从自然语言生成评估到现代学生评估：在ChatGPT时代的大偏差问题与教学多因素评估（P-MFA）
From NLG Evaluation to Modern Student Assessment in the Era of ChatGPT: The Great Misalignment Problem and Pedagogical Multi-Factor Assessment (P-MFA)
AI总结：
本文提出P-MFA模型，旨在解决自然语言生成评估与学生评估中因过度依赖最终产品而产生的大偏差问题，通过多因素认证逻辑构建过程性评估框架。
摘要：
本文探讨了自然语言生成评估与芬兰大学学生评分之间日益增长的认知平行性。我们主张，这两个领域都面临巨大的偏差问题。随着学生越来越多地使用像ChatGPT这样的工具来生成复杂的输出，传统仅关注最终产品而非学习过程的评估方法已失去其有效性。为解决这一问题，我们引入了教学多因素评估（P-MFA）模型，这是一种基于多因素认证逻辑的过程性、多证据框架。
英文摘要：
This paper explores the growing epistemic parallel between NLG evaluation and grading of students in a Finnish University. We argue that both domains are experiencing a Great Misalignment Problem. As students increasingly use tools like ChatGPT to produce sophisticated outputs, traditional assessment methods that focus on final products rather than learning processes have lost their validity. To address this, we introduce the Pedagogical Multi-Factor Assessment (P-MFA) model, a process-based, multi-evidence framework inspired by the logic of multi-factor authentication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
