Refine
Document Type
- Article (8)
- Monograph/Edited Volume (2)
- Doctoral Thesis (1)
- Postprint (1)
Is part of the Bibliography
- yes (12)
Keywords
- Reflexion (6)
- Feedback (3)
- Lehrkräftebildung (3)
- Reflexivität (3)
- NLP (2)
- literature review (2)
- machine learning (2)
- pedagogical content knowledge (PCK) (2)
- pedagogical reasoning (2)
- refined consensus model (RCM) (2)
- reflexion (2)
- science teaching (2)
- teaching practice (2)
- Attention to classroom events (1)
- Deep learning (1)
- Evaluation (1)
- Fortbildung (1)
- KI-Anwendung (1)
- Konzepte (1)
- Künstliche Intelligenz (1)
- Lehrerbildung (1)
- ML (1)
- Maschinelles Lernen (1)
- Natural Language Processing (1)
- Noticing (1)
- Physikdidaktik (1)
- Professionalisierung (1)
- Referendariat (1)
- Reflection (1)
- Reflection Skills (1)
- Reflective writing (1)
- Reflexionskompetenz (1)
- Reflexivity (1)
- Sammelband (1)
- Science education (1)
- Selbstreflexion (1)
- Tagung (1)
- Teacher Education (1)
- Testinstrumente (1)
- Unterrichtsanalyse (1)
- Vernetzung (1)
- application of artificial intelligence (1)
- artificial intelligence (1)
- concepts (1)
- evaluation (1)
- feedback (1)
- natural language processing (1)
- networking (1)
- pedagogical content knowledge (1)
- professionalization (1)
- refined consensus model (1)
- teacher education (1)
- teacher training (1)
- test instruments (1)
- zweite Ausbildungsphase (1)
Schulpraktische Phasen stellen eine bedeutende praxisnahe Lerngelegenheit im Lehramtsstudium dar, da sie Raum für umfangreiche Reflexionen der eigenen Lernerfahrung bieten. Das im Studium erworbene theoretisch-formale Wissen steht hierbei dem praktischen Wissen und Können gegenüber. Mit der professionellen Entwicklung im Referendariat, besonders im Kompetenzbereich des Unterrichtens, kann geschlussfolgert werden, dass sich eine Reflexion über eher fachliche Aspekte unter den Studierenden im Referendariat auf eine Reflexion über eher überfachliche und pädagogische Aspekte weitet. Infolge der Analyse von N = 55 schriftlichen Fremdreflexionen von angehenden Physiklehrkräften aus Studium und Referendariat konnte diese Hypothese für den Bereich der Unterrichtsanalyse und -reflexion unterstützt werden. Weiter wurde aus der Videovignette ein Workshopangebot für Lehrkräfte der zweiten und dritten Phase der Lehrkräftebildung entwickelt, erprobt und evaluiert.
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.