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Human Factors and Ergonomics  2011 Maria C. R. Harrington
 

Human Factors and Ergonomics 2011 Maria C. R. Harrington

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  • My main interests focus on the question: Can simulated ecological environments of nature inspire independent exploration, an intrinsic desire to learn and acts of creation for the child?

Human Factors and Ergonomics  2011 Maria C. R. Harrington Human Factors and Ergonomics 2011 Maria C. R. Harrington Presentation Transcript

  • Maria C. R. Harrington
    • Founder & CEO Virtual Field Trips, LLC
    • Assistant Professor of Information Systems
    • Computer Science Department, Slippery Rock University
    • Ph.D. in Information Science
    • School of Information Science, University of Pittsburgh
    • Bachelor of Science in Economics & Art, Carnegie Mellon
  • “ Can Simulated Ecological Environments of nature inspire independent exploration, an intrinsic desire to learn and acts of creation for the child?”
  • Field Work
  • Design Context & Partnerships
    • User Needs, Goals & Activity
      • Child’s Exploration, Discovery & Inquiry
      • Teacher and Naturalist Guide Support
      • Physical & Cognitive Activity
    • Educational & Learning Content
      • PA Ecology NCLB Standards
      • Informal Educational Materials
      • Audubon Society of Western PA
      • Elementary School Teachers
    • Realistic Data & Visualization
      • ESRI Visualized Terrain Datasets
      • NSF Long-term Plant Population Datasets (Dr. Kalisz) ( http:// www.pitt.edu/~kalisz / )
  • Immersive Virtual World & Informal Learning Simulation
    • Used High-end Game Engine
      • Visualize 1 Square Mile of Data
    • Annotate Virtual World
      • Cards for Facts
      • Audio Sprites for Concepts
    • The Virtual Trillium Trail
      • Working Prototype in 2005
      • Rebuilt with Garage Games Torque 3D in 2009 for a Commercial PC Release
      • iPad, Macintosh versions expected in 2012
  • Photorealistic Game Engine
  • Annotate with Facts
  • Annotate with Story
  • The Virtual Trillium Trail
  • Two Research Studies
    • Study One: Real vs. Virtual
      • Baseline Control (n=12)
      • A one-way, within-subject ANOVA, with repeated measurements, in counterbalanced order (n=12)
    • Study Two: Two Factor ANOVA
      • Factor 1: Visual Fidelity (High & Low)
      • Factor 2: Navigational Freedom (High & Low)
      • One system with four condition states
        • Planed Orthogonal Contrast on Critical User Interface Software Dimensions (n=64)
  • Study One: Real vs. Virtual
    • Baseline Control (n=12)
      • A one-way, within-subject ANOVA, with repeated measures, in counterbalanced order (n=12)
    • Two Groups:
      • Group 1: Real-Virtual
      • Group 2: Virtual-Real
        • Experience the two Environments in opposite order
        • Count Learning Activity of “Salient Events”
      • Measurements
        • Demographic, Pre & Post tests after each on Facts, Concepts, Emotional Response, and a Creative Activity
  •  
    • Total Objects
    • Real has more information than the Virtual, results in higher learning activity:
      • H1a: μ Total Activity (Real) > μ Total Activity (Virtual)
      • Real (M = 4.5, SD = 2.71)
      • Virtual (M = 2.83, SD = 3.43)
      • F(1,11) = 4.68, p = 0.05
    Main Effects: Environment
  • Main Effects: Environment
    • Plant Only Objects
    • When Content in the Virtual is Identical to the Real, there is no difference in learning activity:
      • H2o: μ Plant-only Activity (Real) = μ Plant-only Activity (Virtual)
      • Real (M = 2.75, SD = 1.96)
      • Virtual (M = 2.83, SD = 3.43)
      • F(1,11) = 0.00, p = 0.95
  • Main Effects: Order
    • Second Field Trip,
    • Real or Virtual, shows more Learning Activity
    • Practice Makes Perfect!
    • The t-test for two independent groups (one-tail, post-hoc analysis) was carried out to help explain the results.
    • H8a: Total Activity: μ Real(First) < μ Real(After Virtual)
      • Real (First) (M = 2.8, SD = 2.6)
      • Real (After Virtual) (M = 5.8, SD = 1.9)
      • t = -2.29, df = 10, p = 0.023 (one-tailed, α = 0.01)
    • Prime the Real with the Virtual, as the mean value significantly increased.
    Virtual to Real Transfer
  • Real to Virtual Transfer
    • In the past, such an analysis was impossible for life-critical training -- combat military training.
    • Educational and learning applications, the question is valid and viable.
    • H9a: Total Activity: μ Virtual(First) < μ Virtual(After Real)
      • Virtual (First) (M = 0.0, SD = 0.0)
      • Virtual (After Real) (M = 3.8, SD = 2.4)
      • t = -3.88, df = 10, p = 0.00 (one-tailed, α = 0.01)
    • Reinforce the Real with the Virtual, as the mean value significantly increased.
  • Interaction
    • Interaction was found (Total Map Annotations by Group)
      • The one-way, between-subjects ANOVA produced evidence of interaction for the Total map Annotation learning activity
    • H6a: Total Activity: Interaction Groups x Order
      • F(1, 5) = 30.69, p = 0.003.
    • Thus, some factor is effecting the variables in a non-constant way…
    • Something in the real world is having an interesting impact.
  • Salient Events
    • Some probability distribution of events, like a “ Salamander Find ,” that occur only in the real environment, and result in highly salient, personally meaningful memories.
    • “ Salient Events” are critical for learning and become design features
    • If recognized by a teacher, or software, become “Teachable Moments”
    • Episodic Memory anchors entire knowledge ontology
    Salient Event Teachable Moment
  • Conclusions: Real vs. Virtual
    • Real is Superior Overall
    • Virtual match the Real for in-curriculum materials
    • Practice makes Perfect
    • “ Salient Events” as Environmental Driven Opportunities for Teachable Moments for Learning, Uniquely Personal, & UI Critical Design Features
    • Transfer in Both Directions
    • Prime & Reinforce with the Virtual
    • Use Real & Virtual Together for Maximized Learning
  • Study Two: 2X2 ANOVA
    • Study Two: Two Factor ANOVA as a Planed Orthogonal Contrast (POC) on Critical User Interface Software Dimensions (n=64)
      • Factor 1: Visual Fidelity
        • Low Fidelity set to Cartoon Quality
        • High Fidelity set to Photorealistic Quality
      • Factor 2: Navigational Freedom
        • Low Navigational Freedom set to a virtual path
        • High Navigational Freedom allow 360 degree exploration at will
    • UI and Game Design Parameters?
      • Knowledge Gained
      • Salient Events
      • Emotional Reactions of Curiosity, Beauty, Awe & Wonder, Calm & Excitement
      • Acts of Creation & Sharing
  •  
  • Visual Fidelity
  • Navigational Freedom
    • Independent Variables
      • Visual Fidelity: Two Levels: Low (LF) and High (HF)
      • Navigational Freedom: Two Levels: Low (LN) and High (HN)
    • Dependent Variables
      • Knowledge Gained
      • Salient Events
      • Fact Cards Inquired
      • Time in System
      • Emotional Reactions
      • Acts of Creation
    Study Two: 2X2 ANOVA Group LFLN n=16) Visual Fidelity Navigational Freedom Low Visual Fidelity (LF) High Visual Fidelity (HF) Low Navigational Freedom (LN) High Navigational Freedom (HN) Group LFHN n=16) Group HFHN n=16) Group HFLN n=16) Group LFLN n=16) Visual Fidelity Navigational Freedom Low Visual Fidelity (LF) High Visual Fidelity (HF) Low Navigational Freedom (LN) High Navigational Freedom (HN) Group LFHN n=16) Group HFHN n=16) Group HFLN n=16) Group LFLN n=16)
  • Hypothesis
  • Knowledge Gained
  • Results: Knowledge Gained
    • Navigational Freedom shows Trend
      • F (1,60) = 2.71, p = 0.105
    • Visual Fidelity is Strong and Significant
      • F (1,60) = 10.54, p = 0.0019
    • Significant Evidence of Interaction
      • F (1, 60) = 4.85, p = 0.0315
    • Highest Gains in Condition of High Visual Fidelity & High Navigational Freedom
      • (M=37.44, SD = 13.88)
  • Salient Events
  • Results: Salient Events
    • Navigational Freedom shows strong Trend
      • F (1,60) = 3.23, p = 0.0773
    • Visual Fidelity is Strong and Significant
      • F (1,60) = 4.35, p = 0.00413
    • No Interaction
      • F (1, 60) = 1.48, p = 0.2285
    • Highest Counts: Condition of High Visual Fidelity & High Navigational Freedom
      • (M=16.75, SD = 6.27)
  • Emotional Reactions
    • Spearman Rank Correlation Coefficient show
      • Inquiry to Learning (r =0.700**, p = 0.00)
      • Awe & Wonder to Total Attitude, (r=0.727**, p =0.000)
      • Awe & Wonder to Knowledge Gained, (r = 0.273**, p =0.000)
      • Beauty to Awe & Wonder (r =0.506**, p=0.000)
      • And many more on Exploration, Calm, Excitement, Curiosity, Desire to Share, Create, Presence, and Immersion
  • Future Work
  • SEEE Tripartite Model
    • Simulated Ecological Environments for Education (SEEE)
      • Conceptual, theoretical, and empirical framework
      • Used to explain information flows between the world and the human
      • Markov Model?
    http://etd.library.pitt.edu/ETD/available/etd-08072008-141657/unrestricted/MariaCRHarrington.pdf
  • Child Computer Environment Interface
    • Truth,
    • Fidelity,
    • Freedom,
    • Beauty,
    • Awe &
    • Wonder
  • The Virtual Trillium Trail
    • is available here: http://www.virtualtrilliumtrail.com/index.html