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Accurate Assessment via Process Data

Author

Listed:
  • Susu Zhang

    (University of Illinois at Urbana-Champaign)

  • Zhi Wang

    (Citadel Securities)

  • Jitong Qi

    (Columbia University)

  • Jingchen Liu

    (Columbia University)

  • Zhiliang Ying

    (Columbia University)

Abstract

Accurate assessment of a student’s ability is the key task of a test. Assessments based on final responses are the standard. As the infrastructure advances, substantially more information is observed. One of such instances is the process data that is collected by computer-based interactive items and contain a student’s detailed interactive processes. In this paper, we show both theoretically and with simulated and empirical data that appropriately including such information in the assessment will substantially improve relevant assessment precision.

Suggested Citation

  • Susu Zhang & Zhi Wang & Jitong Qi & Jingchen Liu & Zhiliang Ying, 2023. "Accurate Assessment via Process Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 76-97, March.
    • Handle: RePEc:spr:psycho:v:88:y:2023:i:1:d:10.1007_s11336-022-09880-8
    DOI: 10.1007/s11336-022-09880-8
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    References listed on IDEAS

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    4. Xueying Tang & Zhi Wang & Qiwei He & Jingchen Liu & Zhiliang Ying, 2020. "Latent Feature Extraction for Process Data via Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 378-397, June.
    5. Xueying Tang & Susu Zhang & Zhi Wang & Jingchen Liu & Zhiliang Ying, 2021. "ProcData: An R Package for Process Data Analysis," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 1058-1083, December.
    6. Michelle M. LaMar, 2018. "Markov Decision Process Measurement Model," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 67-88, March.
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