ISSN 0439-755X
CN 11-1911/B

Acta Psychologica Sinica ›› 2022, Vol. 54 ›› Issue (11): 1416-1432.doi: 10.3724/SP.J.1041.2022.01416

• Reports of Empirical Studies • Previous Articles    

Joint-cross-loading multimodal cognitive diagnostic modeling incorporating visual fixation counts

ZHAN Peida1,2()   

  1. 1Department of Psychology, College of Teacher Education, Zhejiang Normal University
    2Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, China
  • Received:2021-06-10 Published:2022-11-25 Online:2022-09-08
  • Contact: ZHAN Peida
  • Supported by:
    the youth fund project of the National Natural Science Foundation of China(31900795);the "special project of Zhijiang youth theory and research"(22ZJQN38YB) of Zhejiang philosophy);the "special project of Zhijiang youth theory and research"(social science planning)


Multimodal data enables accurate cognitive structure diagnosis as well as comprehensive feedback on various cognitive characteristics (e.g., cognitive style). This work presents three multimodal cognitive diagnosis models based on joint-cross-loading modeling to achieve joint analysis of response accuracy, response time (RT), and visual fixation counts (FC). The results of the empirical and simulation studies show that: (1) joint analysis is more suitable for multimodal data than separated analysis; (2) the proposed models can directly use the information in RT and FC to improve the estimation accuracy of latent ability and latent attributes; (3) the proposed models' parameters can be well recovered; and (4) negative results caused by ignoring cross-loading are more serious than those caused by redundant consideration.

Key words: cognitive diagnosis, multimodal data, item response times, fixation counts, cognitive style, eye-tracking