G4LI’s Jan Plass and Bruce Homer Google TechTalk

Games for Learning Institute Co-Director Jan Plass and Co-PI Bruce Homer presented a TechTalk on the research and design of learning games at Google recently. I recommend starting about 24 minutes in! [my notes are below]

Four main functions of learning games
– prepare for future learning
– for specific learning goals (content & skills)
– for practice of existing skills
– development of 21st century skills

Games offer many assessment opportunities
1) Embedded assessment: user logs, event logs, biometrics

2) Possible variables to measure through embedded assessments:
– General trait – spatial ability, verbal ability, executive fxn
– General state – prior knowledge, learning strats, goal orientation, self-regulation
– Situation-specific state – engagement, emotion, cognitive load, situational interest
– Learning Outcomes – skills, knowledge, comprehension, transfer

What is required, though, is the design of thoughtful game mechanics.

If designed well, game mechanics for learning *may* provide details on learning process and outcome and they *may* reveal insights into learner variables.

Game mechanics for learning need to be specifically designed to:
– Engage the player in meaningful learning activities
– Elicit relevant behaviors that can be observed through the user log and interpreted to reveal learning process, outcome, and learner variables
– Be fun and engaging

Learning Mechanics & Assessment Mechanics need to be translated into the Game Mechanics

Learning Mechanics
– Patterns of behavior or building blocks of learner interactivity
– May be a single action or a set of interrelated actions which form the essential learning activity that is repeated throughout a game
– Learning Mechanic: “Apply rules to solve problems” -> Game Mechanics Variant: “Implode”

-Learning Mechanic “Arrange concepts to solve Problems” -> Game Mechanics Variant: “FlightControl” or  “Toobz”

-Learning Mechanic: “Select Sets to solve Problems” -> Game Mechanics Variant: “Osmosis”

Criteria for translating Learning Mechanics into Game Mechanics
– LM need to be grounded in learning sciences/learning theory
– Describe meaningful interaction with specific subject
– Based on theoretical model of interactivity
– Provide different but equally appropriate solutions to problems so that you have a choice

G4LI building a library of Learning Mechanics that is connected to game mechanic examples. Innovation is to start with how we learn and then pick game mechanics.

Requirements for selecting Game Mechanics based on Learning Mechanic
– GM must not introduce excessive amounts of extraneous cognitive load (e.g., narrative, resource management)
– GM must not reduce amounts of germane cognitive load/mental effort by too much
– GM must not introduce unnecessary confounds (fine motor skills, content knowledge or skills, etc.)

Assessment Mechanics
– Patterns of behavior or building blocks of diagnostic interactivity
– May be a single action or a set of interrelated actions which form the essential diagnostic activity that is repeated throughout a game

Assessment Mechanic: Apply Rules to solve Problems
– Learner selects among different rules and shows where/how they apply

Evidence-Centered Design:
– Student/competency Model – what should be assessed? What competencies am I interested in?
– Evidence Model – what kind of evidence would I accept/what behaviors reveal these constructs?
– Task model – what tasks should elicit these behaviors?

Criteria for Assessment Mechanics
– Based on Evidence Model
– Describe Aspects of Task Model
– Test Theoretical Concerns
– Create repeated exposures to similar problems to allow for multiple observations of the behavior of interest
– Make explicit the steps learners used for problem solving
– Assessment character may or may not be obvious to learner

G4LI Library of Assessment Mechanics that have Game Mechanics as examples
Ex LM: Learners apply rules to solve problems; Learner chooses how different items are to be arranged in space and time in order to solve a problem; Learner selects different items that belong to each other in time or space.

Requirements:
– reduce extraneous cognitive load
– germane cognitive load/mental effort
– fine motor skills
– content knowledge or skills
– emotional response

Summary:
– Design of GM benefits from separation of LM, AM
– Learning Theory, ECD provide foundation
– Multiple Game Mechanics (in multiple game genres) possible for each Learning/Assessment Mechanic
– Need to consider additional confounds or constraints they introduce

Log file analysis

DataCrypt for Data Collection
– Log file open standard for game research
– Tags: Common Core Standards, skills
– User actions
– Game Events
– Biometric Data
– Cloud-based data storage
– Analysis tools for Data Viz and Data Mining

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