تقرير
Using Large Language Models to Provide Explanatory Feedback to Human Tutors
| العنوان: | Using Large Language Models to Provide Explanatory Feedback to Human Tutors |
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| المؤلفون: | Lin, Jionghao, Thomas, Danielle R., Han, Feifei, Gupta, Shivang, Tan, Wei, Nguyen, Ngoc Dang, Koedinger, Kenneth R. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction |
| الوصف: | Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning. However, providing learners real-time explanatory feedback often presents challenges related to classification accuracy, particularly in domain-specific environments, containing situationally complex and nuanced responses. We present two approaches for supplying tutors real-time feedback within an online lesson on how to give students effective praise. This work-in-progress demonstrates considerable accuracy in binary classification for corrective feedback of effective, or effort-based (F1 score = 0.811), and ineffective, or outcome-based (F1 score = 0.350), praise responses. More notably, we introduce progress towards an enhanced approach of providing explanatory feedback using large language model-facilitated named entity recognition, which can provide tutors feedback, not only while engaging in lessons, but can potentially suggest real-time tutor moves. Future work involves leveraging large language models for data augmentation to improve accuracy, while also developing an explanatory feedback interface. Comment: 12 pages Workshop paper, The 24th International Conference on Artificial Intelligence in Education, AIED 2023 Educational Dialogue Act Classification, Large Language Models, Named Entity Recognition, Tutor Training, Explanatory Feedback, Natural Language Processing |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2306.15498Test |
| رقم الانضمام: | edsarx.2306.15498 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |