Publications

Bala AM, Lira CT. A Cooperative Learning Exercise to Improve Student s Understanding of Internal Energy. In: ; 2017.

Sieke SA, Knight JK. Identifying and analyzing student misconceptions about the effects of a non-coding mutation. In: Society for the Advacement of Biology Education Research. ; 2017.

Foster T, Haudek KC, Moscarella RA, Yoho R, Urban-Lurain M, Merrill JE. Computerized scoring of student open response questions: A case study of enzyme structure and function [Poster]. In: National Meeting for the Society for the Advancement of Biology Education Research (SABER). Minneapolis, MN; 2017.

Macneill B, Romero M, Hoskinson A-M, Prevost LB. Follow that carbon atom! Using machine learning to trace student understanding of the carbon cycle. In: Ecological Society of America 102nd Annual Meeting. ; 2017.

Lemons PP. Solving Educational Problems: Opportunities and Challenges in Innovative Teaching. In: Innovative Teaching and Experiential Learning Symposium, Augusta University. ; 2017.

Prevost LB, Smith MK, Knight JK. Using student writing and lexical analysis to reveal student thinking about the role of stop codons in the central dogma. CBE - Life Sciences Education. 2016;15:pii: ar65. doi:10.1187/cbe.15-12-0267.

Ha M, Nehm RH. The Impact of Misspelled Words on Automated Computer Scoring: A Case Study of Scientific Explanations. Journal of Science Education and Technology. 2016;25:358–374. https://link.springer.com/article/10.1007/s10956-015-9598-9.

Federer MR, Nehm RH, Pearl D. Examining Gender Differences in Written Assessment Tasks in Biology: A Case Study of Evolutionary Explanations. CBE—Life Sciences Education. 2016;15(Spring). https://www.lifescied.org/doi/pdf/10.1187/cbe.14-01-0018.

Urban-Lurain M, Bierema AM-K, Haudek KC, et al. 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; 2016. 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/.

Steele M, Merrill JE, Haudek KC, Urban-Lurain M. The Development of Constructed Response Astronomy Assessment Items. In: NARST. Baltimore, MD; 2016. http://matthewmsteele.github.io/NARST2016/.

Prevost LB, Bierema AM-K, Kaplan JJ, et al. An iterative approach to developing, refining and validating machine-scored constructed response assessments. In: American Association for the Advancement of Science. Washington, DC; 2016. http://www.enfusestem.org/projects/an-iterative-approach-to-developing-refining-and-validating-machine-scored-constructed-response-assessments/.

Moscarella RA, Haudek KC, Knight JK, et al. Automated Analysis of Written Assessments in STEM: Methodological Issues. In: NARST. Baltimore, MD; 2016.

Lemons PP, McCourt J, Knight JK, et al. A community of enhanced assessment facilitates reformed teaching. In: American Association for the Advancement of Science. Washington, DC; 2016. http://www.enfusestem.org/projects/a-community-of-enhanced-assessment-facilitates-reformed-teaching/.

Moscarella RA, Haudek KC, Knight JK, et al. Automated Analysis Provides Insights into Students’ Challenges Understanding the Processes Underlying the Flow of Genetic Information. In: NARST. Baltimore, MD; 2016.

Ha M, Nehm RH. Predicting the Accuracy of Computer Scoring of Text: Probabilistic, Multi-Model, and Semantic Similarity Approaches. In: NARST. Baltimore, MD; 2016.

Stuhlsatz M, Wilson CD, Buck-Bracey Z, Urban-Lurain M, Merrill JE, Haudek KC. Applying Automated Analysis to Develop a Cost-Effective Measure of Science Teacher Pedagogical Content Knowledge. In: NARST. Baltimore, MD; 2016.

Lira CT, Steele M, Haudek KC, Merrill JE, Urban-Lurain M. Rapid Analysis of Written Responses to Reveal Student Misconceptions in Thermodynamics. In: Annual Meeting of the AIChE. ; 2016.

Bierema AM-K, Moscarella RA, Urban-Lurain M, Merrill JE, Haudek KC. The Importance of Random Assortment and Blinding in Qualitative Data Analysis. In: NARST. Baltimore, MD; 2016.

Smith MK. Transforming the Classroom and Helping Others to Adopt Teaching Innovations. In: Australian Physiological Society. ; 2016.

Lyford A. Investigating Undergraduate Student Understanding of Graphical Displays of Data through Supervised Learning Algorithms [Poster]. In: SEER Center Inaugural Reception & Poster Session. ; 2016.

Pelletreau KN, Andrews TC, Armstrong N, et al. A clicker-based case study that untangles student thinking about the processes in the central dogma. CourseSource. 2016;3:1-11. doi:https://doi.org/10.24918/cs.2016.15.

Park M, Haudek KC, Urban-Lurain M. Computerized lexical analysis of students’ written responses for diagnosing conceptual understanding of energy. In: NARST. ; 2015.

Chen J, Ha M, Nehm RH. Measuring semantic similarity in written text: Applications to learning and assessment. In: NARST. ; 2015.

Ha M, Nehm RH. Exploring students’ evolutionary explanations across natural, sexual, and artificial selection secenarios. In: NARST. ; 2015.

Ha M, Nehm RH. Assessment item "Cover Stories", semantic similarity and successful computerized scoring of open-ended text. In: NARST. ; 2015.

Steele M, Park M, Urban-Lurain M. AACR: Probing Student Thinking with Computer Analyzed Constructed Response Questions. In: American Association of Physics Teachers Summer Meeting. ; 2015.

Pelletreau KN, Knight JK, Lemons PP, et al. 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. ; 2015.

McCourt J, Andrews TC, Crumbs T, et al. Using faculty learning communities to promote the development of student-centered biology instructors. In: SABER. ; 2015.

Ha M, Nehm RH. Exploring the use of machine translation and machine grading of open-ended assessments in international comparison studies. In: European Science Education Research Association Meeting. ; 2015.

Romero M, Carter K, Prevost LB. Assessing writing about matter and energy: Comparing text analysis and machine learning. In: Society for Advancement of Biology Education Research. ; 2015.

Prevost LB. Assessing student biological understanding using text analysis and machine learning. In: Gordon Research Conference on Undergraduate Biology Education Research. ; 2015.

Moscarella RA, Mazur A, Pelletreau KN, Smith MK, Urban-Lurain M, Merrill JE. The Central Dogma of molecular biology: Investigating student challenges understanding transcription. In: SABER. ; 2015.

Haudek KC, Moscarella RA, Weston MM, Merrill JE, Urban-Lurain M. Construction of rubrics to evaluate content in students’ scientific explanation using computerized text analysis. In: NARST. ; 2015.

Steele M, Haudek KC, Merrill JE, Moscarella RA, Park M, Urban-Lurain M. Automated Reporting of the Lexical and Statistical Analysis of Student Constructed Responses. In: CREATE for STEM Mini-Conference. ; 2015.

Park M, Urban-Lurain M, Haudek KC. A Promising Approach to Examining Student Energy Conceptions. In: CREATE for STEM Mini Conference. ; 2015.

Moscarella RA, Stoltzfus JR, Merrill JE, Haudek KC, Urban-Lurain M. Creation of scoring rubrics assisted by Computerized Text Analysis. In: CREATE for STEM Mini Conference. East Lansing, MI; 2015.

Mazur A, Moscarella RA, Merrill JE, Urban-Lurain M. Biology Central Dogma: What are the Student Learning Challenges?. In: University Undergraduate Research and Arts Forum. Michigan State University; 2015.

Urban-Lurain M, Merrill JE, Haudek KC, et al. Automated analysis of constructed responses: What are we modeling?. In: Society for the Advancement of Biology Education Research. ; 2015.

Crumbs T, McCourt J, Lemons PP. Analysis of Teachers’ Thinking about Pedagogical Practices in College Biology Courses [Poster]. In: University of Georgia Research Experiences for Undergraduates Poster Session. Georgia, USA; 2015.

Moscarella RA, Park M, Steele M, et al. Insights into Students Thinking about the Central Dogma from the Automated Analysis of Constructed Response Questions. In: SABER. ; 2015.

Mazur A, Moscarella RA, Urban-Lurain M, Merrill JE. Student writing reveals misconceptions in the Central Dogma of Biology. In: The Mid-Michigan Symposium for Undergraduate Research Experiences. ; 2015.

Steele M, Park M, Urban-Lurain M. AACR: Automated Analysis of Constructed Response Physics and Astronomy Questions. In: American Association of Physics Teachers Summer Meeting. ; 2015.

Carter K, Prevost LB. Assessing student understanding of core principle structure and function. In: SABER. Minneapolis, MN; 2015.

Smith MK. Moving Beyond First-Generation Biology Education Research: Using Faculty Learning Communities to Enhance Student-Centered Instruction. In: Gordon Research Conference on Undergraduate Biology Education Research. ; 2015.

Prevost LB. Assessing student ecological understanding using text analysis and machine learning. In: The Ecological Society of America Annual Meeting. ; 2015.

Merrill JE, Haudek KC, Smith MK, et al. Faculty Learning Communities as a Focus for Assessment-Driven Instructional Change. In: CREATE for STEM Mini Conference. East Lansing, MI; 2015.

Ha M, Baldwin B, Nehm RH. The Long-Term Impacts of Short-Term Professional Development: Science Teachers and Evolution. Evo Edu Outreach. 2015;8(11). doi:10.1186/s12052-015-0040-9.

Urban-Lurain M, Cooper M, Haudek KC, et al. Expanding a national network for automated analysis of constructed response assessments to reveal student thinking in STEM. Computers in Education Journal. 2015;6(1):65-81.

Weston MM, Haudek KC, Prevost LB, Urban-Lurain M, Merrill JE. Examining the Impact of Question Surface Features on Students’ Answers to Constructed-Response Questions on Photosynthesis. CBE—Life Sciences Education. 2015;14(2). doi:doi:10.1187/cbe.14-07-0110.

Williams LC. Students’ understanding of structure-property relationships and the role of intermolecular forces. Chemistry. 2015;Doctor of Philosophy.