Knowing what students know
from game-based learning
David Gibson
Curtin University
The Premise
In an interactive digital-game, traces of a
learner’s progress, problem-solving attempts,
self-expressions and...
Example

Clarke-Midura & Gibson, 2013

• Contexts: Farm, Playground, Science Lab
• Actions: Talking, Testing, Walking to…
...
Interaction Traces = Evidence
There is a need for new frameworks, concepts
and methods for measuring what someone
knows an...
Example

• Ecological rationality & Empirical probability
Clarke-Midura & Gibson, 2013
Sensors
• Wireless EEG
– Facial muscles, emotional
clusters, raw EEG

• Wireless Galvanic Skin
Conductance
– Arousal level...
Anatomy of the System
Helen Chavez & Javier Gomez, ASU
Challenge: New Psychometrics
• What are some of the measurement and
analysis considerations needed to address the
challeng...
Biometric Sensor Nets
• What patterns do we
find?
• How do they change
over time?
• How do they relate to
baseline and
exp...
Network Graphs
Digraphs illustrate
structural
relationships in the
causative factors
during a time slice or
event frame.
Network Analysis
AF3 F7
AF3

Adjacency tables
Centrality

F7
F3
FC5
T7
P7
O1
O2
P8

Digraphs

T8
FC6
F4
F8
AF4
GX
GY

F3

...
Symbolic Regression
Automated
search for
algorithms

Clarke-Midura & Gibson, 2013
New Space for Performance
• Unfold in time
• Cover a multivariate space of possible actions
• Assets contain both intangib...
Example
Clarke-Midura & Gibson, 2013

Students who had
this pattern of
resources were
most likely to
show evidence of
form...
Performance Space Features
• Unconstrained complex multidimensional
stimuli and responses
• Dynamic adaptation of items to...
Research Questions
• What patterns are
found within &
between sensors?
• How do these patterns
relate to baseline and
expe...
Data Dashboard at ASU
Helen Chavez and Javier Gomez
Thinking States

Rise in
uncertainty
and interest

During thinking
Agreement &
concentration
drop
The Game-Based Psychometric
Landscape
• A “do over” for performance assessment
• New ways of performing = new methods of
d...
What Games & Sims Teach
•
•
•
•
•
•
•

Understanding big ideas - systems knowledge
Dealing with time and scale
Practice in...
Conclusion
Methods based in data-mining, machine
learning, model-building and complexity theory
form a theoretical foundat...
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Knowing what students know

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In an interactive digital-game, traces of a learner’s progress, problem-solving attempts, self-expressions and social communications can entail highly detailed and time-sensitive computer-based documentation of the context, actions, processes and products. This talk will present measurement and analysis considerations that are needed to address the challenges of finding patterns and making inferences based on these data. Methods based in data-mining, machine learning, model-building and complexity theory form a new theoretical foundation for dealing with the challenges of time sensitivity, spatial relationships, multiple layers of aggregations at different scales, and the dynamics of complex behavior spaces. Examples of these considerations in game-based learning analytics are presented and discussed, with implications for game-based e-learning design.

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Knowing what students know

  1. 1. Knowing what students know from game-based learning David Gibson Curtin University
  2. 2. The Premise In an interactive digital-game, traces of a learner’s progress, problem-solving attempts, self-expressions and social communications can entail highly detailed and time-sensitive computer-based documentation of the context, actions, processes and products.
  3. 3. Example Clarke-Midura & Gibson, 2013 • Contexts: Farm, Playground, Science Lab • Actions: Talking, Testing, Walking to… • Processes & Products: Test Results, Explanations
  4. 4. Interaction Traces = Evidence There is a need for new frameworks, concepts and methods for measuring what someone knows and can do based on game interactions and artifacts created during serious play Why? (It’s a mouthful) Ubiquitous, unobtrusive, interactive big data created by people working in digital media performance spaces
  5. 5. Example • Ecological rationality & Empirical probability Clarke-Midura & Gibson, 2013
  6. 6. Sensors • Wireless EEG – Facial muscles, emotional clusters, raw EEG • Wireless Galvanic Skin Conductance – Arousal level • Eye Tracker – Gaze-point, duration, mouse-clicks • Haptics – Button presses, head tilt
  7. 7. Anatomy of the System Helen Chavez & Javier Gomez, ASU
  8. 8. Challenge: New Psychometrics • What are some of the measurement and analysis considerations needed to address the challenges of finding patterns and making inferences based on data from digital learning experiences?
  9. 9. Biometric Sensor Nets • What patterns do we find? • How do they change over time? • How do they relate to baseline and experimental activities?
  10. 10. Network Graphs Digraphs illustrate structural relationships in the causative factors during a time slice or event frame.
  11. 11. Network Analysis AF3 F7 AF3 Adjacency tables Centrality F7 F3 FC5 T7 P7 O1 O2 P8 Digraphs T8 FC6 F4 F8 AF4 GX GY F3 FC5 T7 P7 O1 O2 P8 T8 FC6 F4 F8 AF4 GX GY
  12. 12. Symbolic Regression Automated search for algorithms Clarke-Midura & Gibson, 2013
  13. 13. New Space for Performance • Unfold in time • Cover a multivariate space of possible actions • Assets contain both intangible (e.g. value, meaning, sensory qualities, and emotions) and tangible components (e.g. media, materials, time and space) NOTE: Asset utilization during performance provides evidence of what a user knows and can do
  14. 14. Example Clarke-Midura & Gibson, 2013 Students who had this pattern of resources were most likely to show evidence of forming a hypothesis
  15. 15. Performance Space Features • Unconstrained complex multidimensional stimuli and responses • Dynamic adaptation of items to user, which entails interactivity and dependency • Nonlinear behaviors with both temporal and spatial components NOTE: Higher order and creative thinking is supported in such a space
  16. 16. Research Questions • What patterns are found within & between sensors? • How do these patterns relate to baseline and experimental activities?
  17. 17. Data Dashboard at ASU Helen Chavez and Javier Gomez
  18. 18. Thinking States Rise in uncertainty and interest During thinking Agreement & concentration drop
  19. 19. The Game-Based Psychometric Landscape • A “do over” for performance assessment • New ways of performing = new methods of data capture, analysis and display • Complex tasks and artifacts containing – higher order thinking (e.g. decision sequences) – physical performances demonstrating skills – emotional responses
  20. 20. What Games & Sims Teach • • • • • • • Understanding big ideas - systems knowledge Dealing with time and scale Practice in decision-making Active problem-solving Concepts, strategies, & tactics Understanding processes beyond experience Practice makes improvement (Aldrich, 2005)
  21. 21. Conclusion Methods based in data-mining, machine learning, model-building and complexity theory form a theoretical foundation for dealing with the challenges of time sensitivity, spatial relationships, multiple layers of aggregations at different scales, and the dynamics of complex behavior spaces.
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