AACR Publications

We are pleased to share with you some of our academic publications and other recent efforts to disseminate important developments and research to the broader community. Some publications may be downloadable directly from us, while others may be accessed through the Journal publisher provided in the links. Please feel free to contact the author(s) if you have specific questions about any of the work listed here.

2015

Nehm, R. H. (2015). Evolution Challenges: Integrating Research and Practice in Teaching and Learning about Evolution. Science & Education, 24, 481-485. doi:10.1007/s11191-014-9705-y
Prevost, L. (2015). Assessing student biological understanding using text analysis and machine learning. . In The Gordon Research Conference on Undergraduate Biology Education Research. .
Prevost, L. B. (2015). Assessing Student Writing Using Text Analysis: Understanding the Central Dogma. In Virginia Polytechnic Institute and State University, Invited Seminar.
Prevost, L. (2015). Assessing student ecological understanding using text analysis and machine learning. In The Ecological Society of America Annual Meeting.
Romero, M., Carter, K., & Prevost, L.. (2015). Assessing writing about matter and energy: Comparing text analysis and machine learning. . In Society for Advancement of Biology Education Research Annual Meeting.
Steele, M., Park, M., & Urban-Lurain, M.. (2015). AACR: Automated Analysis of Constructed Response Physics and Astronomy Questions. In American Association of Physics Teachers Summer Meeting.

2014

Kaplan, J. J., Haudek, K. C., Ha, M., Rogness, N., & Fisher, D.. (2014). Using Lexical Analysis Software to Assess Student Writing in Statistics. Technology Innovations in Statistics Education. presented at the 10/2014. Retrieved from http://escholarship.org/uc/item/57r90703

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This material is based upon work supported by the National Science Foundation (DUE grants: 1438739, 1323162, 1347740, 0736952 and 1022653). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.