Current Projects
I'm involved in the Automated Analysis of Constructed Responses (AACR) research group. We explore the use of machine learning and computerized text analysis in science education. We use apply these techniques to evaluate students’ short written answers as part of assessments in secondary education and undergraduate STEM courses. We have created computer models that can predict expert ratings of student responses with accuracy similar to inter-rater reliability among expert raters. These techniques have potential for changing assessment practices across STEM disciplines, regardless of class size.
We use these technologies as part of research to gain insight into how students engage in scientific practices and think about “big ideas” in STEM, such as evolution, energy, and genetics.
Publications
Uhl, J. D., Sripathi, K. N., Saldanha, J. N., Moscarella, R. A., Merrill, J., Urban-Lurain, M., & Haudek, K. C. (2020). Introductory biology undergraduate students’ mixed ideas about genetic information flow. Biochemistry and Molecular Biology Education, n/a(n/a). https://doi.org/10.1002/bmb.21483
Zhai, X., Haudek, K. C., Shi, L., H. Nehm, R., & Urban-Lurain, M. (2020). From substitution to redefinition: A framework of machine learning-based science assessment. In Journal of Research in Science Teaching (Vol. 57, Issue 9, pp. 1430–1459). https://doi.org/10.1002/tea.21658
Jescovitch, L. N., Scott, E. E., Cerchiara, J. A., Merrill, J., Urban-Lurain, M., Doherty, J. H., & Haudek, K. C. (2020). Comparison of Machine Learning Performance Using Analytic and Holistic Coding Approaches Across Constructed Response Assessments Aligned to a Science Learning Progression. In Journal of Science Education and Technology. https://doi.org/10.1007/s10956-020-09858-0
Yoho, R., Kohn, K., Haudek, K. C., Merrill, J., & Urban-Lurain, M. (2020). Exploring the meaning of function as a complex idea embedded within the crosscutting concept of “structure and function.” In Journal on Excellence in College Teaching (Vol. 31, Issue 1).