发表机构
SUPSI; IDSIA; University of Almería（瑞士意大利语区高等专业学院; 达勒·莫勒人工智能研究所; 阿尔梅里亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将结构因果建模用于学生能力评估，构建了相关协议，可建模提示等干预措施及反事实分析，无需概率假设，并用义务教育学生算法技能评估数据验证方法。
AI 中文摘要
准确评估学生能力对教育工作者识别个体需求、设计针对性干预措施及评估教育策略有效性至关重要。实证评估程序通常基于心理测量模型，如项目反应理论，该理论将学生能力水平与评估任务表现关联起来。本文倡导在教育评估中采用结构因果建模方法，超越概率信念更新，转向明确支持干预和反事实推理的框架。我们提出了相应的构建协议，并分析了标准关联模型无法实现的推理形式的实际相关性，包括对提示等干预措施的明确建模及相关反事实情景分析。尽管该协议需要从专家处获取结构方程，但所需信息纯粹是逻辑性的，不依赖概率等站不住脚的假设。我们使用来自采用复杂任务的评估的数据来说明该方法，这些任务旨在评估义务教育阶段学生的算法技能。
英文摘要
Accurate assessment of student competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluate the effectiveness of educational strategies. Empirical assessment procedures are typically grounded in psychometric models, such as item response theory, which relate student competence levels to performance on assessment tasks. In this paper, we advocate adopting a structural causal modelling approach to educational assessment, moving beyond probabilistic belief updating toward a framework that explicitly supports interventional and counterfactual reasoning. We propose a corresponding protocol for its construction and analyse the practical relevance of forms of reasoning that remain inaccessible to standard associative models, including the explicit modelling of interventions such as hints and the related counterfactual scenario analysis. Although our protocol requires the structural equations to be elicited from experts, the necessary information is purely logical and does not rely on probabilistic, less tenable assumptions. We illustrate the approach using data from an assessment that employs complex tasks designed to measure compulsory school student algorithmic skills.
