While open-ended self-explanations have been shown to promote robust learning in multiple studies, they pose significant challenges to automated grading and feedback in technology-enhanced learning, due to the unconstrained nature of the students' input. Our work investigates whether recent advances in Large Language Models, and in particular ChatGPT, can address this issue. Using decimal exercises and student data from a prior study of the learning game Decimal Point, with more than 5,000 open-ended self-explanation responses, we investigate ChatGPT's capability in (1) solving the in-game exercises, (2) determining the correctness of students' answers, and (3) providing meaningful feedback to incorrect answers. Our results showed that ChatGPT can respond well to conceptual questions, but struggled with decimal place values and number line problems. In addition, it was able to accurately assess the correctness of 75% of the students' answers and generated generally high-quality feedback, similar to human instructors. We conclude with a discussion of ChatGPT's strengths and weaknesses and suggest several venues for extending its use cases in digital teaching and learning.

翻译：尽管多项研究表明，开放式自我解释能够促进深度学习，但由于学生输入的无约束性，其在技术增强型学习环境中对自动评分与反馈生成提出了重大挑战。本研究探讨大型语言模型的最新进展（特别是ChatGPT）能否解决这一问题。我们基于先前关于学习游戏"Decimal Point"的研究中收集的五千余条开放式自我解释响应及十进制练习题数据，系统评估了ChatGPT的三种能力：(1) 解答游戏内习题，(2) 判断学生答案正确性，(3) 为错误答案生成有意义的反馈。结果表明，ChatGPT能较好应对概念性问题，但在十进制位值及数轴问题上表现欠佳。此外，它能以75%的准确率评估学生答案正确性，并生成与人类教师水平相当的高质量反馈。本文最后讨论了ChatGPT的优势与局限性，并提出拓展其在数字化教学应用中的若干方向。