Publications

Zhai X, Haudek KC, Stuhlsatz M, Wilson CD. Evaluation of construct-irrelevant variance yielded by machine and human scoring of a science teacher PCK constructed response assessment. Studies in Educational Evaluation. 2020;67:100916. doi:https://doi.org/10.1016/j.stueduc.2020.100916.

Yoho R, Foster T, Urban-Lurain M, Merrill JE, Haudek KC. Interdisciplinary insights from instructor interviews reconciling "structure and function" in biology, biochemistry, and chemistry through the context of enzyme binding. Disciplinary and Interdisciplinary Science Education Research. 2019;1. doi:10.1186/s43031-019-0016-7.

Zagallo P, McCourt J, Idsardi R, et al. Through the Eyes of Faculty: Using Personas as a Tool for Learner-Centered Professional Development. CBE - Life Sciences Education. 2019;18. doi:10.1187/cbe.19-06-0114.

Jescovitch LN, Scott EE, Cerchiara JA, et al. Deconstruction of Holistic Rubrics into Analytic Rubrics for Large-Scale Assessments of Students Reasoning of Complex Science Concepts. Practical Assessment, Research \& Evaluation. 2019;24. doi:10.7275/9h7f-mp76.

Sripathi KN, Moscarella RA, Yoho R, et al. Mixed Student Ideas about Mechanisms of Human Weight Loss. CBE—Life Sciences Education. 2019;18:ar37. doi:10.1187/cbe.18-11-0227.

Sieke SA, McIntosh BB, Steele M, Knight JK. Characterizing students ideas about the effects of a mutation in a noncoding region of DNA. CBE - Life Sciences Education. 2019;18:ar18. doi:10.1187/cbe.18-09-0173.

Nehm RH, Mead L. Evolution assessment: Introduction to the special issue. Evolution: Education and Outreach. 2019;12:7. doi:10.1186/s12052-019-0098-x.

Ha M, Wei X, Wang J, Hou D, Nehm RH. Chinese pre-service biology teachers evolutionary knowledge, reasoning patterns, and acceptance levels. International Journal of Science Education. 2019;41:628–651. doi:10.1080/09500693.2019.1572936.

Sbeglia G, Nehm RH. Do you see what I-SEA? A Rasch analysis of the psychometric properties of the Inventory of Student Evolution Acceptance. Science Education. 2019;103:287–316. doi:10.1002/sce.21494.

Haudek KC, Osborne J, Wilson CD. Using Automated Analysis to Assess Middle School Students Competence with Scientific Argumentation. In: NCME. Toronto, Ontario, Canada: NCME; 2019. https://beyondmultiplechoice.org/sites/default/files/2019-08/NCME%20Haudek%20et%20al%2C%202019.pdf.

Uhl J, Sripathi KN, Saldanha J, Moscarella RA, Haudek KC. Exploring student thinking about genetics information flow in large enrollment undergraduate classes [Presentation]. In: PKAL-Ohio. Dayton, OH; 2019.

Jescovitch LN, Doherty JH, Scott EE, et al. Challenges in Developing Computerized Scoring Models for Principle-Based Reasoning in a Physiology Context. In: NARST. Baltimore, MD; 2019.

Jescovitch LN, Doherty JH, Scott EE, et al. Comparison of analytic and holistic coding approaches and machine learning performances across a flux learning progression. In: Society for the Advancement of Biology Education Research . Minneapolis, MN; 2019.

Saldanha J, Shiroda M, Merrill JE, Urban-Lurain M, Haudek KC. Automated Analyses of Constructed Response (AACR) questions in STEM courses provide insights into student thinking [Poster]. In: CREATE for STEM Mini-Conference. ; 2019.

Uhl J, Fleming M, Merrill JE, Haudek KC. Development of a constructed response automated assessment question to elicit student thinking about epigenetics [Roundtable discussion]. In: National Meeting for the Society for the Advancement of Biology Education Research. Minneapolis, MN; 2019.

You HS, Merrill JE, Haudek KC, Urban-Lurain M. Rasch validation of computer scored constructed response items in undergraduate introductory biology courses. In: NARST. Baltimore, MD; 2019.

Roland K, Kaplan JJ. P-values mean what? Conceptions students are demonstrating on open-ended responses [Poster]. In: U.S. Conference on Teaching Statistics. Penn State University; 2019.

Shiroda M, Uhl J, Saldanha J, Sripathi KN, Merrill JE, Haudek KC. Student use of Vision and Change core concepts during discussions to answer conceptual questions in small group interview [Poster]. In: Gordon Research Conference. Lewiston, ME; 2019.

Shiroda M, Merrill JE, Koslowsky D, Haudek KC. Application of automated analysis of constructed responses in large enrollment undergraduate classrooms [Presentation]. In: Gordon Research Conference. Lewiston, ME; 2019.

Sbeglia G, Nehm RH. 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; 2019.

Cheuk T, Osborne J, Remington-Cunningham K, et al. Towards an Equitable Design Framework of Developing Argumentation in Science Items and Rubrics for Machine Learning. In: NARST. ; 2019.

Roland K, Kaplan JJ. Students’ understanding of definitional and relational characteristics of confidence intervals: Initial results [Poster]. In: Joint Statistical Meetings. Denver, CO; 2019.

Shiroda M, Uhl J, Saldanha J, Sripathi KN, Merrill JE, Haudek KC. Student use of Vision and Change core concepts during discussions to answer conceptual questions in small group interview [Poster]. In: CREATE for STEM Mini-Conference. East Lansing, MI; 2019.

Sieke SA, Knight JK. Student misconceptions regarding mutations in non-coding regions. In: Society for the Advancement of Biology Education Research. Minneapolis, MN; 2019.

Saldanha J, Shiroda M, Merrill JE, Urban-Lurain M, Haudek KC. Automated Analyses of Constructed Response (AACR) questions in STEM courses provide insights into student thinking [Poster]. In: Spring Conference on Student Learning and Success. ; 2019.

Uhl J, Sripathi KN, Santiago M, et al. Automated analysis can guide instructor understanding of student writing about cellular respiration [Poster]. In: CREATE for STEM Mini-Conference. ; 2019.

Fleming M, Uhl J, Haudek KC. Development of constructed response items to elicit student thinking about ecology and use with automated assessment. In: Society for the Advancement of Biology Education Research. Minneapolis, MN; 2019. https://saberbio.wildapricot.org/resources/Documents/SAB\%20ER\%202019\%20Round\%20table\%20abstracts.2.pdf.

Uhl J, Sripathi KN, Santiago M, et al. Automated analysis can guide instructor understanding of student writing about cellular respiration [Poster]. In: Spring Conference on Student Learning and Success. ; 2019.

Jescovitch LN, Haudek KC, Steele M, et al. Predicting mental models of undergraduate thinking in STEM courses using computerized analysis [Poster]. In: STEM Alliance. Michigan State University; 2018.

Sripathi KN, Cockerill H, Knapp J, Urban-Lurain M, Haudek KC, Merrill JE. Probing students’ mental models about structure-function relationships in the cell membrane. In: Conference at the Interface of Discipline-Based Education Research (DBER) in STEM and Psychological Science. St. Louis, MO; 2018.

Santiago M, Kaplan JJ, Prevost LB, Steele M, Haudek KC, Urban-Lurain M. Visualizing students’ conceptual understanding in STEM: Faculty perspectives and a live web app demo. In: AAC&U 2018 Transforming STEM Higher Education. Atlanta, GA; 2018.

Haudek KC. Revealing student thinking using automated analysis of constructed response items. In: Conference at the Interface of Discipline-Based Education Research (DBER) in STEM and Psychological Science. St. Louis, MO; 2018.

Haudek KC, Sripathi KN, Jescovitch LN, et al. Rubrics for student writing: Research tools to uncover trends in student thinking [Poster]. In: STEM Alliance. Michigan State University; 2018.

Santiago M, Steele M, Sripathi KN, et al. Web-based system to support instructors’ understanding of student thinking on key STEM concepts [Poster]. In: STEM Alliance. Michigan State University; 2018.

McCourt J, Andrews TC, Knight JK, et al. What Motivates Biology Instructors to Engage and Persist in Teaching Professional Development? CBE—Life Sciences Education. 2018;16(3). doi:10.1187/cbe.16-08-0241.

Yoho R, Urban-Lurain M, Merrill JE, Haudek KC. Structure and function relationships in the educational expectations. Journal of College Science Teaching. 2018;47(6). https://my.nsta.org/resource/?id=10.2505/4/jcst18_047_06_24.

Pelletreau KN, Knight JK, Lemons PP, et al. 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. 2018;17:es5. doi:10.1187/cbe.17-12-0260.

Smith MK. Using Evidence to Transform Undergraduate Teaching. In: Teaching As Research National Conference. ; 2017.

Moscarella RA, Mazur A, Haudek KC, Urban-Lurain M, Merrill JE. A student reasoning framework describing the role of a mRNA in the transcription of DNA. In: Gordon Research Conference on Undergraduate Biology Education Research. ; 2017.

Lyford A. Leveraging Ensembles of Machine Learning Algorithms to Provide Real-Time Instructor Feedback. In: Joint Statistical Meetings. ; 2017.

Cockerill H, Bierema AM-K, Moscarella RA, Sripathi KN, Yoho R, Haudek KC. Qualitative Analysis of Undergraduate Students’ Writing of the Origin of Genetic Variation [Poster]. In: Mid-Michigan Symposium for Undergraduate Research Experiences. ; 2017.

Wang X, Colton J, Sbeglia G, Finch S, Nehm RH. Longitudinal Learning Dynamics and the Conceptual Restructuring of Evolutionary Understanding. In: NARST. ; 2017.

Yoho R, Foster T, Haudek KC, Urban-Lurain M, Merrill JE. Using Machine Learning to Explore Student Thinking in Constructed Responses – An Example of Enzyme Binding Structure and Function. In: American Society for Microbiology Conference on Undergraduate Education. ; 2017.

Williams C, Nyutu E, Moscarella RA, Sydlik M, Urban-Lurain M. A method for analyzing and visualizing project teamwork [Poster]. In: Michigan Academy of Science, Arts & Letters Conference. ; 2017.

Moscarella RA, Hall D, Haudek KC, et al. Exploring students ideas about the flow of genetic information from DNA to RNA. In: Society for the Advancement of Biology Education Research. ; 2017.

You HS, Sripathi KN, Yoho R, et al. Towards meaningful scores: Development and validation of analytic rubrics for diagnostic purposes. In: Society for the Advacement of Biology Education Research. ; 2017.

Cockerill H, Bierema AM-K, Moscarella RA, Haudek KC, Urban-Lurain M, Merrill JE. Undergraduate students’ understanding of the origin of genetic variation: insights from the automated analysis of constructed responses. In: University Undergraduate Research and Arts Forum. ; 2017.

Carter K, Prevost LB. Investigating text analysis models for assessing student understanding of structure and function. In: Transforming Research in Undergraduate STEM Education (TRUSE). ; 2017.

Tornabene R, Nehm RH, Schmiemann P. Testing the Impact of Situational Features on Measures of Biology Students Genetics Understanding. In: NARST. ; 2017.

Sbeglia G, Nehm RH. 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. ; 2017.