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Professional knowledge is an important source of science teachers' actions in the classroom (e.g., personal professional content knowledge [pedagogical content knowledge, PCK] is the source of enacted PCK in the refined consensus model [RCM] for PCK). However, the evidence for this claim is ambiguous at best. This study applied a cross-lagged panel design to examine the relationship between professional knowledge and actions in one particular instructional situation: explaining physics. Pre- and post a field experience (one semester), 47 preservice physics teachers from four different universities were tested for their content knowledge (CK), PCK, pedagogical knowledge (PK), and action-related skills in explaining physics. The study showed that joint professional knowledge (the weighted sum of CK, PCK, and PK scores) at the beginning of the field experience impacted the development of explaining skills during the field experience (beta = .38**). We interpret this as a particular relationship between professional knowledge and science teachers' action-related skills (enacted PCK): professional knowledge is necessary for the development of explaining skills. That is evidence that personal PCK affects enacted PCK. In addition, field experiences are often supposed to bridge the theory-practice gap by transforming professional knowledge into instructional practice. Our results suggest that for field experiences to be effective, preservice teachers should start with profound professional knowledge.
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.