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PSI-Potsdam
(2018)
In Brandenburg kommt der Universität Potsdam eine besondere Rolle zu: Sie ist die einzige, an der zukünftige Lehrerinnen und Lehrer die erste Phase ihres Werdegangs – das Lehramtsstudium – absolvieren können. Vor diesem Hintergrund wurde bereits kurz nach der Gründung im Jahr 1991 das „Potsdamer Modell der Lehrerbildung“ entwickelt. Dieses Modell strebt fortlaufend eine enge Verzahnung von Theorie und Praxis über das gesamte Studium hinweg an und bindet hierfür die schulpraktischen Studienanteile in besonderer Weise ein. Eine erneute Stärkung erfuhr die Lehrerbildung im Dezember 2014 mit der Gründung des Zentrums für Lehrerbildung und Bildungsforschung (ZeLB). Aus der koordinierenden Arbeit des Zentrums entstand das fakultätsübergreifende Projekt „Professionalisierung – Schulpraktische Studien – Inklusion“ (PSI-Potsdam) das im Rahmen der Qualitätsoffensive Lehrerbildung des Bundesministeriums für Bildung und Forschung erfolgreich gefördert wurde (2015–2018) und dessen Verlängerung (2019–2023) bewilligt ist.
Der vorliegende Band vermittelt in den drei großen Kapiteln „Erhebungsinstrumente“, „Seminarkonzepte“ und „Vernetzungen“ einen Überblick über einige der praxisnahen Forschungszugänge, hochschuldidaktischen Ansätze und Strategien zur Vernetzung innerhalb der Lehrerbildung, die im Rahmen von PSI-Potsdam entwickelt und umgesetzt wurden. Die Beiträge wurden mit dem Ziel verfasst, Kolleginnen und Kollegen an Universitäten und Hochschulen, Akteur_innen des Vorbereitungsdiensts sowie der Fort- und Weiterbildung von Lehrkräften möglichst konkrete Einblicke zu gewähren.
Unter der Herausgeberschaft von Prof. Dr. Andreas Borowski (Fachdidaktik Physik), Prof. Dr. Antje Ehlert (Inklusionspädagogik mit dem Förderschwerpunkt Lernen) und Prof. Dr. Helmut Prechtl (Fachdidaktik Biologie) vereinen sich Autor_innen mit breit gestreuter fachdidaktischer und bildungswissenschaftlicher Expertise.
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.
Science education researchers typically face a trade-off between more quantitatively oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of novel hypotheses. More recently, open-ended, constructed response items were used to combine both approaches and advance assessment of complex science-related skills and competencies. For example, research in assessing science teachers' noticing and attention to classroom events benefitted from more open-ended response formats because teachers can present their own accounts. Then, open-ended responses are typically analyzed with some form of content analysis. However, language is noisy, ambiguous, and unsegmented and thus open-ended, constructed responses are complex to analyze. Uncovering patterns in these responses would benefit from more principled and systematic analysis tools. Consequently, computer-based methods with the help of machine learning and natural language processing were argued to be promising means to enhance assessment of noticing skills with constructed response formats. In particular, pretrained language models recently advanced the study of linguistic phenomena and thus could well advance assessment of complex constructs through constructed response items. This study examines potentials and challenges of a pretrained language model-based clustering approach to assess preservice physics teachers' attention to classroom events as elicited through open-ended written descriptions. It was examined to what extent the clustering approach could identify meaningful patterns in the constructed responses, and in what ways textual organization of the responses could be analyzed with the clusters. Preservice physics teachers (N = 75) were instructed to describe a standardized, video-recorded teaching situation in physics. The clustering approach was used to group related sentences. Results indicate that the pretrained language model-based clustering approach yields well-interpretable, specific, and robust clusters, which could be mapped to physics-specific and more general contents. Furthermore, the clusters facilitate advanced analysis of the textual organization of the constructed responses. Hence, we argue that machine learning and natural language processing provide science education researchers means to combine exploratory capabilities of qualitative research methods with the systematicity of quantitative methods.