  24. branding • Look and feel of a site for stress reduction • Public health and globalist imagery • University Life Café and a caring environment Using Value-Added Visuals in E-Learning

  25. 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

  26. From Image captures to deployment… Using Value-Added Visuals in E-Learning

  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 Using Value-Added Visuals in E-Learning

  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) Using Value-Added Visuals in E-Learning

  29. Image Capture equipment and software Equipment 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 Using Value-Added Visuals in E-Learning

  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 Using Value-Added Visuals in E-Learning

  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 Using Value-Added Visuals in E-Learning

  32. Image processing workflow Using Value-Added Visuals in E-Learning

  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 Using Value-Added Visuals in E-Learning

  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?) Using Value-Added Visuals in E-Learning

  35. Which image is more “valuable” and why? • Drought Risk • Snow and Ice Cover • Total Precipitable Water Using Value-Added Visuals in E-Learning

  36. What does “value-added” mean in terms of imagery? Using Value-Added Visuals in E-Learning

  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

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