A Bayesian nonparametric approach for handling item and examinee heterogeneity in assessment data

We propose a novel nonparametric Bayesian item response theory model that estimates clusters at the question level, while simultaneously allowing for heterogeneity at the examinee level under each question cluster, characterized by a mixture of binomial distributions. The main contribution of this work is threefold. First, we present our new model and demonstrate that it is identifiable under a set of conditions. Second, we show that our model can correctly identify question-level clusters asymptotically, and the parameters of interest that measure the proficiency of examinees in solving certain questions can be estimated at a n$$ \sqrt{n} $$ rate (up to a log term). Third, we present a tractable sampling algorithm to obtain valid posterior samples from our proposed model. Compared to the existing methods, our model manages to reveal the multi-dimensionality of the examinees' proficiency level in handling different types of questions parsimoniously by imposing a nested clustering structure. The proposed model is evaluated via a series of simulations as well as apply it to an English proficiency assessment data set. This data analysis example nicely illustrates how our model can be used by test makers to distinguish different types of students and aid in the design of future tests.

Automated summary

We propose a novel nonparametric Bayesian item response theory model that estimates clusters at the question level while accounting for heterogeneity at the examinee level. Our model correctly identifies question-level clusters and accurately estimates examinee proficiency parameters. We present a tractable sampling algorithm that produces valid posterior samples. The model's effectiveness is demonstrated through simulations and analysis of real-world data.