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LASSO-Based Pattern Recognition for Replenished Items With Graded Responses in Multidimensional Computerized Adaptive

Jianan Sun1, Ziwen Ye2, Lu Ren3

  • 1College of Science, Beijing Forestry University, Beijing, China.

Frontiers in Psychology
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces LPRM-GR, a new method for identifying item-trait patterns in multidimensional computerized adaptive testing (MCAT) using the multidimensional graded response model (MGRM). The method accurately identifies patterns for replenished items, enhancing MCAT effectiveness.

Keywords:
LASSOitem-trait pattern recognitionmultidimensional computerized adaptive testingmultidimensional graded response modelreplenished itemsweighted BIC

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Area of Science:

  • Psychometrics and Statistical Modeling
  • Educational Measurement and Assessment

Background:

  • Multidimensional item response theory (MIRT) is crucial in psychometrics for analyzing complex data.
  • The Multidimensional Graded Response Model (MGRM) is essential for developing Multidimensional Computerized Adaptive Testing (MCAT) with graded response data and multiple latent traits.

Purpose of the Study:

  • To develop an automated method for identifying item-trait patterns of replenished items within MCAT using the MGRM.
  • To enhance the effectiveness of item replenishment in MCAT by improving parameter estimation for new items.

Main Methods:

  • An exploratory pattern recognition method for graded-response items, LPRM-GR, was developed, utilizing the least absolute shrinkage and selection operator (LASSO).
  • Regular and weighted Bayesian Information Criterion (BIC) were employed to select optimal item-trait patterns.
  • Simulations were conducted to evaluate LPRM-GR's accuracy in pattern recognition and item estimation under various conditions (dimensionality, response categories, trait correlation, stopping rules, item selection).

Main Results:

  • The LPRM-GR method demonstrated good performance in pattern recognition for item replenishment in 2- to 4-dimensional MCAT with MGRM.
  • Weighted BIC generally outperformed regular BIC in pattern recognition accuracy.
  • The method showed high accuracy and efficiency in identifying graded-response item patterns, proving easy to implement and practically feasible.

Conclusions:

  • LPRM-GR provides an accurate and efficient approach for identifying item-trait patterns in MCAT item replenishment.
  • The method facilitates better parameter estimation for replenished items, maintaining MCAT effectiveness.
  • Weighted BIC is recommended for optimal pattern selection in this context.