Kamali N. Sripathi, R. Moscarella, Matthew Steele, Rachel Yoho, Hyesun You, L. Prevost, M. Urban-Lurain, John E. Merrill, Kevin C. Haudek
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Machine Learning Mixed Methods Text Analysis: An Illustration From Automated Scoring Models of Student Writing in Biology Education
Assessing student knowledge based on their writing using traditional qualitative methods is time-consuming. To improve speed and consistency of text analysis, we present our mixed methods development of a machine learning predictive model to analyze student writing. Our approach involves two stages: first an exploratory sequential design, and second an iterative complex design. We first trained our predictive model using qualitative coding of categories (ideas) in student writing. We next revised our model based on feedback from instructor-users. The model itself highlighted categories in need of revision. The contribution to mixed methods research lies in our innovative use of the machine learning tool as a rapid, consistent additional coder, and a resource that can predict codes for new student writing.
期刊介绍:
The Journal of Mixed Methods Research serves as a premiere outlet for ground-breaking and seminal work in the field of mixed methods research. Of primary importance will be building an international and multidisciplinary community of mixed methods researchers. The journal''s scope includes exploring a global terminology and nomenclature for mixed methods research, delineating where mixed methods research may be used most effectively, creating the paradigmatic and philosophical foundations for mixed methods research, illuminating design and procedure issues, and determining the logistics of conducting mixed methods research. JMMR invites articles from a wide variety of international perspectives, including academics and practitioners from psychology, sociology, education, evaluation, health sciences, geography, communication, management, family studies, marketing, social work, and other related disciplines across the social, behavioral, and human sciences.