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Application of Generalizability Theory to Concept-Map Assessment Research

Application of Generalizability Theory to Concept-Map Assessment Research. Yue Yin & Richard J. Shavelson Stanford Educational Assessment Laboratory (SEAL) Stanford University & CRESST AERA 2004, San Diego CA. Overview.

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Application of Generalizability Theory to Concept-Map Assessment Research

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  1. Application of Generalizability Theory to Concept-Map Assessment Research Yue Yin & Richard J. Shavelson Stanford Educational Assessment Laboratory (SEAL) Stanford University & CRESST AERA 2004, San Diego CA

  2. Overview • Part 1: Feasibility of applying G-theory to concept-map assessment (CMA) research - Examining the dependability of CMA scores - Designing a CMA for a particular application - Narrowing down alternatives • Part 2: Empirical study of using G-theory to compare two CMAs: - Construct-a-map with created linking phrases (C) - Construct-a-map with selected linking phrases (S)

  3. Concepts/Terms Linking lines Linking Phrases Proposition A Concept-map

  4. Variations in CMA

  5. Part 1 Feasibility of Applying G Theory to CMA Research

  6. Viewing CMA with G theory • Basic idea A particular type of score, given by a particular rater, based on a particular type of concept map, on a particular occasion, … is a sample from a multifaceted universe. • Object of measurement People—the variation in students’ knowledge structure • Facets Task (concept & proposition), response format, scoring system, rater, occasion, …

  7. Concept-term sampling Proposition sampling Rater sampling Occasion sampling Equivalence of alternate forms Internal consistency Inter-rater reliability Stability over time G theory vs. CTT Similarity G Theory’s Advantage • Integrate conceptually and simultaneously evaluate all the technical properties above • Estimate not only the effect of individual facets, but also interaction effects • Permits us to optimize an assessment’s technical quality

  8. Examining Technical Properties & Designing Assessments • Examining dependability (G study) How well can a measure of student’s declarative knowledge structure be generalized across concept map tasks? scoring systems? occasions? raters? propositions? different concept samples? • Designing an assessment (D study) How many concept map tasks, scoring systems, occasions, raters, propositions, and/or different concept samples will be needed to obtain a reliable measurement of students’ declarative knowledge structure?

  9. Narrowing Down Alternatives • Task - Which task type is more reliable over raters, occasions, propositions, concept samples? - Accordingly, this task needs fewer raters, occasions, propositions, and concept samples. • Scoring system - Which scoring system is more reliable over raters, occasions, propositions, concept samples? - Accordingly, this scoring system needs fewer raters, occasions, propositions, and concept samples.

  10. Part 2 Empirical Study of Using G-theory to Compare Two CMAs

  11. Two Frequently UsedCMAs • Construct-a-map with created linking phrases (C)--Provides a cognitively valid measure of knowledge structure (e.g., Ruiz-Primo et al., 2001 & Yin et al., 2004) • Construct-a-map with selected linking phrases (S)--Provides an efficient way to measure knowledge structure (e.g., Klein et al., 2001)

  12. Method • Concept-map task - 9 Concepts (for C & S) water, volume, cubic centimeter, wood, density, mass, buoyancy, gram, and matter - 6 Linking phrases (for S only) is a measure of… has a property of… depends on… is a form of… is mass divided by… divided by volume equals… • Participants - 92 eighth-graders - 46 girls - previously studied a related unit - no related instruction between two occasions • Procedures C  S (n = 22) S  C (n = 23) C  C (n = 26) S  S (n = 21)

  13. Water Wood has has is a form of is a form of has has has has Matter has has a property of has a property of has a property of Mass divided by volume equals Density is mass divided by Volume is unit of is a unit of depends on Gram CC Buoyancy Criterion Map

  14. Mandatory Propositions

  15. CS & SC Person (P) Proposition/Item (I) Format (F) P x F P x I F x I P x F x I, e CC & SS Person (P) Proposition/Item (I) Occasion (O) P x O P x I O x I P x O x I, e Source of Variation

  16. Variance Component Estimate

  17. G Study in SC & CS

  18. G Study in CC & SS

  19. D Study for C CMA

  20. D Study for S CMA

  21. Conclusions • G study pinpoints multiple sources of measurement error, thereby giving insight into how to improve the reliability and applicability of CMA via a D study • C and S mapping tasks are not equivalent in their technical properties • Fewer occasions and propositions are needed in S than C to get a reliable evaluation of students’ declarative knowledge structure

  22. Thank You for Your Interest!  To get the complete paper, please either contact Yue Yin at yyin@stanford.edu Or download the file directly at http://www.stanford.edu/dept/SUSE/SEAL/

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