Adaptive Evaluation System Based on IRT Theory

2014 ◽  
Vol 484-485 ◽  
pp. 547-551
Author(s):  
Jiang Min ◽  
Dong Wei

This paper, on the basis of the author realizing the skill evaluation system based on real environment, discusses several commonly used parameter estimation methods based on item response theory ( IRT ) and analyzes the advantages and disadvantages of each estimation method and their respective application fields. Also, it expounds the research theory and design process of skill adaptive evaluation system based on real environment and the innovation of the system.

2021 ◽  
Author(s):  
Kazuhiro Yamaguchi

This research reviewed the recent development of parameter estimation methods in item response theory models. Various new methods to manage the computational burden problem with respect to the item factor analysis and multidimensional item response models, which have high dimensional factors, were introduced. Monte Carlo integral methods, approximation methods for marginal likelihood, new optimization methods, and techniques used in the machine learning field were employed for the estimation methods. Theoretically, a new type of asymptotical setting, that assumes infinite number of sample sizes and items, was considered. Several methods were classified apart from the maximum likelihood method or Bayesian method. Theoretical development of interval estimation methods for individual latent traits were also proposed and they provided highly accurate intervals


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