A Longitudinal Diagnostic Model with Hierarchical Learning Trajectories. Issue 3 (15th March 2021)

• Record Type: Journal Article
• Authors: Zhan, Peida; He, Keren
• Is Part Of: Educational measurement. Volume 40:Issue 3(2021)
• Page Start: 18
• Page End: 30
• Publication Date: 2021-03-15
• Subjects: attribute hierarchy -- cognitive diagnosis -- diagnostic classification models -- learning trajectory -- longitudinal assessment -- longitudinal learning diagnosis; Educational tests and measurements -- Periodicals; Educational tests and measurements -- United States -- Periodicals; 371.262
• DOI: 10.1111/emip.12422
• Languages: English
• ISSNs: 0731-1745

Abstract

Abstract: In learning diagnostic assessments, the attribute hierarchy specifies a sequential network of interrelated attribute mastery processes, which makes a test blueprint consistent with the cognitive theory. One of the most important functions of attribute hierarchy is to guide or limit the developmental direction of students and then form a hierarchical learning trajectory. To address the issue that the existing longitudinal learning diagnosis models cannot track the development of hierarchical attributes, this study proposes a new hierarchical longitudinal learning diagnostic modeling approach and two sample models. Compared to the longitudinal learning diagnosis models that do not consider the attribute hierarchy, the proposed models, by taking the sequential mastery tree into account, can accommodate various attribute hierarchies and simultaneously track an individual's learning developmental trajectory. An empirical study was conducted to illustrate the advantages of the proposed model. The results mainly indicated that the proposed model can properly diagnose the development of hierarchical attributes in longitudinal assessments. A simulation study was further conducted to explore the model parameter recovery of the proposed models.