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Session Type: Symposium
The symposium presents research related to diagnostic assessment, including research on both psychometric models and practical classroom implications. The research focusing on the former includes new applications of the Attribute Hierarchy Method and new psychometric models from the family of Diagnostic Classification Models. The research on the latter involves teachers’ interpretations of diagnostic reports, effects of diagnostic assessment on student learning, and the use of diagnostic assessment to measure teacher knowledge. The symposium focuses on diagnostic assessments that use distracter-driven or ordered multiple-choice items, in which incorrect response options are linked to misconceptions or cognitive levels. The session addresses the question: How can we best model and use diagnostic assessments to improve learning and instruction?
Modeling Ordered Multiple-Choice Items With the Attribute Hierarchy Method to Facilitate Learning Progression Classifications - Derek C. Briggs, University of Colorado, Boulder; Ruhan Circi Kizil, University of Colorado - Boulder; Katie Larsen McClarty, Pearson; Changjiang Wang, Pearson
Teachers’ Interpretations of Score Reports Based Upon Ordered Multiple-Choice Items Linked to a Learning Progression - Alicia C. Alonzo, Michigan State University; Xeng Elizabeth de los Santos, California State University - Long Beach; Jennifer L. Kobrin, Pearson
The Diagnostic Geometry Assessment System: Results From a Randomized Controlled Trial - Jessica Masters, Measured Progress
Psychometric Model for Diagnostic Classification for Multiple-Choice Option-Based Scoring: Application to a Diagnostic Classroom Assessment Instrument - Louis V. DiBello, University of Illinois at Chicago; Robert Henson, University of North Carolina - Greensboro; William F. Stout, University of Illinois; Louis A. Roussos, Measured Progress
Diagnostic Measurement Models for Item Response Dependencies Caused by Misconception Effects - Laine Bradshaw, University of Georgia - Athens
Employing Distracter-Driven Assessments in Measuring Teacher Knowledge and Effectiveness - Philip M. Sadler, Harvard University