A Hidden Markov Model for Learning Trajectories in Cognitive Diagnosis With Application to Spatial Rotation Skills. Issue 1 (January 2018)

• Record Type: Journal Article
• Authors: Chen, Yinghan; Culpepper, Steven Andrew; Wang, Shiyu; Douglas, Jeffrey
• Other Names: Chen Yunxiao guest-editor.
• Is Part Of: Applied psychological measurement. Volume 42:Issue 1(2018)
• Page Start: 5
• Page End: 23
• Publication Date: 2018-01
• Subjects: cognitive diagnosis -- Latent class models -- learning; Psychometrics -- Periodicals; Psychological tests -- Periodicals; Psychology, Applied -- Periodicals; 150.1519505
• DOI: 10.1177/0146621617721250
• Languages: English
• ISSNs: 0146-6216

Abstract

The increasing presence of electronic and online learning resources presents challenges and opportunities for psychometric techniques that can assist in the measurement of abilities and even hasten their mastery. Cognitive diagnosis models (CDMs) are ideal for tracking many fine-grained skills that comprise a domain, and can assist in carefully navigating through the training and assessment of these skills in e-learning applications. A class of CDMs for modeling changes in attributes is proposed, which is referred to as learning trajectories. The authors focus on the development of Bayesian procedures for estimating parameters of a first-order hidden Markov model. An application of the developed model to a spatial rotation experimental intervention is presented.