2025/08/07 by Jiawei Xiong, Huan Kuang, Cheng Tang +6 · 1 voice
Computer Science · Decision Sciences · Social Sciences · #Computational and Text Analysis Methods #Psychometric Methodologies and Testing #Technology and Data Analysis
paper · pdf · doi:10.1111/jedm.70001
openalex publication_date 2025/08/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Abstract Constructed responses (CRs) within testlets are widely used to assess complex skills but can pose calibration challenges due to local item dependence. A few current testlet models incorporate testlet‐specific effects to address local dependence but struggle with interpreting these effects and may not fully capture the complexities of CR items because they rely only on response or score patterns. A Topic Testlet Model (TTM) integrates topic modeling within a psychometric framework was proposed. It uses latent topics from student written responses to adjust for local dependence, enable simultaneous calibration, and provide insights into evaluating student reasoning and writing in testlet CR items. Using empirical data from both English Language and Arts as well as Science assessments for grades 3‐12, we compare the TTM with existing models in terms of ability estimates, item parameter estimates, and overall model fit. Simulation studies further demonstrate parameter recovery under various testing scenarios. Results show that the TTM effectively accounts for local dependence, improves testlet effect interpretability, and demonstrates a better fit than the existing models. TTM advances CR testlet calibration, leveraging additional information from student written responses to improve the precision of the assessment systems and validity of the use of test scores.