GPT-3-based automatic generation of advices to support learner reflection and planning

Abstract

We propose a method for automatically generating advice to support the writing of learning plans and reflections. When the quality of the training data is matched, the automatically generated advice tends to be uniform, but at the same time we want to achieve a response that is in line with the descriptions of individual learners. Therefore, the GPT-3 was retrained using the rubric and teacher's composition training data. The automatically generated advice had a ROUGE-1 of 0.405 and a Cosine similarity of 0.906 with the learner's description, and a Cosine similarity of 0.872 with the rubric-aligned canned text. These results approximated those of the teacher's compositions, suggesting that the advice was generated with a balance similar to that of the teacher's compositions. For the cosine similarity, Embedding of GPT-3 was used. However, some failures were observed. Future issues are to be verified by qualitative evaluation.