A Bayesian Hierarchical Model for Extracting Individuals' Theory-based Causal Knowledge
A Bayesian Hierarchical Model for Extracting Individuals' Theory-based Causal Knowledge
复制标题
用于提取个体基于理论的因果知识的贝叶斯分层模型
DOI：
10.1115/1.4055596
复制
发表时间：
2022
影响因子：
3.1
通讯作者：
Panchal, Jitesh H.
中科院分区：
文献类型：
--
作者：
Hans, Atharva;Chaudhari, Ashish M.;Bilionis, Ilias;Panchal, Jitesh H.
提取个人的科学知识对于改善教育评估和理解工程活动中的认知任务（如推理和决策）至关重要。然而，知识提取是一个几乎不可能的奋进，如果知识和可用的观测数据是不受限制的。本文的目的是量化个人的理论为基础的因果知识，从他们的回答给定的问题。我们的方法使用有向无环图（DAG）来表示给定理论的因果知识和基于图的逻辑模型，该模型将个人的问题特定子图映射到问题响应。我们遵循分层贝叶斯方法来估计个人的DAG从观察。使用205名工程专业学生对机械零件疲劳分析问题的回答说明了该方法。在我们的研究结果中，我们展示了如何开发的方法提供人口水平的DAG和DAG的个别学生的估计。这种双重表征对于补救是必不可少的，因为它使我们能够识别出一个群体或个人所挣扎的理论部分以及他们已经掌握的部分。该方法的一个补充是，它能够基于推断的个体特异性DAG预测个体对新问题的反应。后者对人类问题解决的描述性建模有影响，这是社会技术系统建模的一个关键因素。
Extracting an individual’s scientific knowledge is essential for improving educational assessment and understanding cognitive tasks in engineering activities such as reasoning and decision-making. However, knowledge extraction is an almost impossible endeavor if the domain of knowledge and the available observational data are unrestricted. The objective of this paper is to quantify individuals’ theory-based causal knowledge from their responses to given questions. Our approach uses directed-acyclic graphs (DAGs) to represent causal knowledge for a given theory and a graph-based logistic model that maps individuals’ question-specific subgraphs to question responses. We follow a hierarchical Bayesian approach to estimate individuals’ DAGs from observations. The method is illustrated using 205 engineering students’ responses to questions on fatigue analysis in mechanical parts. In our results, we demonstrate how the developed methodology provides estimates of population-level DAG and DAGs for individual students. This dual representation is essential for remediation since it allows us to identify parts of a theory that a population or individual struggles with and parts they have already mastered. An addendum of the method is that it enables predictions about individuals’ responses to new questions based on the inferred individual-specific DAGs. The latter has implications for the descriptive modeling of human problem-solving, a critical ingredient in sociotechnical systems modeling.