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Assessing Ability Recovery of the Sequential IRT Model With Unstructured Multiple-Attempt Data.

Ziying Li1, A Corinne Huggins-Manley1, Walter L Leite1

  • 1University of Florida, Gainesville, USA.

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Summary
This summary is machine-generated.

This study explores ability estimation in virtual learning environments using a multidimensional sequential 2-PL IRT model for multiple-attempt data. While promising, certain data conditions can lead to biased ability estimates.

Keywords:
multiple attemptssequential IRT modelsunstructured datavirtual learning environment

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Learning Analytics

Background:

  • Unstructured multiple-attempt (MA) item response data from virtual learning environments (VLEs) present challenges for educational measurement due to missing data and unknown ability growth.
  • Accurate ability measurement from VLE data is crucial for improving VLE systems, monitoring student progress, and supporting educational research.

Purpose of the Study:

  • To evaluate the ability recovery of the multidimensional sequential 2-PL item response theory (IRT) model when applied to unstructured MA data from VLEs.
  • To investigate the impact of ability growth magnitude and the proportion of students with two attempts on ability estimation accuracy.
  • To examine the moderating effects of sample size, test length, and data missingness on the bias and accuracy of ability estimates.

Main Methods:

  • A simulation study was designed to assess the performance of the multidimensional sequential 2-PL IRT model.
  • The simulation manipulated key parameters including ability growth, student attempt proportions, sample size, test length, and missing data rates.
  • Bias and root mean square error (RMSE) of ability estimates were calculated to evaluate model performance under various conditions.

Main Results:

  • The multidimensional sequential 2-PL IRT model demonstrates potential for ability estimation in unstructured VLE data.
  • Significant bias in ability estimates was observed under specific data conditions, highlighting potential limitations.
  • The magnitude of ability growth and the proportion of students with multiple attempts were found to influence estimation accuracy.

Conclusions:

  • The multidimensional sequential 2-PL IRT model shows promise for analyzing complex VLE data, but careful consideration of data characteristics is necessary.
  • Certain data conditions, such as high missingness or specific growth patterns, can compromise the accuracy of ability estimates.
  • Further research is needed to refine models and methods for robust ability estimation from diverse VLE data.