Towards Automated Assessment of Scientific Explanations in Turkish using Language Transfer

The paper presents a preliminary study on employing Natural Language Processing (NLP) techniques for automated formative assessment of scientific explanations in Turkish, a morphologically rich language with limited educational resources. The proposed method employs zero and few-shot language transfer techniques for creating Turkish NLP models, obviating the need for extensive collection and annotation of Turkish datasets. The study utilizes multilingual BERT-based pre-trained transformer models. It evaluates the effectiveness of different fine-tuning approaches using an existing annotated dataset in Hebrew. The results indicate that, despite being trained using non-perfectly automated translations from Hebrew responses, the best-performing models demonstrated adequate performance when evaluated on authentic Turkish responses. Thus, this research may provide a useful method for building automated scientific explanations assessment models that are transferred between languages.