Data challenges: Leveraging a simulation to assess learning
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The document discusses leveraging data from digital simulations to assess learning. It notes that simulation data provides detailed, time-sensitive documentation of learner actions, processes, and products. New methods are needed to analyze patterns in this "big data" from things like sensors, and relate patterns to learner activities and performance. The document provides examples of using network graphs and machine learning on simulation data to measure higher-order thinking and infer what learners know based on their in-simulation behaviors and artifacts.
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Data challenges: Leveraging a simulation to assess learning
- 1. Data Challenges: Leveraging a Simulation to Assess Learning David Gibson Curtin University
- 2. The Premise In an interactive digital simulation, 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. New 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
- 4. 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
- 5. Anatomy of the System Helen Chavez & Javier Gomez, ASU
- 6. Data Dashboard at ASU Helen Chavez and Javier Gomez
- 7. Research Questions • What patterns are found within & between sensors? • How do these patterns relate to baseline and experimental activities?
- 8. 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 (fast, wide variety & voluminous) created by people working in digital media performance spaces
- 9. 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?
- 10. Example Clarke-Midura & Gibson, 2013 • Contexts: Farm, Playground, Science Lab • Actions: Talking, Testing, Walking to… • Processes & Products: Test Results, Explanations
- 11. Example • Ecological rationality & Empirical probability Clarke-Midura & Gibson, 2013
- 12. Network Graphs Digraphs illustrate structure in the causative factors during a time slice or event frame.
- 13. Network Analysis AF3 F7 AF3 Adjacency tables Centrality F7 F3 FC5 T7 P7 O1 O2 P8 T8 FC6 F4 F8 AF4 GX GY F3 FC5 T7 P7 O1 O2 P8 T8 FC6 F4 F8 AF4 GX GY
- 14. Symbolic Regression Automated search for algorithms Clarke-Midura & Gibson, 2013
- 15. 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
- 16. Example Clarke-Midura & Gibson, 2013 Students who had this pattern of resources were most likely to show evidence of forming a hypothesis
- 17. 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
- 18. Thinking States Rise in uncertainty and interest During thinking Agreement & concentration drop
- 19. 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.