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Title: Building%20Mental%20Models%20with%20Visuals%20for%20e-Learning

Building Mental Models with Visuals for
  • Shalin Hai-Jew
  • Aug. 7 10, 2008
  • Minneapolis, MN
  • MERLOT Still Blazing the Trail and Meeting New
    Challenges in the Digital Age

Mental Models
  • Definitions A learners internal
    conceptualization of a system / paradigm,
    situation, personage, phenomena or equipment a
    learners sense making (vs. an instructors
    conceptual model on which the designed learning
    is based) implicit and explicit knowledge,
    internalized and externalized knowledge
    subconscious and conscious beliefs
  • Synonyms An underlying substructure an
    analogue of the world an operationalized mental
    template for meaning and form (Riggins
    Slaughter, 2006, p. 4) worldview

Mental Models (cont.)
  • Ideas People Act On Theories in use vs.
    espoused theories (the implied theories based on
    peoples actions vs. what they say they believe),
    to paraphrase Argyris Schon
  • Structured Knowledge A hypothetical knowledge
    structure that integrates the ideas,
    assumptions, relationships, insights, facts, and
    misconceptions that together shape the way an
    individual views and interacts with reality
    (Steiger Steiger, 2007, p. 1) an embodiment of
    domain knowledge in order to abstractly reason
    about the domain (objects, grouping,
    interrelationships, sequences, processes, and

Theoretical Underpinnings
  • Constructivism, which asserts that learners make
    meanings in their own minds
  • Cognitivism, the study of the human mind,
    awareness and mental functions, especially the
    Dual Channel Model (auditory-verbal and
    visual-pictorial), with strategies to maintain
    cognitive load (Mayer Moreno, 1998)

Metaphoric Mapping
Some Types of Mental Models
  • Representational describes, articulates,
    renders coherent, illustrates, and defines
  • Predictive anticipates, proposes trend lines,
    predicts, and projects / forecasts
  • Proscriptive defines how something should be
  • Speculative proposes an un-testable thesis,
    purely theoretical (may be mental models at
    extreme scales, beyond sight, sound, and human

Some Types of Mental Models (cont.)
  • Live Info-Heavy Modeling collates live,
    dynamic, and multi-variate information (from
    remote sensors, cameras, people, and other
    sources) into a semi-coherent larger view /
    visualization may be user-interactive and
    user-manipulatable for analysis and

Pedagogical Considerations
  • Identification of threshold conceptsdifficult
    core concepts that once understood provide a
    broad base for comprehension of more advanced
  • The fomenting of cognitive dissonance related to
    beliefs, perception, attitudes, behaviors to help
    learners adjust internal mental models
  • Building on naïve mental models which tend to be
    elusive, poorly formed, incomplete, poorly
    structured, difficult to articulate, illogical,
    overly generalized, and invalid (from Nardi
    Zarmer, 1991, p. 487)

Pedagogical Considerations (cont.)
  • Surfacing learner mental models offering methods
    to test assumptions avoiding negative transfer
    avoiding undesirable dependencies to the learning
    tools, and reinforcing accurate conclusions and
    observations (consistently, with augmenting
  • Identifying and addressing misconceptions (of
    objects, of relationships, fragmented knowledge,
    comparison, contrast, over-generalization,
    over-simplification, and others)

Pedagogical Considerations (cont.)
  • Must have a defined learning approach clear
    context, defined terminology, defined and
    reasonable learning objectives, defined objects,
    clear relationships, interactivity, processes,
    and a systems view in the design offer
    opportunities for discovery methods to build
    mental models (Moreno, 2004, p. 99)
  • Should have some consideration for self-regulated
    learning (SRL) and self-discovery learning (SDL),
    particularly with automated learning constructs
    and self discovery learning virtual spaces

Pedagogical Considerations (cont.)
  • Should consider expert approaches (establishing a
    context for the task classifying problems based
    on underlying principles and concepts (Alexander
    Judy, Winter 1988, p. 382)

Designing a Mental Model
  1. Identify a learning domain. Select a portion (or
    the whole) to model.
  2. Define the foundational realities.
  3. Define the learning objectives and outcomes.
  4. Define the relevant terminology and nomenclature.
  5. Define the range of possible variables and
  6. Define relevant processes within the model.
  7. Prototype and build the mental model while
    considering and adhering to mental modeling
  8. Build learning scenarios.
  9. Build test scenarios, and test with novices and

Setups for Mental Model Learning
  • Pre-learning
  • Human facilitation / automated facilitation
  • Decision supports
  • Learner tracking and measures
  • Debriefing
  • Pre- and post-testing
  • Takeaways and downloadables

Digital Mental Model Viability
  • The accuracy and comprehensiveness of the
    depicted information
  • The fidelity and logical alignment of the model
    to the real world
  • The applied predictability and utility
    particularly with multiple interrelated factors
    in play (with clear lines of reasoning)
  • The efficacy of the model used in conjunction
    with other tested models in a complex environment
  • The expressibility or communicability (vs.
    implicit representational forms like metaphors or

Digital Mental Model Viability (cont.)
  • The timeliness (non-obsolescence / real-time
    value) and updatability of the model (with new
  • The soundness of the assumptions and premises
    (without inherent biases or propensities)
  • The portability of the mental model between
    technological systems / of information from the
  • The real-time, time-varying, information-rich,
    multi-stream, multi-representational and
    real-time feedback

Digital Mental Model Viability (cont.)
  • The aesthetic presentation
  • The originality and uniqueness of the digital
    mental model
  • The malleability of the model to incorporate new,
    foundational design elements
  • The legality of the materials (accessibility,
    intellectual property rights, avoidance of libel
    and slander, and others)

Mental Models Enablement
  • with High-Tech Visualizations and Graphics in

  • Illusion-making and the hard-wiring of the human
    brain and visual perception
  • Flow and movement 1D, 2D, 3D and 4D
    transparency and overlap texture luminance,
    brightness, saturation, and reflectance
    contrast shape, size, boundaries and edges
    axes, planes, and others
  • Gestalt Laws of Pattern Perception proximity,
    similarity, connectedness, continuity, symmetry,
    closure, relative size, figure and ground
    (Westheimer, Koffka, Kohler, 1912, 1935, as
    cited in Ware, 2004, pp. 189 197)
  • Labels and text, sounds, and voice

High Tech Affordances
  • Structure mapping with computer coding
    (ontologies, taxonomies) and spatial layouts
    (bubble graphs, node-link diagrams) of mental
  • Uses of multimedia multi-channel modes for
    coherence, transparency, and clarity (some with
    real-time elements)
  • Simulation for similarity, experiential learning,
    and full-sensory immersion (with experiential
  • Situated cognition for field-dependent learning
    situated action for field-dependent analysis,
    decision-making and behaviors

High Tech Affordances (cont.)
  • Informational visualizations (2D, 3D and 4D) to
    capture ever greater informational complexity
    without clutter or visual confusion (and without
    cognitive overload)
  • Environmental visualizations via animation and
    movement sequential experiences branched
  • Interactivity and hypothesis testing
  • Immersive spaces with automation, intelligent
    agents and / or live human interactions (and
    possible mediation)
  • Information gathering in the digital enclosure
    swarm behavior, urban probes, stalked
    trashcans, lost postcard with URLs behaviors
    (Paulos Jenkins, 2005, pp. 341 350)

Conveying a Mental Model
  • Realia and real-world artifacts (digital stills
    and video feeds, live sensor feeds, mobile
    sensors), introduction of serendipity and
    apparent chance
  • Simulations, depictions (sensory details sight,
    sound, smell, taste, and touch)
  • Behaviors (agent and user) and interactivity
    (with feedback loops)
  • User informed choices
  • Avoidance of unintentional negative learning

Visualization for Presentation
  • Representational, descriptive and realistic or
    theoretical, conceptual or imaginary (alternate
    conceptual universes)
  • Holistic or partial, decomposition of images,
  • Process dynamism or change vs. static
  • Stylized or non-stylized, natural
  • High, medium or low fidelity selective or
    non-selective fidelity
  • Conveyance of emotions through emo bots and
    agents, building trust and relations with robots
  • Macro or micro perspectives
  • Discrete or continuous (Tory Möller, 2002, as
    cited in Tory Möller, Jan. Feb. 2004, p. 72)

Visualization for Prototyping
  • Modeling production designs and blueprints
  • Representing different phases of a build
  • Revising plans
  • Effective for virtual teaming shared mental
    models (but avoiding groupthink) (Thomas
    Bostrom, 2007, pp. 1 8)

Visualization for Culling Data
  • May extract data from visual captures (such as
    road information from a satellite image, traffic
    imagery gas dispersion flows from a live site
    quantifying the number of people or objects at a
    scene facial recognition software checking
    architectural compliances in terms of distances
    in a blueprint 3D imaging or cross-sections of a
    tumor identification of correlations between
    forms / images simulated flows and transitions
    weathering and aging forensically analyzing
    satellite images disaster response natural
    resources management agricultural planning, and

Visualization for Culling Data (cont.)
  • Digital re-constructions of events
  • Digital cartography / map-making
  • Simmed projections of potential events (with or
    without human inputs / interactions)
  • Deformation and animation of soft objects (from
    video captures) de-noising image captures (for
    clearer info) feature enhancements
  • Analyzing hyperspectral imagery

Visualization for Organizing Data
  • Defining relationships between informational
    objects in a domain-specific database as compared
    to an expert-based domain competency model
    (Ahmad, et al., June 2007, pp. 452 461)
    checking mental models (naïve and expert)

Some Mental Models in E-Learning
  • Simulations
  • Immersive learning spaces
  • Role playing in case studies, team or group
    simulations, simmed decision-making (e.g. game
  • Knowledge systems, ontologies, and taxonomies,
    with user interfaces that map with learning
  • Traditional e-learning

2D Visuals
  • Sketches, drawings, blueprints, diagrams, charts,
    tables, timelines, icons, symbols and designs
  • Slideshows (static and dynamic)
  • Screenshots
  • Interactive maps, screencasts, and games
  • Photo montage, photorealistic images
  • Non-photorealistic images and depictions
  • Video
  • Animated agents, avatars, maquettes / models for
    intended work
  • Satellite imagery, live data-fed images, and
    acoustical imaging

3D and 4D Visuals
  • Fractals
  • Videos, field recordings
  • Animated agents, avatars, maquettes (models of
    intended works like a sculpture), and scenes
  • Satellite imagery, live data-fed images, and
    acoustical imaging
  • Immersive spaces, microworlds, and metaworlds
  • Augmented reality, ambient space
  • Holography
  • Haptic visual interface
  • 4D
  • 3D with time (temporal changes and motions)

Combined 2D and 3D Visualizations
  • Orientation indicators (icons, separate windows)
  • In-place methods (clip and cutting planes)
  • Orthographic 2D overlays around a 3D object
  • Medical imaging
  • Flow visualization
  • Oceanographic visualization
  • Computer aided design (Tory, Oct. 2003, p. 371)

Capturing Images in Digital Form
  • Digital cameras, mobile devices, mini-cams
  • Scanners
  • Microscopes
  • Telescopes
  • 3D devices / multiple synced cameras
  • Sonar image devices, acoustic image devices
  • Remote sensors, mobile robot sensors, unmanned
    aerial vehicles (UAVs)
  • 3D game engines
  • Database-stored information and statistics
  • Radar
  • Satellite
  • Telephone call registries
  • Remote labs
  • Digital pens and tablets
  • Desktop screen captures
  • 2D to 3D with minimal image sets (as in
    ubiquitous video for immersive flythroughs for
    situational awareness)

The Role of Digital Visuals
  • in Mental Modeling

digital storytellingscience-based digital
wetlabsmedical diagnosisdeep sea
explorationouter space explorationaerial image
analysishuman facial identificationmuseum and
art gallery capturesmanga illustrationsinformati
on extractionmachine arttelemedicineimmersive
simulationsvisual information accessvideo
tooningarchitectural designslandscape
architectureperformance artmobile
visualizationgeographically mapped spaces via
GPSthermal imagingtime-lapse /time monitoring
of flows / stock portfolios
Image Maps
  • DESCRIPTION Spatial information, interactive,
    integration of text and images, conveyance of
    forms and distances, spatial relationships
  • Tends to be informationally pre-determined and
    static, with designed interactive effects

Glyphs or Iconic Visualizations
  • DESCRIPTION A sculptured figure or relief
    carving a font type as in an element of writing
    a visual object that contains one or more data
    variables (coded in the shape, color,
    transparency, orientation, or other aspects of an
  • Often used in cartography (map-making), logic,
    semiotics (signs and symbols), and pictoral
    information systems (Ebert, Shaw, Zwa Starr,
    1996, p. 205)

  • DESCRIPTION An arrangement of aerial or seabed
    photos that form a composite image a visual
    effect in which an image is created of many
    smaller images
  • Used for forensic analysis

Screen Captures / Screenshots
  • DESCRIPTION Realistic to the computer screen,
    annotatable static (non-motion) and non-dynamic
    dynamic (with motion) may have voice overlays
  • Examples of interfaces
  • Authentic at the moment of capture, usually not
    refreshed (as in websites)

  • DESCRIPTION Process-oriented, sequential,
    annotated, realia, voice narrated, multi-sensory
  • Used to teach about how to use software programs
    or interfaces via desktop computers
  • Captures of live synchronous interactive
    experiences, including voice, video, text, live
    annotation, and other features
  • Used for virtual teaming meetings, classes, and
    live interactions

  • DESCRIPTION 3D and 4D, geometric, elegant,
    relational, a kind of machine art based on
    mathematical formulas
  • Shows relationships, trends
  • Self-similarity in design (at least
  • Tends towards irregularity
  • Is meaningful at both macro and micro levels
  • Tends towards recursiveness

Photo-realistic Images
  • DESCRIPTION Digital photo captures and imagery
  • May be microscope-enhanced, may be
  • May originate from satellite, acoustical image
    gathering , sonograms, x-rays, CAT scans
  • May be editable and enhanced, and digitally
  • Requires a sense of objective size and measure
    requires a correct white balance
  • May be mixed with overlays of annotation, drawing
    or other information, annotatable
  • May be informational, illustrative , decorative,
    and others

Non-Photorealistic Images
  • Image morphing
  • Photo-mosaicing
  • Cartoon rendering from images
  • Computerized drawing and imaging fictional
  • Photogravure effects / intaglio printmaking
    etching simulation
  • Machine art
  • Acoustic-created synced imagery
  • Digital sculpting
  • Theoretical modeling and visualizations
    (particularly in the sciences and arts)
  • Synthesized image overlays for information-rich
    experiences (usually with photo-realistic images
    or real spaces)

Digital Video
  • DESCRIPTION Involves color, movement and sound
    realistic or fantastical sequential or
    non-sequential may be stylized may include
  • May be interactive if interspersed with Flash and
    other objects
  • May be segmented for easier deployment

  • DESCRIPTION Human or animal or symbolic shapes
    playable characters
  • May communicate in voice / sound and / or text
  • May make decisions and actions in digital spaces
  • Represent their animating players from the

(Semi-)Intelligent Agents
  • DESCRIPTIONS Non-playable, automated
    characters may be static or dynamic
  • Programmed abilities, roles, emotions, beliefs,
    actions, intelligence and decision-making
  • May play a direct pedagogical or instructional
  • May be a tutor
  • May infuse a sense of telepresence into automated
    learning spaces

Flocking Group Behaviors
  • DESCRIPTION The automation of autonomous
    digital entity behaviors in coordinated motion,
    with or without individual agent guides, with
    agent attraction / repulsion also swarming,
    schooling, herding, autonomous pedestrians and
    crowd behaviors character or object motion
  • Members of a crowd as a-agents (alpha agents)
    may be inertial and pre-determined
  • Basic C. Reynolds boids approach (1986)
    cohesion or flock centering (staying close with
    fellow agents) alignment (matching velocity or
    speed with a-agents), and separation (avoid
    collisions with nearby agents)

Live Data-feed Images
  • DESCRIPTION Remote sensor-fed, database-fed,
    representations often in spatial layouts,
    satellite feeds, and other types of
    multi-spectral / multi-source / multivariate
    integrated data
  • Evolving and changing
  • Real-time
  • Potential suggested trend lines
  • Macro and micro perspectives

Digital WetLabs
  • DESCRIPTION Process-based actions,
    causes-and-effects, human-mediated or remote labs
    or simulations, 3D computer sims with game-engine
  • Narration of processes
  • Building of context with facts
  • Explanations of measures
  • Clear definition of materials used
  • Explanations of the processes and effects
  • Explanations of negationwhat the process is not

  • DESCRIPTION Machine cinema, captures of
    avatar interactions and 3D immersive digital
    environments and game spaces pre-recorded
    includes sound
  • In-world digital effects
  • May be performance-based or unscripted and

Machine-Generated Art
  • DESCRIPTION Based on math formulas,
    evolutionary art, chance and other factors tends
    towards fractals
  • Synthetic art with unique vector imprints and
  • Chaos tools, morphogenesis, cellular
    machines, neuronal co-evolution, and
    non-photo-realistic techniques
  • Visualization algorithms
  • Perpetual Art Machine

3D Immersive Spaces
  • DESCRIPTION Live, unpredictable,
    human-populated, automated and true serendipity
  • The capturing of visual complexity (with
    multi-channel sensory information without
    cognitive overload)
  • The highlighting of particular isosurfaces for
  • Scene updates

High-Tech Image Editing
  • Video tooning (Wang, Xu, Shum Cohen, 2004, pp.
    574 583) or turning video into a
    spatio-temporally coherent cartoon animation
  • Photo-realistic image to manga illustration
    personalized image-to-cartoon stills
  • Image relighting, event relighting

Augmented Reality (AR)
  • AR DESCRIPTION Real-space overlay of digital
    images and sound through backpack wearable
    computers, head-sets and goggles interactive
    live data feeds often full-sensory used for
    coordinated multi-participant practices in real
    space may be place-sensitive (location-based) or
    place-agnostic (fully mobile) visual
    enhancements on user interfaces and overlaid into
    real spaces

Ambient Intelligence (AI)
  • AI DESCRIPTION Built-in integrated-display
    electronic environments responsive to the
    presence of people, context-aware,
    individual-aware, adaptive, and anticipatory of
    unique human needs

Some Examples
  • Augmented Reality(AR) and Ambient Intelligence
  • mixed reality outdoor gaming arenas ubi
    (ubiquitous) computing, social gaming, physical
    gaming, mobile gaming
  • tangible or haptic / tactile interfaces
  • movement / kinesthetic -based interfaces
  • immersive real-space and digital installations,
  • smart rooms in smart buildings / houses

Digitized Visual-Based Mental Models
  • Some Examples

A Few Live Digital Examples
  • A fractal to describe the infection paths of HIV
    transmission between people
  • A predictive simulation of molecular interactions
  • A Doppler Radar weather map and visualizations
  • A moving 3D projection representing the 4th
  • A house floor plan a proposed village
  • A social interchange in a leadership situation
    with artificial intelligence (AI) avatars

A Few Live Digital Examples (cont.)
  • Interactive computer models for analytic
    chemistry instruction, a forest simulation,
    animal physiology and signal transduction,
    acid-base titration in a virtual chemistry lab,
    the self-heating and scaling of silicon
    nano-transistors, crystallography, and others
  • A computer-generated smoke dispersion /
    progression over a photo-realistic image of a 2D
  • Deep space exploration depictions

The Future
  • Increasing sophistication of digital image
    captures and editing, realia and digital
  • Increasing realism and increasing synthetic
    digital imagery (both ends of the continuum)
  • Synaesthesia with the inclusion of digital smells
  • Easier end-user editing and publishing tools (for
    do-it-yourself faculty and learners) for richer
    digital image capture and deployment
  • Increased collaborative digital image creation

The Future (cont.)
  • Increased divergence between open source /
    Creative Commons and (cc) imagery and secure,
    private imagery
  • More effective automated metadata (and data /
    content) capture and organization
  • Content-rich image repository ontologies,
    taxonomies and collections
  • Less unwieldy augmented reality / ambient
    intelligence spaces
  • Increasing pedagogical sophistication in the use
    of imagery in e-learning for mental modeling

  • Ahmad, F., de la Chica, S., Butcher, K., Tumner,
    T. Martin, J.H. (2007, June ). Towards
    automatic conceptual personalization tools. ACM.
    452 461.
  • Alexander, P.A. Judy, J.E. (1988, Winter).
    The interaction of domain-specific and strategic
    knowledge in academic performance. Review of
    Educational Research Vol. 58, No. 4, 375-404.
  • Ebert, D.S., Shaw, C.D., Zwa, A., Starr, C.
    (1996). Two-handed interactive stereoscopic
    visualization. IEEE. 205.
  • Moreno, R. (2004). Decreasing cognitive load for
    novice students Effects of explanatory versus
    corrective feedback in discovery-based
    multimedia. Instructional Science Vol. 32.
    Kluwer Academic Publishers. 99 113.
  • Nardi, B.A. Zarmer, C.L. (1991). Beyond models
    and metaphors Visual formalisms in user
    interface design. IEEE. 487.
  • Paulos, E. Jenkins, T. (2005, Apr. 2- 7).
    Urban probes Encountering our emerging urban
    atmospheres. CHI 2006 PAPERS Design Thoughts
    Methods. ACM. 341 - 350.

References (cont.)
  • Riggins, F.J. Slaughter, K.T. (2006). The role
    of collective mental models in IOS adoption
    Opening the black box of rationality in RFID
    deployment. 4.
  • Steiger, N.M. Steiger, D.M. (2007). Knowledge
    management in decision making Instance-based
    cognitive mapping. Proceedings of the 40th
    Hawaii International Conference on System
    Sciences. 1- 2.
  • Thomas, D.M. Bostrom, R.P. (2007). The role of
    a shared mental model of collaboration technology
    in facilitating knowledge work in virtual teams.
    Proceedings of the 40th Hawaii International
    Conference on System Sciences 2007. IEEE. 1
  • Tory, M. (2003, Oct.) Mental registration of 2D
    and 3D visualizations (an empirical study). IEEE
    Visualization 2003. 371 378.
  • Tory, M. and Möller, T. (2004, Jan. Feb.)
    Human factors in visualization research. IEEE
    Transactions on Visualization and Computer
    Graphics 10 (1). 72.

References (cont.)
  • Wang, J., Xu, Y., Shum, H-Y., Cohen, M.F.
    (2004). Video tooning. ACM. 574 583.
  • Ware, C. (2004). Information Visualization. 2nd
    Ed. San Francisco Elsevier, Morgan Kaufmann.
  • Wells, J.D. Fuerst, W.L. (2000).
    Domain-oriented interface metaphors Designing
    Web interfaces for effective customer
    interaction. IEEE. 1.

  • to Kansas State University for the many
    e-learning design opportunities.
  • to the Society for Applied Learning Technology
    (SALT) for first showcasing a piece of the mental
    modeling research in Feb. 2008, in Orlando, FL.
  • to IGI-Global for supporting this work with a
    forthcoming text.
  • to R. Max

Conclusion and Contact
  • Dr. Shalin Hai-Jew
  • Office of Mediated Education / Instructional
  • Kansas State University
  • (785) 532-5262 (work phone)
  • (785) 532-5914 (fax number)
  • Instructional Design Open Studio (IDOS) Blog
  • There is an accompanying activities handout with
    this presentation for the brainstorming of
    visuals for mental model building in context.
  • See the Notes area below each slide for
    additional URL links.