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New Measurement Paradigms: Psychometric Methods for Technology-Based Assessments
Sun, April 15, 12:25 to 1:55pm, Pan Pacific, Floor: Lobby Level, Oceanview 1&2Session Submission Type: Structured Poster Session
Abstract
The use of technology in the educational measurement in cyberlearning environments presents new opportunities for better understanding of what students know and can do. This session will present a diverse set of educational researchers under the auspices of the CADRE network, with the goal of focusing on how technologies and emerging psychometric methods can be applied to enhance what we know about students’ knowledge and abilities and explore what methods of assessment development, scoring and reporting are needed to make those assessments as effective as possible. It will examine what reliability and validity mean in the new contexts created by advanced use of technology, and what methods might be applied in determining those qualities.
Sub Unit
Chair
Papers
From Rasch Models to Rule Space and Poset-Based Adaptive Testing - Douglas H. Clements, University of Denver; Curtis Tatsuoka, Case Western Reserve University; Kikum Tatsuoka, Teachers College, Columbia University
Using Model-tracing to Conduct Performance Assessment of Students’ Science Inquiry Skills Within a Microworld - Janice D. Gobert, Worcester Polytechnic Institute; Kenneth R. Koedinger, Carnegie Mellon University
Applying Educational Data Mining in E-Learning Environments - Diane Jass Ketelhut, University of Maryland - College Park; Michael Timms, Australian Council for Educational Research
Knowledge Discovery From Selene Data - Ben Hitt, Wheeling Jesuit University; Debbie Denise Denise Reese, Wheeling Jesuit University - Center for Educational Technologies
Knowledge Specification: A Priori Requirement for Effective Embedded Assessment - Debbie Denise Denise Reese, Wheeling Jesuit University - Center for Educational Technologies
Toward a Hidden Markov Modeling Framework for Real-time Assessment in Tutorial Dialogue - James Lester, North Carolina State University; Kristy Boyer, North Carolina State University; Eric N. Wiebe, North Carolina State University
Using Bayes Nets to Measure Learning in E-Learning Environments - Michael Timms, Australian Council for Educational Research