Data challenges: Leveraging a simulation to assess learning

Data Challenges:Leveraging a Simulation

to Assess Learning

David GibsonCurtin University

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.

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

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

Anatomy of the System

Helen Chavez & Javier Gomez, ASU

Data Dashboard at ASUHelen Chavez and Javier Gomez

Research Questions

• What patterns are found within & between sensors?

• How do these patterns relate to baseline and experimental activities?

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

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?

Example

• Contexts: Farm, Playground, Science Lab• Actions: Talking, Testing, Walking to…• Processes & Products: Test Results, Explanations

Clarke-Midura & Gibson, 2013

Example

• Ecological rationality & Empirical probabilityClarke-Midura & Gibson, 2013

Network Graphs

Digraphs illustrate structure in the causative factors during a time slice or event frame.

Network Analysis

Adjacency tablesCentrality

AF3 F7 F3 FC5 T7 P7 O1 O2 P8 T8 FC6 F4 F8 AF4 GX GY

AF3

F7

F3

FC5

T7

P7

O1

O2

P8

T8

FC6

F4

F8

AF4

GX

GY

Symbolic Regression

Automated search for algorithms


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

Example

Students who had this pattern of resources were most likely to show evidence of forming a hypothesis


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

Thinking States

Rise inuncertainty and interest

Agreement & concentration drop

During thinking

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.

Data challenges: Leveraging a simulation to assess learning

Education

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