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.

以书面形式反映出自己的教学法被认为是促进教师在大学任职前教师教育中职业发展的一个促进因素。可以通过外部反馈来编写结构化的反射。但是，研究人员指出，职前教师教育中的反馈通常依赖于整体而不是基于内容的分析反馈，因为教育者通常缺乏提供更多分析反馈的资源（例如时间）。为了克服这种阻碍反馈以书面反思的障碍，可以利用计算机技术的进步。因此，这项研究试图利用自然语言处理和机器学习技术来训练基于计算机的分类器，该分类器对职前物理教师在德国大学教师教育计划中对其教学表现的书面思考进行分类。为此，将反射模型应用于物理教育。然后测试了在多大程度上，基于计算机的分类器可以在职前物理教师的书面反思部分中对反思模型的元素进行准确分类。发现使用单词计数作为预测因子的多项式逻辑回归可以产生可接受的平均人机协议（在保留的测试数据集中为0的F1评分。