The article explores the application of sequence labeling in education, specifically for analyzing tutor feedback. By adapting techniques from natural language processing, such as Named Entity Recognition, the study identifies different types of praise within tutor responses. This innovative approach provides crucial insights into effective tutoring practices, enabling tutors to refine their feedback methods. Furthermore, the use of large language models like GPT-3.5 in this context underscores the potential of advanced AI technologies to enhance educational outcomes through tailored feedback mechanisms.
Sequence labeling identifies and categorizes key text segments according to predefined labels, allowing for insightful analysis of feedback components in tutor responses.
In our study, we extend sequence labeling to highlight components of praise in tutor feedback, facilitating deeper understanding of tutors' communication styles.
Collection
[
|
...
]