Serious Play Conference

Los Angeles, CA – July 21, 2012

Girlie C. Delacruz and Ayesha L. Madni

Setting Up Learning Objectives and Measurement for Game Design

Components of Assessment Architecture

Create assessment architecture (Your Example)

Assessment Validity

Overview

What is so hard?

What are some of your challenges?

Passed the Game

Gameplay

Log data

Domain

Challenges We Have

• Translating objectives into assessment outcomes– Purpose of assessment information– Communication between designers and educators

• Game is developed—need to assess its effectiveness– Cannot change code, wraparounds

How can we meet the challenge?

Assessment requirements

Technology requirements

Instructional requirements

Front-end Efforts Support Effectiveness

Model-Based Engineering Design

Communication

Collaboration

Model-Based Engineering Design

z

Part One

ASSESSMENT VALIDITY

Assessment (noun) = Test

What Is Assessment?

=

Assessment As A Verb

Process of drawing reasonable inferences about what a person

knows by evaluating what they say or do in a given situation.

ASSESSMENT

Games As Formative Assessment

Formative Assessment:

Use and interpretation of task performance information with intent to adapt learning, such as provide feedback. (Baker, 1974; Scriven, 1967).

Games As Formative Assessment

Games as Formative Assessment:

Use and interpretation of game performance information with intent to adapt learning, such as provide feedback.

What is Validity?

Assessment Validity as a Quality Judgment

Critical Analysis

Legal Judgment

Scientific Process

=

Assessment Validity

Bringing evidence and analysis to evaluate the propositions of interpretive argument.

(Linn, 2010)

ASSESSMENT VALIDITY

How Does This Relate to Design?

① Identification of the inferences to be made.• What do you want to be able to say?

② Specificity about the expected uses and users of the learning system.• Define boundaries of the training system

• Determine need for supplemental resources

③ Translate into game mechanics

④ Empirical analysis of judgment of performance within context of assumptions.

What do you want to be able to say about the gameplayer(s)?

• Player mastered the concepts.• How do you know?• Because they did x, y, z (player history)• Because they can do a, b, c (future events)

Identify Key Outcomes: Defining Success Metrics

• Quantitative Criteria (Generalizable)– % of successful levels/quests/actions– Progress into the game– Changes in performance

• Errors• Time spent on similar levels• Correct moves

• Qualitative Criteria (Game-specific)– Patterns of gameplay– Specific actions

motion

pre1

speed direction duration

o1 o2 o3

pre2 pre3 pre4 pre5

o4 o5 o6 o7 o8

BACKGROUND LAYER• Prior knowledge• Game experience• Age, sex• Language

proficiency

CONSTRUCT LAYERConstruct, subordinate constructs, and inter-dependencies

INDICATOR LAYERBehavioral evidence of construct

EVENT LAYERPlayer behavior and game states

FUNCTION LAYERComputes indicator value

fn(e1, e2, e3, ...; s1, s2, s3, ...): Computes an indicator value given raw events and game states

Game events and states (e1, e2, e3, ...; s1, s2, s3, ...)

General Approach

• Derive structure of measurement model from ontology structure

• Define “layers”– Background: Demographic and other variables that may

moderate learning and game performance– Construct: Structure of knowledge dependencies– Indicator: Input data (evidence) of construct– Function: Set of functions that operate over raw event

stream to compute indicator value– Event: Atomic in-game player behaviors and game states

• Assumptions– Chain of reasoning among the layers are accurate

Part Two

ASSESSMENT ARCHITECTURE

Components of Assessment Architecture

COGNITIVE DEMANDS

• defines targeted knowledge, skills, abilities, practices

• domain-independent descriptions of learning

DOMAIN REPRESENTATION• instantiating domain-specific related

information and practices• guides development• allows for external review

TASK SPECIFICATIONS

• defines what the students (tasks/scenarios, materials, actions)

• defines rules and constraints)

• defines scoring

Cognitive Demands

What kind of thinking do you want capture?

• Adaptive, complex problem solving

• Conceptual, procedural, and systemic learning of content

• Transfer

• Situation awareness and risk assessment

• Decision making

• Self-regulation

• Teamwork

• Communication

Domain Representation

• External representation(s) of domain-specific models

• Defines universe (or boundaries) of what is to be learned and tested

Ontologies

Item specifications

Example: Math

Knowledge specifications

Task Specifications

① Operational statement of content and behavior for task

• Content = stimulus/scenario (what will the users see?)

② Behavior = what student is expected to do/ response (what will the users do?)

• Content limits

③ Rules for generating the stimulus/scenario posed to the student

• Permits systematic generation of scenarios with similar attributes

• Response descriptions

④ Maps user interactions to cognitive requirements

Force and Motion Pushes and pulls, can have different strengths and directions. Pushing and pulling on an object can change the speed or direction of its motion and can start or stop it. Each force acts on one particular object and has both strength and a direction. Energy The faster a given object is moving, the more energy it possesses

NGSS performance expectation

Plan and conduct an investigation to compare the effects of different strengths of pushes on the motion of an object (K-PS2-1).

Analyze data to determine if a design solution works as intended to change the speed or direction of an object with a push (K-PS2-2).

Content limits Effects: change in position; increased or decreased accelerationStrengths of pushes: Qualitative (small, medium, big), or quantitative Type of Motion: Rotational Constraints on planar objects: Must be something that can be pushed horizontally and attached to its fulcrum (e.g., the door to a house) Allowable variations on objects: Mass, height and width, location of object Constraints on fulcrum objects: Must be attached to the planar object; position of fulcrum object cannot be changed

Data: distance, slope, time, speedSpeed change: increase in accelerationDirection: Vertical movement Constraints on planar objects: Must be something flat (e.g., book, frame, ruler) that can be placed on another object and can be pushed in a downward movementAllowable variations on planar objects: Mass, height and width, location of object in the room, surface materialConstraints on fulcrum objects: The structural properties of the fulcrum should support some, but not all of the set of planar objects; position of fulcrum object can be changed

Targeted science and engineering practice(s)

Ask questions that can be investigated based on patterns such as cause and effect relationships.

Use observations to describe patterns and/or relationships in that natural and designed world(s) in order to answer scientific questions and solve problems.

Response description

Ask questions: Query the MARI about the properties of the objects (e.g., what is the distance between the hinge and where I pushed) based on observed outcomes (e.g., how hard it was to push the door, or how far the door moved).

Use observations: use snapshot images of activity in the HRLA with overlaid measurement data generated by the MARI to sort situations based on the physical features, behaviors, or functional roles in the design.

Task complexity Student only has 4 attempts to pass the ball to the girl and can only vary position and strength of push.

Easy: Student can vary the position and strength of the push, but must apply force by placing additional objects on the planar object and pushing downward with both hands (to connect the kinesthetic experience of applying the force with hands on experience of the object). Harder: Student can vary both the position and strength of the push and how the planar object is placed on the fulcrum (e.g., load is moved closer or further away from fulcrum)

Available resources Iconic and graphical representation of underlying physics laws will be on the screen, and will change based on student actions. Guided questions will ask students about distance, mass, force magnitude and direction, height, and slope based on observed outcomes.

Components of Computational Model

Components of Decision Model

Do nothing: move on, end taskGet more evidence or information: repeat same task, perform similar task, ask a question

Intervene (instructional remediation): give elaborated feedback, worked example or add scaffolding, more supporting information

Intervene (task modification): new task (reduced or increased difficulty), new task (qualitatively different)

Courses of Action

Components of Decision Model

Confidence of diagnosis : How certain are we about hypothesized causal relation?

Consequence of misdiagnosis: What happens if we get it wrong? What are the implications of ignoring other possible states or causal relations?

Effectiveness of intervention: How effective is the intervention we will give after diagnosis?

Constraints: Do we have to efficiency concerns with respect to time or resource constraints?

Decision Factors

Part Three

ASSESSMENT ARCHITECTURE(YOUR EXAMPLE)

36

Person (prior knowledge and experience)

Task characteristics

Context (test, simulation, game)

Fixed Variables

+

+

Assumptions and Design Rationale

Assessment Architecture

37

Person (prior knowledge and experience)

Task characteristics

Context (test, simulation, game)

Fixed Variables

+

+

Performance to be Assessed

Assessment Architecture

Observed Event(s)

What happened?(Raw data, scored

information?)

38

Person (prior knowledge and experience)

Task characteristics

Context (test, simulation, game)

Fixed Variables

+

+

Assessment Architecture

Observed Event(s)

What happened?(Raw data, scored

information?)

Translation

What does this mean?Judgment of performance

39

Person (prior knowledge and experience)

Task characteristics

Context (test, simulation, game)

Fixed Variables

+

+

Assessment Architecture

Observed Event(s)

What happened?(Raw data, scored

information?)

Translation

What does this mean?

Assessment Validation

Inferences

What are the potential causes of the observed events?

Lack of Knowledge?

Context?

Characteristics of the task?

Not sure?

Potential Course of Actions

Repeat Same Trial

Get more evidence or information

Perform Similar Task

Ask a question

No intervention

Move On End Task Instructional Remediation

Intervene

Give Elaborated Feedback

Worked Example

More Information

Modify Task

New Task With

Reduced Difficulty

Add Scaffolding