Search results for author:"Minsu Ha"
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The Impact of Misspelled Words on Automated Computer Scoring: A Case Study of Scientific Explanations
Minsu Ha; Ross H. Nehm
Journal of Science Education and Technology Vol. 25, No. 3 (2016) pp. 358–374
Automated computerized scoring systems (ACSSs) are being increasingly used to analyze text in many educational settings. Nevertheless, the impact of misspelled words (MSW) on scoring accuracy remains to be investigated in many domains, particularly...
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Transforming Biology Assessment with Machine Learning: Automated Scoring of Written Evolutionary Explanations
Ross H. Nehm; Minsu Ha; Elijah Mayfield
Journal of Science Education and Technology Vol. 21, No. 1 (February 2012) pp. 183–196
This study explored the use of machine learning to automatically evaluate the accuracy of students' written explanations of evolutionary change. Performance of the Summarization Integrated Development Environment (SIDE) program was compared to human ...
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Applying Computerized-Scoring Models of Written Biological Explanations across Courses and Colleges: Prospects and Limitations
Minsu Ha; Ross H. Nehm; Mark Urban-Lurain; John E. Merrill
CBE - Life Sciences Education Vol. 10, No. 4 (December 2011) pp. 379–393
Our study explored the prospects and limitations of using machine-learning software to score introductory biology students' written explanations of evolutionary change. We investigated three research questions: 1) Do scoring models built using...
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Assessing Scientific Practices Using Machine-Learning Methods: How Closely Do They Match Clinical Interview Performance?
Elizabeth P. Beggrow; Minsu Ha; Ross H. Nehm; Dennis Pearl; William J. Boone
Journal of Science Education and Technology Vol. 23, No. 1 (February 2014) pp. 160–182
The landscape of science education is being transformed by the new "Framework for Science Education" (National Research Council, "A framework for K-12 science education: practices, crosscutting concepts, and core ideas." The...
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Clicker Score Trajectories and Concept Inventory Scores as Predictors for Early Warning Systems for Large STEM Classes
Un Jung Lee; Gena C. Sbeglia; Minsu Ha; Stephen J. Finch; Ross H. Nehm
Journal of Science Education and Technology Vol. 24, No. 6 (2015) pp. 848–860
Increasing the retention of STEM (science, technology, engineering, and mathematics) majors has recently emerged as a national priority in undergraduate education. Since poor performance in large introductory science and math courses is one...