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Diagnostic Instrument for Morphology of Elementary Students

About the Project

The development of morphological skills is essential to students’ literacy growth and, hence, to a host of positive student education and academic outcomes. Teachers need a tool that can help them assess students’ strengths and weaknesses in these areas so that they can identify underlying challenges to reading success and design instruction accordingly.

Project DIMES is a study to develop a computer adaptive, diagnostic assessment of teachable morphological skills for students in Grades 3 to 5. Our assessment will help teachers identify certain skills within morphological awareness that can support literacy skills. Also, while Project DIMES is focused on providing teachers with this much needed assessment, the assessment is also intended to fill a large gap in reading research, in which evaluating the effectiveness of reading interventions requires diagnostic assessments such as this one.

Project Goals

The goals of this project are listed below:
1) To develop a computerized, adaptive, diagnostic instrument of morphological skills for students in grades 3-5, their teachers, and researchers.
2) To conduct, present, and publish research on morphology in upper elementary grades.
3) To conduct, present, and publish research on measurement methodology.

Funding

The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A190079 to the University of Florida. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

Dissemination

  • Kwon, T. Y. (g), Huggins-Manley, A. C., Templin, J., & Zheng, M. (2021, June). Modeling hierarchical attribute structures in diagnostic classification models with multiple attempts. Paper presented at the annual meeting of the National Council on Measurement in Education, Baltimore, MD.
  • Zheng, M., Templin, J., Huggins-Manley, A. C., & Kwon, T. Y. (2020, July). Computerized adaptive testing item selection for Bayesian diagnostic classification models. Poster presented at the international annual meeting of the Psychometric Society in College Park, MD. (virtual meeting due to COVID-19)
  • Kwon, T. Y. (g), Huggins-Manley, A. C., Goodwin, A. P., & Benedict, A. E. (2021, April). Dimensionality of morphology skills in elementary students. In A. P. Goodwin’s (Chair) Coordinated Session Mapping Morphology and its Multidimensionality, Assessment, Relations to Outcomes, and Relation to Covariates. Paper presented at the annual meeting of the American Educational Research Association (virtual due to COVID-19).
  • Kwon, T. Y. (g), Huggins-Manley, A. C., Goodwin, A. P., Templin, J., Benedict, A. E., & Zheng, M. (g) (2020, March). Works in progress: Project DIMES- Diagnostic Instrument for Morphology of Elementary Students. Poster accepted for presentation the annual meeting of the Society for Research on Educational Effectiveness, Arlington, VA. (virtual meeting due to COVID-19)
  • Kwon, T. Y. (g), Huggins-Manley, A. C., Templin, J., & Zheng, M. Y. (2020, November). The development and evaluation of a sequential hierarchical diagnostic classification model. Paper presented at the virtual forum of the Florida Educational Research Association.
  • Kwon, T. Y. (g), Huggins-Manley, A. C., Benedict, A. E., Templin, J., & Goodwin, A. P. (2019, November). Involving teachers in evidence-centered design development of a classroom assessment. Paper presented at the annual meeting of the Florida Educational Research Association in St. Petersburg, FL.
  • Da Silva, M., Huggins-Manley, A.C., & Benedict, A. E. (2024). A method of empirical Q-matrix validation for multidimensional item response theory. Applied Measurement in Education. https://doi.org/10.1080/08957347.2024.2345597
  • Kwon, T., Huggins-Manley, A. C., Templin, J., & Zheng, M. (g) (2024). Modeling hierarchical attribute structures in diagnostic classification models with multiple attempts. Journal of Educational Measurement. https://doi.org/10.1111/jedm.12387
  • Huggins-Manley, A. C., Booth, B. M., & D’Mello, S. (2022). Toward argument-based fairness with an application to AI-enhanced educational assessments. Journal of Educational Measurement. http://doi.org/10.1111/jedm.12334
  • Huggins-Manley, A. C. (2021, November). Argument-based fairness in educational assessment. Presentation at the annual meeting of the Florida Educational Research Association in St. Petersburg, FL.
  • Jung, A. K., Templin, J., Huggins-Manley, A. C., & Kwon, T. (2024, April). Integrating Bayesian Explanatory IRT Models and CAT Algorithms for Small Sample Settings. Paper presented at the National Council on Measurement in Education in Philadelphia, PA.