传统评分方法失效，提出基于学习过程的多因素评估模型
From NLG Evaluation to Modern Student Assessment in the Era of ChatGPT: The Great Misalignment Problem and Pedagogical Multi-Factor Assessment (P-MFA)
- 以多因素认证为灵感，构建过程导向的评估框架
- 强调学习过程而非最终输出，应对AI工具带来的挑战
- 适合关注教学本质与学生真实成长的教育研究者
本文探讨了自然语言生成评估与芬兰大学学生评价之间的日益显著的认知类比。我们指出，两个领域都面临'重大错配问题'：当学生越来越多地使用ChatGPT等工具生成高质量成果时，仅关注最终产出的传统评估方式已失去有效性。为此，我们提出一种基于学习过程的多证据评估框架——教育多因素评估（P-MFA），其逻辑借鉴多因素认证机制，强调从多个维度综合判断学习成效。
原文摘要 · Abstract (English)
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.
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