Combining Process Information and Item Response Modeling to Estimate Problem-Solving Ability

Yue Xiao, Bernard Veldkamp*, Hongyun Liu

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

The action sequences of respondents in problem-solving tasks reflect rich and detailed information about their performance, including differences in problem-solving ability, even if item scores are equal. It is therefore not sufficient to infer individual problem-solving skills based solely on item scores. This study is a preliminary attempt to incorporate process data analysis into the measurement of problem-solving ability. The entire procedure consists of using information from process data as prior information for the estimation of problem-solving proficiency in an item response model. The purpose of this study is twofold: (1) to investigate the impact of adding process information on the estimation of latent ability; (2) to examine the extent to which the ability estimates obtained from the combination model can reflect the information of the problem-solving process. Seven problem-solving items from the Programme for International Assessment of Adult Competencies were used. Results indicate that the inclusion of process priors enhances the correlation between proficiency estimates and process information related to the problem-solving strategies adopted by respondents, as well as to their solution efficiency. The inclusion of process priors further reveals differences in the problem-solving performance of respondents exhibiting the same score pattern and increases precision of latent ability estimation.

Original languageEnglish
JournalEducational Measurement: Issues and Practice
DOIs
Publication statusE-pub ahead of print/First online - 1 Dec 2021

Keywords

  • ability estimation
  • Bayesian framework
  • item response theory
  • problem-solving
  • process data analysis
  • UT-Hybrid-D
  • 22/1 OA procedure

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