1. AERA 2010 Robert L. Linn Lecture Slide 1 May 1, 2010 Integrating Measurement and Sociocognitive Perspectives in Educational Assessment Robert J. Mislevy University of Maryland Robert L. Linn Distinguished Address Sponsored by AERA Division D. Presented at the Annual Meeting of the American Educational Research Association, Denver, CO, May 1, 2010. This work was supported by a grant from the Spencer Foundation.

2. AERA 2010 Robert L. Linn Lecture Slide 2 May 1, 2010 Messick, 1994 [W]hat complex of knowledge, skills, or other attribute should be assessed... Next, what behaviors or performances should reveal those constructs, and what tasks or situations should elicit those behaviors?

3. AERA 2010 Robert L. Linn Lecture Slide 3 May 1, 2010 Snow & Lohman, 1989 Summary test scores, and factors based on them, have often been though of as “signs” indicating the presence of underlying, latent traits. … An alternative interpretation of test scores as samples of cognitive processes and contents … is equally justifiable and could be theoretically more useful.

4. AERA 2010 Robert L. Linn Lecture Slide 4 May 1, 2010 Roadmap Rationale Model-based reasoning A sociocognitive perspective Assessment arguments Measurement models & concepts Why are these issues important? Conclusion

5. AERA 2010 Robert L. Linn Lecture Slide 5 May 1, 2010 Measurement frame Sociocognitive frame Rationale

6. AERA 2010 Robert L. Linn Lecture Slide 6 May 1, 2010 Rationale An articulated way to think about assessment: Understand task & use situations in “emic” sociocognitive terms. Identify the shift in to “etic” terms in task-level assessment arguments. Examine the synthesis of evidence across tasks in terms of model-based reasoning. Reconceive measurement concepts. Draw implications for assessment practice.

7. AERA 2010 Robert L. Linn Lecture Slide 7 May 1, 2010 Model-Based Reasoning

8. Entities and relationships Representational Form B Representational Form A y=ax+b (y-b)/a=x Mappings among representational systems Real-World Situation Reconceived Real-World Situation Measurement concepts Measurement models

9. Representational Form B Representational Form A y=ax+b (y-b)/a=x Entities and relationships in lower-level model Reconceived Entities and relationships in higher-level model Mappings among representational systems Real-World SituationReconceived Real-World Situation Measurement concepts Measurement models Sociocognitive concepts

10. AERA 2010 Robert L. Linn Lecture Slide 10 May 1, 2010 A Sociocognitive Perspective

11. AERA 2010 Robert L. Linn Lecture Slide 11 May 1, 2010 Some Foundations Themes from, e.g., cog psych, linguistics, neuroscience, anthropology: »Connectionist metaphor, associative memory, complex systems (variation, stability, attractors) Situated cognition & information processing »E.g., Kintsch’s Construction-Integration (CI) theory of comprehension; diSessa’s “knowledge in pieces” Intrapersonal & Extrapersonal patterns

12. AERA 2010 Robert L. Linn Lecture Slide 12 May 1, 2010 Some Foundations Extrapersonal patterns: »Linguistic: Grammar, conventions, constructions »Cultural models: What ‘being sick’ means, restaurant script, apology situations »Substantive: F=MA, genres, plumbing, etc. Intrapersonal resources: »Connectionist metaphor for learning »Patterns from experience at many levels

13. AERA 2010 Robert L. Linn Lecture Slide 13 May 1, 2010 B Inside B Inside AA observablenot observable

14. AERA 2010 Robert L. Linn Lecture Slide 14 May 1, 2010 B Inside B Inside A Context A A la Kintsch: Propositional content of text / speech… and internal and external aspects of context …

15. AERA 2010 Robert L. Linn Lecture Slide 15 May 1, 2010 B Inside B Inside A Context A The C in CI theory is Construction: Activation of both relevant and irrelevant bits from LTM, past experience. All L/C/S levels involved. Example: Chemistry problems in German. The C in CI theory is Construction: Activation of both relevant and irrelevant bits from LTM, past experience. All L/C/S levels involved. Example: Chemistry problems in German. If a pattern hasn’t been developed in past experience, it can’t be activated (although it may get constructed in the interaction). A relevant pattern from LTM may be activated in some contexts but not others (e.g., physics models). If a pattern hasn’t been developed in past experience, it can’t be activated (although it may get constructed in the interaction). A relevant pattern from LTM may be activated in some contexts but not others (e.g., physics models).

16. AERA 2010 Robert L. Linn Lecture Slide 16 May 1, 2010 B Inside B Inside A Context A The I in CI theory, Integration: Situation model: synthesis of coherent / reinforced activated L/C/S patterns The I in CI theory, Integration: Situation model: synthesis of coherent / reinforced activated L/C/S patterns

17. AERA 2010 Robert L. Linn Lecture Slide 17 May 1, 2010 B Inside B Inside A Context A Situation model is also the basis of planning and action.

18. AERA 2010 Robert L. Linn Lecture Slide 18 May 1, 2010 B Inside B Inside A Context A

19. AERA 2010 Robert L. Linn Lecture Slide 19 May 1, 2010 B Inside B Inside A Context A Ideally, activation of relevant and compatible intrapersonal patterns…

20. AERA 2010 Robert L. Linn Lecture Slide 20 May 1, 2010 B Inside B Inside A Context A to lead to (sufficiently) shared understanding; i.e., co-constructed meaning. Persons’ capabilities, situations, and performances are intertwined – Meaning co-determined, through L/C/S patterns Persons’ capabilities, situations, and performances are intertwined – Meaning co-determined, through L/C/S patterns

21. AERA 2010 Robert L. Linn Lecture Slide 21 May 1, 2010 What can we say about individuals? Use of resources in appropriate contexts in appropriate ways; i.e., Attunement to targeted L/C/S patterns: Recognize markers of externally-viewed patterns? Construct internal meanings in their light? Act in ways appropriate to targeted L/C/S patterns? What is the range and circumstances of activation? (variation of performance across contexts)

22. AERA 2010 Robert L. Linn Lecture Slide 22 May 1, 2010 Assessment Arguments

23. AERA 2010 Robert L. Linn Lecture Slide 23 May 1, 2010 Messick, 1994 [W]hat complex of knowledge, skills, or other attribute should be assessed... Next, what behaviors or performances should reveal those constructs, and what tasks or situations should elicit those behaviors?

24. AERA 2010 Robert L. Linn Lecture Slide 24 May 1, 2010 Toulmin’s Argument Claim Backing unless since Warrant Alternative explanation so Data Structure

25. Student acting in assessment situation Alternative explanations unless on account of Backing concerning assessment situation Warrant concerning assessment since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation Warrant concerning evaluation since Concerns features of (possibly evolving) context as seen from the view of the assessor – in particular, those seen as relevant to targets of inference. Evaluation of performance seeks evidence of attunement to features of targeted L/C/S patterns. Note the move from the emic to the etic! Choice in light of assessment purpose and conception of capabilities. Note the move from the emic to the etic! Choice in light of assessment purpose and conception of capabilities. Depends on contextual features implicitly, since evaluated in light of targeted L/C/S patterns.

26. Student acting in assessment situation on account of Backing concerning assessment situation Alternative explanations unless Warrant concerning assessment since Warrant concerning evaluation since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation “Hidden” aspects of context—not in test theory model but essential to argument: What attunements to linguistic cultural / substantive patterns can be presumed or arranged for among examinees, to condition inference re targeted l/c/s patterns? “Hidden” aspects of context—not in test theory model but essential to argument: What attunements to linguistic cultural / substantive patterns can be presumed or arranged for among examinees, to condition inference re targeted l/c/s patterns? Fundamental to situated meaning of student variables in measurement models; Both critical and implicit. Fundamental to situated meaning of student variables in measurement models; Both critical and implicit.

27. Student acting in assessment situation on account of Backing concerning assessment situation Alternative explanations unless Warrant concerning assessment since Warrant concerning evaluation since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation Macro features of performance Micro features of performance Unfolding situated performance Micro features of situation as it evolves Macro features of situation Time Features of context arise over time as student acts / interacts. Features of performance evaluated in light of emerging context. Especially important in simulation, game, and extended performance contexts (e.g., Shute)

28. Student acting in assessment situation on account of Backing concerning assessment situation Alternative explanations unless Warrant concerning assessment since Warrant concerning evaluation since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation Design Argument

29. Claim about student in use situation Other information concerning student vis a vis use situation Warrant concerning use situation since on account of Alternative explanations unless Design Argument Use Argument Data concerning use situation Student acting in assessment situation on account of Backing concerning assessment situation Alternative explanations unless Warrant concerning assessment since Warrant concerning evaluation since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation Backing concerning use situation (Bachman)

30. Claim about student in use situation Other information concerning student vis a vis use situation Warrant concerning use situation since on account of Alternative explanations unless Design Argument Use Argument Data concerning use situation Student acting in assessment situation on account of Backing concerning assessment situation Alternative explanations unless Warrant concerning assessment since Warrant concerning evaluation since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation Backing concerning use situation (Bachman) Claim about student is output of the assessment argument, input to the use argument. How it is cast depends on psychological perspective and intended use. When measurement models are used, the claim is an etic synthesis of evidence, expressed as values of student-model variable(s).

31. Claim about student in use situation Other information concerning student vis a vis use situation Warrant concerning use situation since on account of Alternative explanations unless Design Argument Use Argument Data concerning use situation Student acting in assessment situation on account of Backing concerning assessment situation Alternative explanations unless Warrant concerning assessment since Warrant concerning evaluation since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation Backing concerning use situation

32. Claim about student in use situation Other information concerning student vis a vis use situation Warrant concerning use situation since on account of Alternative explanations unless Design Argument Use Argument Data concerning use situation Student acting in assessment situation on account of Backing concerning assessment situation Alternative explanations unless Warrant concerning assessment since Warrant concerning evaluation since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation Backing concerning use situation

33. Claim about student in use situation Other information concerning student vis a vis use situation Warrant concerning use situation since on account of Alternative explanations unless Design Argument Use Argument Data concerning use situation Student acting in assessment situation on account of Backing concerning assessment situation Alternative explanations unless Warrant concerning assessment since Warrant concerning evaluation since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation Backing concerning use situation

34. Claim about student in use situation Other information concerning student vis a vis use situation Warrant concerning use situation since on account of Alternative explanations unless Design Argument Use Argument Data concerning use situation Student acting in assessment situation on account of Backing concerning assessment situation Alternative explanations unless Warrant concerning assessment since Warrant concerning evaluation since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation Backing concerning use situation Warrant for inference: Increased likelihood of activation in use situation if was activated in task situations. What features do tasks and use situations share? Implicit in trait arguments Explicit in sociocognitive arguments What features do tasks and use situations share? Implicit in trait arguments Explicit in sociocognitive arguments Empirical question: Degrees of stability, ranges and conditions of variability (Chalhoub-Deville)

35. Claim about student in use situation Other information concerning student vis a vis use situation Warrant concerning use situation since on account of Alternative explanations unless Design Argument Use Argument Data concerning use situation Student acting in assessment situation on account of Backing concerning assessment situation Alternative explanations unless Warrant concerning assessment since Warrant concerning evaluation since Warrant concerning task design since Other information concerning student vis a vis assessment situation so Claim about student Data concerning student performance Data concerning task situation Backing concerning use situation What features do tasks and use situations not have in common? Use situation features call for other L/C/S patterns that weren’t in task and may or may not be in examinee’s resources. Target patterns activated in task but not use context. Target patterns activated in use but not task context. Issues of validity & generalizability e.g., “method factors” Use situation features call for other L/C/S patterns that weren’t in task and may or may not be in examinee’s resources. Target patterns activated in task but not use context. Target patterns activated in use but not task context. Issues of validity & generalizability e.g., “method factors” Knowing about relation of target examinees and use situations strengthen inferences “bias for the best” (Swain, 1985) Knowing about relation of target examinees and use situations strengthen inferences “bias for the best” (Swain, 1985)

36. AERA 2010 Robert L. Linn Lecture Slide 36 May 1, 2010 Multiple Tasks Claim about student … D p1 OI 1 A1A1 D s1 D p1 OI 2 A2A2 D s2 D p2 D p1 OI n AnAn D sn D pn Synthesize evidence from multiple tasks, in terms of proficiency variables in a measurement model Snow & Lohman’s sampling What accumulates? L/C/S patterns, but variation What is similar from analyst’s perspective need not be from examinee’s.

37. AERA 2010 Robert L. Linn Lecture Slide 37 May 1, 2010 AS IF Tendencies for certain kinds of performance in certain kinds of situations expressed as student model variables . Probability models for individual performances (X) modeled as probabilistic functions of  – variability. Probability models permit sophisticated reasoning about evidentiary relationships in complex and subtle situations, BUT they are models, with all the limitations implied! Measurement Models & Concepts

38. AERA 2010 Robert L. Linn Lecture Slide 38 May 1, 2010 Xs result from particular persons calling upon resources in particular contexts (or not, or how) Mechanically  s simply accumulate info across situations Our choosing situations and what to observe drives their situated meaning. Situated meaning of  s are tendencies toward these actions in these situations that call for certain interactional resources, via L/C/S patterns. Measurement Models & Concepts

39. AERA 2010 Robert L. Linn Lecture Slide 39 May 1, 2010 Classical Test Theory Probability model: “true score” = stability along implied dimension, “error” = variation Situated meaning from task features & evaluation Can organize around traits, task features, or both, depending on task sets and performance features. Profile differences unaddressed Claim about student … D p1 OI 1 A1A1 D s1 D p1 OI 2 A2A2 D s2 D p2 D p1 OI n AnAn D sn D pn  X

40. AERA 2010 Robert L. Linn Lecture Slide 40 May 1, 2010 Item Response Theory  = propensity to act in targeted way, b j =typical evocation, IRT function = typical variation Situated meaning from task features & evaluation Task features still implicit Profile differences / misfit highlights where the narrative doesn’t fit – for sociocognitive reasons Claim about student … D p1 OI 1 A1A1 D s1 D p1 OI 2 A2A2 D s2 D p2 D p1 OI n AnAn D sn D pn  X1X1 X2X2 XnXn … Complex systems concepts: Attractors & stability  regularities in response patterns, quantified in parameters; Typical variation  prob model Complex systems concepts: Attractors & stability  regularities in response patterns, quantified in parameters; Typical variation  prob model Will work best when most nontargeted L/C/S patterns are familiar… Item-parameter invariance vs Population dependence (Tatsuoka, Linn, Tatsuoka, & Yamamoto, 1988) Will work best when most nontargeted L/C/S patterns are familiar… Item-parameter invariance vs Population dependence (Tatsuoka, Linn, Tatsuoka, & Yamamoto, 1988)

41. AERA 2010 Robert L. Linn Lecture Slide 41 May 1, 2010 Multivariate Item Response Theory (MIRT)  s = propensities to act in targeted ways in situations with different mixes of L/C/S demands. Good for controlled mixes of situations

42. AERA 2010 Robert L. Linn Lecture Slide 42 May 1, 2010 Structured Item Response Theory Explicitly model task situations in terms of L/C/S demands. Links TD with sociocognitive view. Work explicitly with features in controlled and evolved situations (design / agents) Can use with MIRT; Cognitive diagnosis models Claim about student … D p1 OI 1 A1A1 D s1 D p1 OI 2 A2A2 D s2 D p2 D p1 OI n AnAn D sn D pn  X1X1 X2X2 XnXn … q1q1 v i1 q2q2 v i2 qnqn v in

43. AERA 2010 Robert L. Linn Lecture Slide 43 May 1, 2010 Mixtures of IRT Models Different IRT models for different unobserved groups of people Modeling different attractor states Can be theory driven or discovered in data Claim about student … D p1 OI 1 A1A1 D s1 D p1 OI 2 A2A2 D s2 D p2 D p1 OI n AnAn D sn D pn  X1X1 X2X2 XnXn … Claim about student … D p1 OI 1 A1A1 D s1 D p1 OI 2 A2A2 D s2 D p2 D p1 OI n AnAn D sn D pn  X1X1 X2X2 XnXn … OR

44. AERA 2010 Robert L. Linn Lecture Slide 44 May 1, 2010 Measurement Concepts Validity »Soundness of model for local inferences »Breadth of scope is an empirical question »Construct representation in L/C/S terms »Construct irrelevant sources of variation in L/C/S terms Reliability »Through model, strength of evidence for inferences about tendencies, given variabilities … or about characterizations of variability.

45. AERA 2010 Robert L. Linn Lecture Slide 45 May 1, 2010 Measurement Concepts Method Effects »What accumulates in terms of L/C/S patterns in assessment situations but not use situations Generalizability Theory (Cronbach) »Watershed in emphasizing evidentiary reasoning rather than simply measurement »Focus on external features of context; can be recast in L/C/S terms, & attend to correlates of variability

46. AERA 2010 Robert L. Linn Lecture Slide 46 May 1, 2010 Why are these issues important? Connect assessment/measurement with current psychological research »Connect assessment with learning Appropriate constraints on interpreting large scale assessments Inference in complex assessments »Games, simulations, performances »Assessment modifications & accommodations »Individualized yet comparable assessments

47. AERA 2010 Robert L. Linn Lecture Slide 47 May 1, 2010 Measurement frame Sociocognitive frame Conclusion Communication at the interface We have work we need to do, together.