Cross-Lingual and Cross-Cultural Transfer of Talk Move Classification to German Science Classrooms

Abstract

Talk moves are discourse categories used to analyse classroom interactions. They provide insights into the types of exchanges between teachers and students and can serve as indicators of teaching quality, supporting feedback and reflection. The automatic classification of talk moves is therefore valuable for educational research and teacher development. While previous studies have explored this task, almost all have focused on English data. We constructed a small corpus of German science classroom transcripts and investigated whether multilingual language models can classify talk moves effectively under data-scarce conditions. Specifically, we examined (1) training with a very limited amount of German data and (2) cross-lingual transfer from English training data, which also entails cross-cultural adaptation. Our results show that multilingual large language models are capable of cross-lingual and cross-cultural transfer, but models trained directly on even a small amount of German data achieve better performance. Combining English and German data yields the best results overall, though the additional benefit of including English data is small.

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