Publications

A library of publications associated with AACR and Beyond Multiple Choice

2016

Conference Paper

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.

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.

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

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

Miscellaneous

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.

Journal Article

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.

2015

Conference Paper

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.

Thesis

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

Journal Article

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.

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.

2014

Journal Article

Federer MR, Nehm RH, Opfer JE, Pearl D. 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. 2014. doi:10.1007/s11165-014-9435-9.

Kaplan JJ, Haudek KC, Ha M, Rogness N, Fisher DG. Using Lexical Analysis Software to Assess Student Writing in Statistics. Technology Innovations in Statistics Education. 2014;8(1). http://escholarship.org/uc/item/57r90703.

Moharreri K, Ha M, Nehm RH. EvoGrader: an online formative assessment tool for automatically evaluating written evolutionary explanations. Evolution: Education and Outreach. 2014;7:15. doi:10.1186/s12052-014-0015-2.

Conference Paper

Ha M, Ponnuraj GT, Nehm RH. EvoGrader: An Online Formative Assessment Tool for Automatically Analyzing Students Ideas in Written Evolutionary Explanations. In: Saber. ; 2014.

Conference Proceedings

Corley L, Olwine J, Noyes K, Cooper M, Haudek KC, Urban-Lurain M. Automated Analysis of Students Constructed Explanations in Chemistry. Biennial Conference on Chemical Education. 2014.

Kaplan JJ, Gabrosek J, Curtiss P, Malone CJ. Everyone Knows What a Histogram Is, or Do They?: How Non-Statisticians Read Histograms. Joint Statistical Meetings. 2014.

Prevost LB. Exploring students mental models of matter and energy transformation through lexical analysis of written assessments. Ecological Society of America annual meeting. 2014.

Prevost LB. Assessing students ability to trace matter and energy using lexical analysis of written assessments. Society for Advancement of Biology Education Research annual meeting. 2014.