Computer-Based Classification of Preservice Physics Teachers’ Written Reflections
Computer-Based Classification of Preservice Physics Teachers’ Written Reflections
复制标题
职前物理教师书面反思的计算机分类
DOI：
10.1007/s10956-020-09865-1
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发表时间：
2020
影响因子：
4.4
通讯作者：
Andreas Borowski
中科院分区：
文献类型：
--
作者：
P. Wulff;David Buschhüter;Andrea Westphal;Ann I. Nowak;Lisa Becker;Hugo Robalino;Manfred Stede;Andreas Borowski
摘要在以大学为基础的师范教育中，以书面形式反思自己的教学行为被认为是促进教师专业成长的一种手段。通过外部反馈可以促进结构化反思的写作。然而，研究人员指出，在副教师教育的反馈往往依赖于整体，而不是更多的基于内容的分析反馈，因为教育工作者往往缺乏资源（例如，时间），以提供更多的分析反馈。为了克服这种障碍，以书面反映反馈，先进的计算机技术可以使用。因此，本研究试图利用自然语言处理和机器学习的技术来训练一个基于计算机的分类器，该分类器对德国大学教师教育计划中物理教师的书面教学反思进行分类。为此，一个反思模型被改编为物理教育。然后，测试了基于计算机的分类器在何种程度上可以准确地分类反思模型的元素在片段的在职物理教师的书面反思。使用单词计数作为预测因子的多项式逻辑回归被发现产生可接受的平均人机协议（F1分数为0.56），因此它可能会进一步推动自动反馈工具的发展，以补充现有的基于数据的书面反馈的整体反馈，分析反馈。
Reflecting in written form on one’s teaching enactments has been considered a facilitator for teachers’ professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers’ written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers’ written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.