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.

翻译：研究表明，学习者在产生解释以支持其推理的过程中，可以对学习产生积极影响。然而，为学习者提供实时的解释性反馈常常面临分类准确性的挑战，尤其是在领域特定环境中，该环境包含情境复杂且微妙的回应。我们提出了两种方法，用于在一节关于如何有效表扬学生的在线课程中，为导师提供实时反馈。这项进行中的工作在纠正性反馈的有效表扬（即基于努力的回应，F1分数=0.811）和无效表扬（即基于结果的回应，F1分数=0.350）的二元分类中显示出相当高的准确性。更值得注意的是，我们介绍了一种通过大语言模型促进的命名实体识别来提供解释性反馈的增强方法的进展，该方法不仅能在课程进行中为导师提供反馈，还能潜在地建议实时的导师行动。未来工作将利用大语言模型进行数据增强以提高准确性，同时开发一个解释性反馈界面。