机器学习混合方法文本分析——以生物教育学生写作自动评分模型为例

IF 3.8 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
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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引用次数: 4

摘要

使用传统的定性方法根据学生的写作来评估他们的知识是非常耗时的。为了提高文本分析的速度和一致性,我们提出了混合方法开发的机器学习预测模型来分析学生的写作。我们的方法包括两个阶段:首先是探索性顺序设计,其次是迭代复杂设计。我们首先在学生写作中使用类别(想法)的定性编码来训练我们的预测模型。接下来,我们根据指导用户的反馈修改了我们的模型。该模型本身突出了需要修订的类别。对混合方法研究的贡献在于我们创新地使用机器学习工具作为快速,一致的额外编码器,以及可以预测新学生写作代码的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Journal of Mixed Methods Research
Journal of Mixed Methods Research SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.40
自引率
28.20%
发文量
36
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.
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