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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  • 1. Knowing what students know from game-based learning David Gibson Curtin University

2. The Premise In an interactive digital-game, traces of a learners 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. ExampleClarke-Midura & Gibson, 2013 Contexts: Farm, Playground, Science Lab Actions: Talking, Testing, Walking to Processes & Products: Test Results, Explanations 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? (Its a mouthful) Ubiquitous, unobtrusive, interactive big data created by people working in digital media performance spaces 5. Example Ecological rationality & Empirical probability Clarke-Midura & Gibson, 2013 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. Anatomy of the System Helen Chavez & Javier Gomez, ASU 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. Biometric Sensor Nets What patterns do we find? How do they change over time? How do they relate to baseline and experimental activities? 10. Network Graphs Digraphs illustrate structural relationships in the causative factors during a time slice or event frame. 11. Network Analysis AF3 F7 AF3Adjacency tables CentralityF7 F3 FC5 T7 P7 O1 O2 P8DigraphsT8 FC6 F4 F8 AF4 GX GYF3FC5 T7P7O1 O2 P8T8FC6 F4F8AF4 GX GY 12. Symbolic Regression Automated search for algorithmsClarke-Midura & Gibson, 2013 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. Example Clarke-Midura & Gibson, 2013Students who had this pattern of resources were most likely to show evidence of forming a hypothesis 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. Research Questions What patterns are found within & between sensors? How do these patterns relate to baseline and experimental activities? 17. Data Dashboard at ASU Helen Chavez and Javier Gomez 18. Thinking StatesRise in uncertainty and interestDuring thinking Agreement & concentration drop 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. 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. 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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