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Enhancing Educational Assessment through Tailored Analysis Procedures: A Case Study

Dive into the impact of guessing on test scores and the effectiveness of tailored analysis procedures in educational assessment. Explore the analysis of data generated from 500 students with specific theta and item distributions. Compare fit statistics pre- and post-controlling for guessing. Conduct thorough analysis with Winsteps software, examining biases in item difficulty and student ability, as well as RMSE values and outfit statistics. Uncover the nuances of student-item interactions for improved assessment practices.

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Enhancing Educational Assessment through Tailored Analysis Procedures: A Case Study

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Presentation Transcript

  1. Playing the y-model XU Kun 24 Nov 2011

  2. Outline • Revisit the performance of tailored analysis procedure (Andrich, Marais & Humphry, 2011) • Impact of guessing on person estimates • Fit Statistics before and after controlling for guessing

  3. Data Generation • 500 students theta ~ N(-0.75, 1.22) • 35 items b ~ U(-3.0, 3.0) • c fixed to 1/7 • y fixed to 15 • 100 replications

  4. Analysis • First analysis by Winsteps • Export the item difficulty and student ability estimates • Calculate the probability of each student on each item • Prepare the data set for Tailored analysis • Tailored analysis by Winsteps

  5. Bias of Item Difficulty

  6. RMSE of Item Difficulty

  7. Bias of Student Ability

  8. RMSE of Student Ability

  9. Outfit Standardized t of Item

  10. Outfit Standardized t of Student

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