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Im Lehramtsstudium sollen Studierende grundlegende Fähigkeiten zur theoriegeleiteten Unterrichtsplanung erwerben.
In Übereinstimmung mit Modellen zur professionellen Handlungskompetenz von Lehrkräften wird hierbei meist angenommen, dass das im Studienverlauf erworbene Professionswissen eine wesentliche Grundlage für den Aufbau von Fähigkeiten zur Unterrichtsplanung bildet.
Lerngelegenheiten zur Anwendung dieses Professionswissens bieten vor allem schulpraktische Phasen im fortgeschrittenen Studienverlauf. Es wird aber ebenso angenommen, dass gerade Erfahrungen mit der Unterrichtsplanung den Aufbau von Professionswissen unterstützen.
Der Zusammenhang zwischen dem Ausmaß des Professionswissens und der Entwicklung von Planungsfähigkeit ist bisher unzureichend empirisch geklärt. Eine besondere methodische Herausforderung besteht darin, Planungsfähigkeiten sowohl möglichst authentisch als auch auf standardisierte Weise zu erfassen. Zur Untersuchung des genannten Zusammenhangs wurde eine längsschnittliche Studie im Prä-Post-Design bei angehenden Physiklehrkräften (N = 68 im Längsschnitt) an vier Universitäten durchgeführt.
Die Unterrichtsplanungsfähigkeit wurde mit Hilfe eines standardisierten Performanztests vor und nach dem Absolvieren eines Praxissemesters erfasst, indem als Standardsituation der Entwurf einer Unterrichtsstunde zum 3. Newton’schen Axiom unter definierten Zeitvorgaben im Praxissemester simuliert wurde. Zusätzlich wurden das fachliche, fachdidaktische und pädagogische Wissen der Studierenden mit Hilfe standardisierter Instrumente zu beiden Zeitpunkten erhoben, sowie die einschlägigen Lerngelegenheiten im Praxissemester über einen Fragebogen erfasst.
Sowohl für Unterrichtsplanungsfähigkeit als auch für alle Wissensvariablen können Zuwächse im Praxissemester beobachtet werden. Cross-Lagged-Panel-Analysen zeigen, dass insbesondere die Ausprägung des fachdidaktischen und pädagogischen Wissens der Studierenden am Beginn des Praxissemesters die Entwicklung von Unterrichtsplanungsfähigkeit begünstigt.
Computer-based analysis of preservice teachers' written reflections could enable educational scholars to design personalized and scalable intervention measures to support reflective writing. Algorithms and technologies in the domain of research related to artificial intelligence have been found to be useful in many tasks related to reflective writing analytics such as classification of text segments. However, mostly shallow learning algorithms have been employed so far. This study explores to what extent deep learning approaches can improve classification performance for segments of written reflections. To do so, a pretrained language model (BERT) was utilized to classify segments of preservice physics teachers' written reflections according to elements in a reflection-supporting model. Since BERT has been found to advance performance in many tasks, it was hypothesized to enhance classification performance for written reflections as well. We also compared the performance of BERT with other deep learning architectures and examined conditions for best performance. We found that BERT outperformed the other deep learning architectures and previously reported performances with shallow learning algorithms for classification of segments of reflective writing. BERT starts to outperform the other models when trained on about 20 to 30% of the training data. Furthermore, attribution analyses for inputs yielded insights into important features for BERT's classification decisions. Our study indicates that pretrained language models such as BERT can boost performance for language-related tasks in educational contexts such as classification.
Effective professional development programs (PDPs) rely on well-defined goals. However, recent studies on PDPs have not explored the goals from a multi-stakeholder perspective. This study identifies the most important learning goals of PDPs at science research institutions as perceived by four groups of stakeholders, namely teachers, education researchers, government representatives, and research scientists. Altogether, over 100 stakeholders from 42 countries involved in PDPs at science research institutions in Europe and North America participated in a three-round Delphi study. In the first round, the stakeholders provided their opinions on what they thought the learning goals of PDPs should be through an open-ended questionnaire. In the second and third rounds, the stakeholders assessed the importance of the learning goals that emerged from the first round by rating and ranking them, respectively. The outcome of the study is a hierarchical list of the ten most important learning goals of PDPs at particle physics laboratories. The stakeholders identified enhancing teachers' knowledge of scientific concepts and models and enhancing their knowledge of the curricula as the most important learning goals. Furthermore, the results show strong agreement between all the stakeholder groups regarding the defined learning goals. Indeed, all groups ranked the learning goals by their perceived importance almost identically. These outcomes could help policymakers establish more specific policies for PDPs. Additionally, they provide PDP practitioners at science research institutions with a solid base for future research and planning endeavors.
Science education researchers have developed a refined understanding of the structure of science teachers’ pedagogical content knowledge (PCK), but how to develop applicable and situation-adequate PCK remains largely unclear. A potential problem lies in the diverse conceptualisations of the PCK used in PCK research. This study sought to systematize existing science education research on PCK through the lens of the recently proposed refined consensus model (RCM) of PCK. In this review, the studies’ approaches to investigating PCK and selected findings were characterised and synthesised as an overview comparing research before and after the publication of the RCM. We found that the studies largely employed a qualitative case-study methodology that included specific PCK models and tools. However, in recent years, the studies focused increasingly on quantitative aspects. Furthermore, results of the reviewed studies can mostly be integrated into the RCM. We argue that the RCM can function as a meaningful theoretical lens for conceptualizing links between teaching practice and PCK development by proposing pedagogical reasoning as a mechanism and/or explanation for PCK development in the context of teaching practice.
Science education researchers have developed a refined understanding of the structure of science teachers’ pedagogical content knowledge (PCK), but how to develop applicable and situation-adequate PCK remains largely unclear. A potential problem lies in the diverse conceptualisations of the PCK used in PCK research. This study sought to systematize existing science education research on PCK through the lens of the recently proposed refined consensus model (RCM) of PCK. In this review, the studies’ approaches to investigating PCK and selected findings were characterised and synthesised as an overview comparing research before and after the publication of the RCM. We found that the studies largely employed a qualitative case-study methodology that included specific PCK models and tools. However, in recent years, the studies focused increasingly on quantitative aspects. Furthermore, results of the reviewed studies can mostly be integrated into the RCM. We argue that the RCM can function as a meaningful theoretical lens for conceptualizing links between teaching practice and PCK development by proposing pedagogical reasoning as a mechanism and/or explanation for PCK development in the context of teaching practice.