Computerized Oral Reading Evaluation - CORE - is a project funded by the Institute of Education Sciences (IES) to develop and validate a new computerized assessment of oral reading fluency that is administered and scored online.
CORE uses an automated speech recognition engine and an an advanced psychometric model to overcome some of the inadequacies of traditional oral reading fluency assessments. CORE’s online delivery and automated speech recognition allows for whole classrooms to be tested at one time, which reduces administration time and cost, and minimizes administration errors by standardizing the delivery, setting, and scoring. CORE’s shorter passages and advanced psychometric model increases score reliability and provides teachers with more accurate oral reading fluency scores. CORE also equates and links passages, placing scores that are on the same scale across across Grades 2 through 4 (i.e., vertically linked) so that student oral reading fluency scores and growth can be evaluated across grades. CORE is one part of a larger effort to help improve systems for data-based decisions.
The CORE project consists of four phases.
Joseph F. T. Nese is a Research Associate Professor at the University of Oregon with Behavioral Research and Teaching. He received his Ph.D. in school psychology from the University of Maryland in 2009, and his B.A from the University of California at Santa Barbara in 2002. His research involves educational assessment and applied measurement, focusing on developing and improving systems that support data-based decision making, and using advanced statistical methods to measure and monitor student growth.
Akihito Kamata is a Professor at Southern Methodist University (Department of Education Policy & Leadership, and Department of Psychology), and the Executive Director at the Center on Research and Evaluation. His primary research interest is psychometrics and educational and psychological measurement, focusing on implementation of item-level test data analysis methodology through various modeling framework, including item response theory, multilevel modeling, and structural equation modeling.
The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A140203 to the University of Oregon. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.