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IES Grant

Title: Developing Computational Tools for Model-Based Oral Reading Fluency Assessments
Center: NCER Year: 2020
Principal Investigator: Kamata, Akihito Awardee: Southern Methodist University
Program: Statistical and Research Methodology in Education      [Program Details]
Award Period: 3 years (08/01/2020 – 07/31/2023) Award Amount: $899,901
Type: Methodological Innovation Award Number: R305D200038

Co-Principal Investigators: Kara, Yusuf; Nese, Joseph; Potgieter, Cornelis

Purpose: The purpose of this study is to expand the existing estimation model available in a computer-based oral reading fluency (ORF) assessment which was developed as part of a previous IES grant (R305A140203). The extensions of the previous model will include the development of a sentence-level model that takes into account between-sentence dependency and incomplete reading.

Project Activities: The model parameter estimation algorithms for these extensions will be developed by the method of moments, the Monte Carlo EM algorithm approach, and the Bayesian HMC approach used in the software package Stan.

The primary product of this grant will be a Shiny app for rendering user-friendly the R code needed to estimate model parameters. The Shiny app will expand the capabilities of the previous software by providing better ORF score comparability both within and between students for better longitudinal and cross-sectional studies, as well as better estimates of measurement errors for ORF scores. The research team will also demonstrate the use of the new software on data collected for 150 passages as part of the work done for R305A140203 and develop web-based tutorials for supporting applied researchers who want to use the Shiny app.

Related IES Projects: Measuring Oral Reading Fluency: Computerized Oral Reading Evaluation (CORE) (R305A140203)

Products and Publications

Bui, M. T., Potgieter, C. J., & Kamata, A. (2022). Penalized likelihood methods for modeling count data. Journal of Applied Statistics, 1–20.

Kara, Y., Kamata, A., Ozkeskin, E. E., Qiao, X., & Nese, J. F. (2023). Predicting oral reading fluency scores by between-word silence times using natural language processing and random forest algorithm. Psychological Test and Assessment Modeling, 65(2), 36–54.

Kara, Y., Kamata, A., Qiao, X., Potgieter, C. J., & Nese, J. F. (2023). Equating Oral Reading Fluency Scores: A Model-Based Approach. Educational and Psychological Measurement, 00131644221148122.