Publications by Ross H. Nehm

Pelletreau, K. N., Andrews, T. C., Armstrong, N., Bedell, M. A., Dastoor, F., Dean, N., et al. (2016). A clicker-based case study that untangles student thinking about the processes in the central dogma. CourseSource. http://doi.org/https://doi.org/10.24918/cs.2016.15
Lemons, P. P., McCourt, J., Knight, J. K., Merrill, J. E., Nehm, R. H., Prevost, L. B., et al. (2016). A community of enhanced assessment facilitates reformed teaching. In American Association for the Advancement of Science. Washington, DC. Retrieved from http://www.enfusestem.org/projects/a-community-of-enhanced-assessment-facilitates-reformed-teaching/ (Original work published April 2016)
Pelletreau, K. N., Knight, J. K., Lemons, P. P., McCourt, J., Merrill, J. E., Nehm, R. H., et al. (2018). A Faculty Professional Development Model That Improves Student Learning, Encourages Active-Learning Instructional Practices, and Works for Faculty at Multiple Institutions. Cbe—Life Sciences Education, 17, es5. http://doi.org/10.1187/cbe.17-12-0260
Prevost, L. B., Bierema, A. M. -K., Kaplan, J. J., Knight, J. K., Lemons, P. P., Lira, C. T., et al. (2016). An iterative approach to developing, refining and validating machine-scored constructed response assessments. In American Association for the Advancement of Science. Washington, DC. Retrieved from http://www.enfusestem.org/projects/an-iterative-approach-to-developing-refining-and-validating-machine-scored-constructed-response-assessments/ (Original work published April 2016)
Ha, M., Nehm, R. H., Urban-Lurain, M., & Merrill, J. E. (2011). Applying Computerized-Scoring Models of Written Biological Explanations across Courses and Colleges: Prospects and Limitations. Cbe Life Sciences Education, 10, 379-393. http://doi.org/10.1187/cbe.11-08-0081
Beggrow, E. B., Ha, M., Nehm, R. H., Pearl, D., & Boone, W. J. (2013). Assessing Scientific Practices Using Machine-Learning Methods: How Closely Do They Match Clinical Interview Performance?. Journal Of Science Education And Technology. http://doi.org/DOI 10.1007/s10956-013-9461-9 (Original work published July 2013)
Ha, M., & Nehm, R. H. (2015). Assessment item "Cover Stories", semantic similarity and successful computerized scoring of open-ended text. In NARST. (Original work published April 2015)
Schmiemann, P., Nehm, R. H., & Tornabene, R. (2017). Assessment of Genetics Understanding: Under What Conditions Do Situational Features Have an Impact on Measures?. Science & Education, 26, 1161–1191. http://doi.org/10.1007/s11191-017-9925-z
Urban-Lurain, M., Merrill, J. E., Haudek, K. C., Nehm, R. H., Moscarella, R. A., Steele, M., & Park, M. (2015). Automated analysis of constructed responses: What are we modeling?. In Society for the Advancement of Biology Education Research.
Ha, M., Wei, X., Wang, J., Hou, D., & Nehm, R. H. (2019). Chinese pre-service biology teachers evolutionary knowledge, reasoning patterns, and acceptance levels. International Journal Of Science Education, 41, 628–651. http://doi.org/10.1080/09500693.2019.1572936
Ha, M., & Nehm, R. H. (2011). Comparative Efficacy of Two Computer-Assisted Scoring Tools for Evolution Assessment. In National Association of Research in Science Teaching. Orlando, FL. (Original work published April 3-6, 2011)
Ha, M., & Nehm, R. H. (2014). Darwin's Difficulties and Students' Struggles with Trait Loss: Cognitive-Historical Parallelisms in Evolutionary Explanation. Science & Education, 23, 1051-1074. http://doi.org/10.1007/s11191-013-9626-1
Sbeglia, G. C., & Nehm, R. H. (2019). Do you see what I-SEA? A Rasch analysis of the psychometric properties of the Inventory of Student Evolution Acceptance. Science Education, 103, 287–316. http://doi.org/10.1002/sce.21494
Sbeglia, G. C., & Nehm, R. H. (2017). Does evolution acceptance differ across biological scales? A Rasch analysis of the Inventory of Student Evolution Acceptance (I-SEA). In Society for the Advacement of Biology Education Research.
Ha, M., Ponnuraj, G. T., & Nehm, R. H. (2014). EvoGrader: An Online Formative Assessment Tool for Automatically Analyzing Students Ideas in Written Evolutionary Explanations. In SABER. (Original work published Minneapolis, MN)
Moharreri, K., Ha, M., & Nehm, R. H. (2014). EvoGrader: an online formative assessment tool for automatically evaluating written evolutionary explanations. Evolution: Education And Outreach, 7, 15. http://doi.org/10.1186/s12052-014-0015-2 (Original work published August 2014)
Nehm, R. H., & Mead, L. S. (2019). Evolution assessment: Introduction to the special issue. Evolution: Education And Outreach, 12, 7. http://doi.org/10.1186/s12052-019-0098-x
Rachmatullah, A., Ha, M., Roshayanti, F., & Nehm, R. H. (2017). Evolution Education in Indonesia: Pre-service Biology Teachers Evolutionary Knowledge Levels, Reasoning Models, and Acceptance Patterns. In H. Deniz & Borgerding, L. (Eds.), Evolution Education around the Globe. Springer.
Federer, M. R., Nehm, R. H., & Pearl, D. (2016). Examining Gender Differences in Written Assessment Tasks in Biology: A Case Study of Evolutionary Explanations. Cbe—Life Sciences Education, 15(Spring). Retrieved from https://www.lifescied.org/doi/pdf/10.1187/cbe.14-01-0018
Urban-Lurain, M., Bierema, A. M. -K., Haudek, K. C., Hoskinson, A. -M., Kaplan, J. J., Knight, J. K., et al. (2016). Expanding a National Network for Automated Analysis of Constructed Response Assessments to Reveal Student Thinking in STEM. In American Association for the Advancement of Science. Washington, DC: American Association for the Advancement of Science. Retrieved from http://www.enfusestem.org/projects/collaborative-research-expanding-a-national-network-for-automated-analysis-of-constructed-response-assessments-to-reveal-student-thinking-in-stem-5/ (Original work published April 2016)
Urban-Lurain, M., Cooper, M., Haudek, K. C., Kaplan, J. J., Knight, J. K., Lemons, P. P., et al. (2015). Expanding a national network for automated analysis of constructed response assessments to reveal student thinking in STEM. Computers In Education Journal, 6(1), 65-81.
Urban-Lurain, M., Cooper, M., Haudek, K. C., Kaplan, J. J., Knight, J. K., Lemons, P. P., et al. (2014). Expanding a National Network for Automated Analysis of Constructed Response Assessments to Reveal Student Thinking in STEM. In ASEE. Indianapolis, IN. Retrieved from http://www.asee.org/public/conferences/32/papers/9856/view (Original work published June 2014)
Ha, M., & Nehm, R. H. (2015). Exploring students' evolutionary explanations across natural, sexual, and artificial selection secenarios. In NARST. (Original work published April 2015)
Ha, M., & Nehm, R. H. (2015). Exploring the use of machine translation and machine grading of open-ended assessments in international comparison studies. In European Science Education Research Association Meeting.
Merrill, J. E., Haudek, K. C., Smith, M. K., Lemons, P. P., Prevost, L. B., Nehm, R. H., et al. (2015). Faculty Learning Communities as a Focus for Assessment-Driven Instructional Change. In CREATE for STEM Mini Conference. East Lansing, MI.
Zhai, X., Haudek, K. C., Shi, L., Nehm, R. H., & Urban-Lurain, M. (2020). From substitution to redefinition: A framework of machine-learning based science assessment. Journal Of Research In Science Teaching. http://doi.org/10.1002/tea.21658 (Original work published October 2020)
Haudek, K. C., Kaplan, J. J., Knight, J. K., Long, T., Merrill, J. E., Munn, A., et al. (2011). Harnessing Technology to Improve Formative Assessment of Student Conceptions in STEM: Forging a National Network. Cbe - Life Sciences Education, 10, 149-155. http://doi.org/10.1187/cbe.11-03-0019 (Original work published Summer)
Nehm, R. H., & Haertig, H. (2012). Human vs. Computer Diagnosis of Students Natural Selection. Journal Of Science Education And Technology, 21, 56-73. http://doi.org/10.1007/s10956-011-9282-7
Voreis, J. S., Andrews, T. C., Federer, M. R., Knight, J. K., Merrill, J. E., Merrill, S. J., et al. (2014). Investigating the Impact of Faculty Learning Communities on Biology Instructors. Society for the Advancement of Biology Education Research (SABER). Minneapolis, MN. (Original work published July 2014)
Wang, X., Colton, J., Sbeglia, G. C., Finch, S., & Nehm, R. H. (2017). Longitudinal Learning Dynamics and the Conceptual Restructuring of Evolutionary Understanding. In NARST.
Chen, J., Ha, M., & Nehm, R. H. (2015). Measuring semantic similarity in written text: Applications to learning and assessment. In NARST. (Original work published April 2015)
Ha, M., & Nehm, R. H. (2016). Predicting the Accuracy of Computer Scoring of Text: Probabilistic, Multi-Model, and Semantic Similarity Approaches. In NARST. Baltimore, MD. (Original work published April 2016)
Nehm, R. H., Beggrow, E. B., Opfer, J. E., & Ha, M. (2012). Reasoning About Natural Selection: Diagnosing Contextual Competency Using the ACORNS Instrument. American Biology Teacher, 74, 92-98. Retrieved from http://www.jstor.org/stable/10.1525/abt.2012.74.2.6 (Original work published February 2012)
Sbeglia, G. C., & Nehm, R. H. (2019). Student reasoning about matter and energy transformation across contexts: Psychometric evaluation of cognitive coherence using Rasch analysis. In Society for the Advancement of Biology Education Research. Minneapolis, MN.
Tornabene, R., Nehm, R. H., & Schmiemann, P. (2017). Testing the Impact of Situational Features on Measures of Biology Students Genetics Understanding. In NARST.
Ha, M., & Nehm, R. H. (2016). The Impact of Misspelled Words on Automated Computer Scoring: A Case Study of Scientific Explanations. Journal Of Science Education And Technology, 25, 358–374. Retrieved from https://link.springer.com/article/10.1007/s10956-015-9598-9
Ha, M., Baldwin, B., & Nehm, R. H. (2015). The Long-Term Impacts of Short-Term Professional Development: Science Teachers and Evolution. Evo Edu Outreach, 8(11). http://doi.org/10.1186/s12052-015-0040-9
Zagallo, P., McCourt, J., Idsardi, R., Smith, M. K., Urban-Lurain, M., Andrews, T. C., et al. (2019). Through the Eyes of Faculty: Using Personas as a Tool for Learner-Centered Professional Development. Cbe - Life Sciences Education, 18. http://doi.org/10.1187/cbe.19-06-0114 (Original work published November 2019)
Nehm, R. H., Ha, M., & Mayfield, E. (2012). Transforming Biology Assessment with Machine Learning: Automated Scoring of Written Evolutionary Explanations. Journal Of Science Educational Technology, 21. http://doi.org/10.1007/s10956-011-9300-9
Federer, M. R., Nehm, R. H., Opfer, J. E., & Pearl, D. (2014). Using a Constructed-Response Instrument to Explore the Effects of Item Position and Item Features on the Assessment of Students Written Scientific Explanations. Research In Science Education. http://doi.org/10.1007/s11165-014-9435-9 (Original work published October-29-2014)
McCourt, J., Andrews, T. C., Crumbs, T., Knight, J. K., Merrill, J. E., Merrill, S. J., et al. (2015). Using faculty learning communities to promote the development of student-centered biology instructors. In SABER.
Pelletreau, K. N., Knight, J. K., Lemons, P. P., McCourt, J., Merrill, S. J., Moscarella, R. A., et al. (2015). Using student constructed responses to guide the development of instructional activities by cross- institutional faculty learning communities. In Gordon Research Conference on Undergraduate Biology Education Research.
McCourt, J., Andrews, T. C., Knight, J. K., Merrill, J. E., Nehm, R. H., Pelletreau, K. N., et al. (2018). What Motivates Biology Instructors to Engage and Persist in Teaching Professional Development?. Cbe—Life Sciences Education, 16(3). http://doi.org/10.1187/cbe.16-08-0241