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Advancing Diagnostic Psychometric Models to Enhance Student Learning: Statistical Theory, Methods, and Applications
Thu, April 13, 8:00 to 9:30am CDT (8:00 to 9:30am CDT), Chicago Marriott Downtown Magnificent Mile, Floor: 4th Floor, Armitage - Avenue BallroomSession Type: Symposium
Abstract
The COVID-19 pandemic forced millions of students to learn in online environments, which raised awareness of the need for robust formative assessments to diagnose student skill profiles and accelerate student learning. The purpose of this symposium session is to highlight psychometric advances in the classification of student skill mastery and the assessment of student learning. The session highlights novel methods that are designed to formulate precise diagnostic decisions for educational interventions within the item response theory, cognitive diagnosis, and hidden Markov modeling paradigms. The session contributes to existing work by disseminating new statistical methodology and theory to support assessment systems that leverage the wealth of student assessment data to provide educators and families with timely feedback to enhance learning.
Sub Unit
Chair
Papers
Item Response Theory Models for Learning With Item-Specific Learning Parameters - Albert Yu, University of Illinois at Urbana-Champaign; Jeff Douglas, University of Illinois at Urbana-Champaign
A Cognitive Diagnosis Learning Model With Covariates: A Three-Step Approach - Jimmy de la Torre, University of Hong Kong; Qianru Liang, University of Hong Kong; Nancy W.Y. Law, University of Hong Kong
Identifiability and Estimation of Hidden Markov Models for Learning Trajectories in Cognitive Diagnosis - Steven Andrew Culpepper, University of Illinois at Urbana-Champaign; Ying Liu, University of Illinois at Urbana-Champaign; Yuguo Chen, University of Illinois at Urbana-Champaign
A Higher-Order Cognitive Diagnostic Model for Large-Scale, Spiral Assessment Designs - Steven Andrew Culpepper, University of Illinois at Urbana-Champaign; James J. Balamuta
Bayesian Inference for Unknown Number of Attributes in Cognitive Diagnosis Models - Yinghan Chen, University of Nevada - Reno; Steven Andrew Culpepper, University of Illinois at Urbana-Champaign