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This paper presents a case study of digital-supported seminars in pre-service informatics teacher education. The goal of this poster is to present an approach to identify and describe effective teaching patterns for easy reuse. Therefore, a case study first examines learning and instructional designs and their implications with (N = 21) pre-service informatics teachers and (N = 3) University teachers. Therefore, instructional scenarios with associated evaluation data are stored in a database that is used to identify best practices. In this poster, we illustrate the idea how beneficial instructional strategies in pre-service informatics teacher courses can be evaluated, ontologized, and ultimately reused using a relational database and association rule pattern mining algorithms. We conclude this poster with some implications for learning and instructional design in informatics.

In order to understand the digital world and the underlying concepts of digital devices and applications, one of the skills required is algorithmic thinking. Therefore, it is important that not only the students learn algorithmic thinking in school, but also future informatics teachers. To enable them to experience this as effectively as possible, factors such as TPACK competencies and self-efficacy are important. This poster presents a study design on how a teaching-learning lab (TLL) can be integrated into pre-service informatics teacher education. The goal is, on the one hand, to increase the future teachers TPACK competencies and self-efficacy, and, on the other hand, to develop two different didactic approaches that foster pupils' algorithmic thinking. Considering this, a Robotic workshop with a block-based programming language was developed integrating these didactic approaches, both of which are designed to promote pupils' algorithmic thinking. The students conducted these two workshops in our TLL and reflected on their didactic activities. Using a mixed-methods approach, we analyzed how students' TPACK skills and self-efficacy develop over time and how two different didactic approaches differ in terms of promoting algorithmic thinking in pupils. In the next step further data will be collected, evaluated and analyzed to achieve more insights to this preliminary results.

This pilot study explores N = 4 pre-service informatics teachers who were video-recorded when guiding a group of learners in solving algorithmic problem-solving tasks. Before and after the process, they were asked to assess their ability to guide the learners in a real, accepting and emphatic way when addressing and formulating questions or giving feedback. The group of learners also evaluated the competencies of their learning guides.&nbsp

Zur Erforschung von Text und Sprache, einem Kernbereich der Digital Humanities, existieren bereits verschiedene erprobte Werkzeuge und Forschungsmethoden. MUSE4Anything ist ein domänenunabhängiges Forschungswerkzeug, das die detaillierte und strukturierte Erfassung von komplexen Eigenschaften verschiedener Artefakte als Untersuchungsgrundlage von Forschungsfragen unterstützt. Das Werkzeug wurde basierend auf Erfahrungen aus den Projekten MUSE und MUSE4Music entwickelt und soll das dort angewandte Vorgehen für andere Forschungsfragen zugänglich machen. MUSE4Anything lässt sich individuell an die jeweilige Domäne anpassen. Dazu kann ein Benutzer des Werkzeuges beliebige Eigenschaften in Form einer Ontologie definieren. Die Erfassung der Eigenschaften mit dem MUSE4Anything Datenrepositorium erfolgt mit einer automatisch aus der vom Benutzer erstellten Ontologie generierten Eingabemaske. Ein Beitrag zur 8. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2022 Kulturen des digitalen Gedächtnisses.