{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:14:18Z","timestamp":1772903658253,"version":"3.50.1"},"reference-count":50,"publisher":"ASME International","issue":"3","license":[{"start":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T00:00:00Z","timestamp":1663027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"funder":[{"DOI":"10.13039\/100000147","name":"Division of Civil, Mechanical and Manufacturing Innovation","doi-asserted-by":"publisher","award":["1728165"],"award-info":[{"award-number":["1728165"]}],"id":[{"id":"10.13039\/100000147","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Extracting an individual\u2019s 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\u2019 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\u2019 question-specific subgraphs to question responses. We follow a hierarchical Bayesian approach to estimate individuals\u2019 DAGs from observations. The method is illustrated using 205 engineering students\u2019 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\u2019 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.<\/jats:p>","DOI":"10.1115\/1.4055596","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T08:45:51Z","timestamp":1663058751000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":4,"title":["A Bayesian Hierarchical Model for Extracting Individuals\u2019 Theory-Based Causal Knowledge"],"prefix":"10.1115","volume":"23","author":[{"given":"Atharva","family":"Hans","sequence":"first","affiliation":[{"name":"Purdue University School of Mechanical Engineering, , West Lafayette, IN 47907"}]},{"given":"Ashish M.","family":"Chaudhari","sequence":"additional","affiliation":[{"name":"Massachusetts Institute Technology Institute of Data, Systems and Society, , Cambridge, MA 02139"}]},{"given":"Ilias","family":"Bilionis","sequence":"additional","affiliation":[{"name":"Purdue University School of Mechanical Engineering, , West Lafayette, IN 47907"}]},{"given":"Jitesh H.","family":"Panchal","sequence":"additional","affiliation":[{"name":"Purdue University School of Mechanical Engineering, , West Lafayette, IN 47907"}]}],"member":"33","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"issue":"1","key":"2023121303514318300_CIT0001","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1016\/j.compedu.2012.06.012","article-title":"Using Bayesian Networks to Improve Knowledge Assessment","volume":"60","author":"Mill\u00e1n","year":"2013","journal-title":"Comput. 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