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A Multistrategy Cognitive Diagnosis Model Incorporating Item Response Times Based on Strategy Selection Theories

Author

Listed:
  • Junhuan Wei

    (Jiangxi Normal University)

  • Liufen Luo

    (Fuzhou Vocational Technical College)

  • Yan Cai
  • Dongbo Tu

    (Jiangxi Normal University)

Abstract

Response times (RTs) facilitate the quantification of underlying cognitive processes in problem-solving behavior. To provide more comprehensive diagnostic feedback on strategy selection and attribute profiles with multistrategy cognitive diagnosis model (CDM) and utilize additional information for item RTs, this study develops a multistrategy cognitive diagnosis modeling framework combined with RTs. The proposed model integrates individual response accuracy and RT into a unified framework to define strategy selection and make it closer to the individual’s strategy selection process. Simulation studies demonstrated that the proposed model had reasonable parameter recovery and attribute classification accuracy and outperformed the existing multistrategy CDMs and single-strategy CDMs in terms of performance. Empirical results further illustrated the practical application and the advantages of the proposed model.

Suggested Citation

  • Junhuan Wei & Liufen Luo & Yan Cai & Dongbo Tu, 2024. "A Multistrategy Cognitive Diagnosis Model Incorporating Item Response Times Based on Strategy Selection Theories," Journal of Educational and Behavioral Statistics, , vol. 49(4), pages 658-686, August.
    • Handle: RePEc:sae:jedbes:v:49:y:2024:i:4:p:658-686
    DOI: 10.3102/10769986231200469
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    References listed on IDEAS

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    1. Tianyou Wang & Jiawei Zhang, 2006. "Optimal Partitioning of Testing Time: Theoretical Properties and Practical Implications," Psychometrika, Springer;The Psychometric Society, vol. 71(1), pages 105-120, March.
    2. Chia-Yi Chiu & Jeffrey Douglas & Xiaodong Li, 2009. "Cluster Analysis for Cognitive Diagnosis: Theory and Applications," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 633-665, December.
    3. Jimmy Torre & Jeffrey Douglas, 2004. "Higher-order latent trait models for cognitive diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 333-353, September.
    4. Wim van der Linden, 2007. "A Hierarchical Framework for Modeling Speed and Accuracy on Test Items," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 287-308, September.
    5. Hans-Friedrich Köhn & Chia-Yi Chiu, 2017. "A Procedure for Assessing the Completeness of the Q-Matrices of Cognitively Diagnostic Tests," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 112-132, March.
    6. Jonathan Templin & Laine Bradshaw, 2013. "Measuring the Reliability of Diagnostic Classification Model Examinee Estimates," Journal of Classification, Springer;The Classification Society, vol. 30(2), pages 251-275, July.
    7. Maria Bolsinova & Paul Boeck & Jesper Tijmstra, 2017. "Modelling Conditional Dependence Between Response Time and Accuracy," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1126-1148, December.
    8. Jimmy de la Torre, 2011. "The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 179-199, April.
    9. Robert Mislevy & Norman Verhelst, 1990. "Modeling item responses when different subjects employ different solution strategies," Psychometrika, Springer;The Psychometric Society, vol. 55(2), pages 195-215, June.
    10. Jimmy de la Torre & Jeffrey Douglas, 2008. "Model Evaluation and Multiple Strategies in Cognitive Diagnosis: An Analysis of Fraction Subtraction Data," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 595-624, December.
    11. Jimmy Torre, 2011. "Erratum to: The Generalized DINA Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 510-510, July.
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