1. 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

2. Components of Assessment Architecture Create assessment architecture (Your Example) Assessment Validity Overview

3. What is so hard?

4. What are some of your challenges?

5. Passed the Game

6. Gameplay Log data Domain

7. 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

8. How can we meet the challenge?

9. Assessment requirements Technology requirements Instructional requirements Front-end Efforts Support Effectiveness

10. Model-Based Engineering Design Communication Collaboration

11. Model-Based Engineering Design z

12. Part One ASSESSMENT VALIDITY

13. Assessment (noun) = Test What Is Assessment?

14. = 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

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

16. Games As Formative Assessment Games as Formative Assessment: Use and interpretation of game performance information with intent to adapt learning, such as provide feedback. Games as Formative Assessment: Use and interpretation of game performance information with intent to adapt learning, such as provide feedback.

17. What is Validity?

18. Assessment Validity as a Quality Judgment Critical Analysis Legal Judgment Scientific Process

19. = Assessment Validity Bringing evidence and analysis to evaluate the propositions of interpretive argument. (Linn, 2010) Bringing evidence and analysis to evaluate the propositions of interpretive argument. (Linn, 2010) ASSESSMENT VALIDITY

20. 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. 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.

21. 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)

22. 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

23. motion pre 1 speed direction duration o1o1 o2o2 o3o3 pre 2 pre 3 pre 4 pre 5 o4o4 o5o5 o6o6 o7o7 o8o8 BACKGROUND LAYER Prior knowledge Game experience Age, sex Language proficiency CONSTRUCT LAYER Construct, subordinate constructs, and inter- dependencies INDICATOR LAYER Behavioral evidence of construct EVENT LAYER Player behavior and game states FUNCTION LAYER Computes indicator value f n (e 1, e 2, e 3,...; s 1, s 2, s 3,...): Computes an indicator value given raw events and game states Game events and states (e 1, e 2, e 3,...; s 1, s 2, s 3,...)

24. 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

25. Part Two ASSESSMENT ARCHITECTURE

26. Components of Assessment Architecture COGNITIVE DEMANDS defines targeted knowledge, skills, abilities, practices domain-independent descriptions of learning 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 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 TASK SPECIFICATIONS defines what the students (tasks/scenarios, materials, actions) defines rules and constraints) defines scoring

27. 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 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

28. Domain Representation External representation(s) of domain- specific models Defines universe (or boundaries) of what is to be learned and tested External representation(s) of domain- specific models Defines universe (or boundaries) of what is to be learned and tested

29. Ontologies Item specifications Example: Math Knowledge specifications

30. 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 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

31. 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 acceleration Strengths 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, speed Speed change: increase in acceleration Direction: 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 movement Allowable variations on planar objects: Mass, height and width, location of object in the room, surface material Constraints 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.

32. Components of Computational Model

33. Components of Decision Model Do nothing: move on, end task Get 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

34. 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

35. 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 36

37. Person (prior knowledge and experience) Task characteristics Context (test, simulation, game) Fixed Variables + + Performance to be Assessed Assessment Architecture 37 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 38 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 39 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?

40. Potential Course of Actions Repeat Same Trial Get more evidence or information Perform Similar Task Ask a questio n No intervention Move OnEnd TaskInstructional Remediation Intervene Give Elaborated Feedback Worked Example More Information Modify Task New Task With Reduced Difficulty Add Scaffoldin g