Optimizing Large-Scale Educational Assessment with a “Divide-and-Conquer” Strategy: Fast and Efficient Distributed Bayesian Inference in IRT Models

Sainan Xu (), Jing Lu (), Jiwei Zhang (), Chun Wang () and Gongjun Xu ()
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Sainan Xu: Northeast Normal University
Jing Lu: Northeast Normal University
Jiwei Zhang: Northeast Normal University
Chun Wang: University of Washington
Gongjun Xu: University of Michigan

Psychometrika, 2024, vol. 89, issue 4, No 2, 1119-1147

Abstract: Abstract With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational speed. This study introduces a novel “divide- and-conquer” parallel algorithm built on the Wasserstein posterior approximation concept, aiming to enhance computational speed while maintaining accurate parameter estimation. This algorithm enables drawing parameters from segmented data subsets in parallel, followed by an amalgamation of these parameters via Wasserstein posterior approximation. Theoretical support for the algorithm is established through asymptotic optimality under certain regularity assumptions. Practical validation is demonstrated using real-world data from the Programme for International Student Assessment. Ultimately, this research proposes a transformative approach to managing educational big data, offering a scalable, efficient, and precise alternative that promises to redefine traditional practices in educational assessments.

Keywords: large-scale testing; item response theory; divide-and-conquer strategy; distributed Bayesian inference; Wasserstein posterior (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11336-024-09978-1

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