An application of Bayesian inference to examine student retention and attrition in the STEM classroom
IntroductionAs artificial intelligence (AI) technology becomes more widespread in the classroom environment, educators have relied on data-driven machine learning (ML) techniques and statistical frameworks to derive insights into student performance patterns. Bayesian methodologies have emerged as a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Education |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feduc.2023.1073829/full |
| _version_ | 1811164263263240192 |
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| author | Roberto Bertolini Stephen J. Finch Ross H. Nehm |
| author_facet | Roberto Bertolini Stephen J. Finch Ross H. Nehm |
| author_sort | Roberto Bertolini |
| collection | DOAJ |
| description | IntroductionAs artificial intelligence (AI) technology becomes more widespread in the classroom environment, educators have relied on data-driven machine learning (ML) techniques and statistical frameworks to derive insights into student performance patterns. Bayesian methodologies have emerged as a more intuitive approach to frequentist methods of inference since they link prior assumptions and data together to provide a quantitative distribution of final model parameter estimates. Despite their alignment with four recent ML assessment criteria developed in the educational literature, Bayesian methodologies have received considerably less attention by academic stakeholders prompting the need to empirically discern how these techniques can be used to provide actionable insights into student performance.MethodsTo identify the factors most indicative of student retention and attrition, we apply a Bayesian framework to comparatively examine the differential impact that the amalgamation of traditional and AI-driven predictors has on student performance in an undergraduate in-person science, technology, engineering, and mathematics (STEM) course.ResultsInteraction with the course learning management system (LMS) and performance on diagnostic concept inventory (CI) assessments provided the greatest insights into final course performance. Establishing informative prior values using historical classroom data did not always appreciably enhance model fit.DiscussionWe discuss how Bayesian methodologies are a more pragmatic and interpretable way of assessing student performance and are a promising tool for use in science education research and assessment. |
| first_indexed | 2024-04-10T15:18:41Z |
| format | Article |
| id | doaj.art-c104d692d872432a821e6a8e194de180 |
| institution | Directory Open Access Journal |
| issn | 2504-284X |
| language | English |
| last_indexed | 2024-04-10T15:18:41Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Education |
| spelling | doaj.art-c104d692d872432a821e6a8e194de1802023-02-14T17:45:18ZengFrontiers Media S.A.Frontiers in Education2504-284X2023-02-01810.3389/feduc.2023.10738291073829An application of Bayesian inference to examine student retention and attrition in the STEM classroomRoberto Bertolini0Stephen J. Finch1Ross H. Nehm2Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United StatesDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United StatesDepartment of Ecology and Evolution, Program in Science Education, Stony Brook University, Stony Brook, NY, United StatesIntroductionAs artificial intelligence (AI) technology becomes more widespread in the classroom environment, educators have relied on data-driven machine learning (ML) techniques and statistical frameworks to derive insights into student performance patterns. Bayesian methodologies have emerged as a more intuitive approach to frequentist methods of inference since they link prior assumptions and data together to provide a quantitative distribution of final model parameter estimates. Despite their alignment with four recent ML assessment criteria developed in the educational literature, Bayesian methodologies have received considerably less attention by academic stakeholders prompting the need to empirically discern how these techniques can be used to provide actionable insights into student performance.MethodsTo identify the factors most indicative of student retention and attrition, we apply a Bayesian framework to comparatively examine the differential impact that the amalgamation of traditional and AI-driven predictors has on student performance in an undergraduate in-person science, technology, engineering, and mathematics (STEM) course.ResultsInteraction with the course learning management system (LMS) and performance on diagnostic concept inventory (CI) assessments provided the greatest insights into final course performance. Establishing informative prior values using historical classroom data did not always appreciably enhance model fit.DiscussionWe discuss how Bayesian methodologies are a more pragmatic and interpretable way of assessing student performance and are a promising tool for use in science education research and assessment.https://www.frontiersin.org/articles/10.3389/feduc.2023.1073829/fullBayesian methodsretention and attritionlearning management systemconcept inventorymachine learningSTEM education |
| spellingShingle | Roberto Bertolini Stephen J. Finch Ross H. Nehm An application of Bayesian inference to examine student retention and attrition in the STEM classroom Frontiers in Education Bayesian methods retention and attrition learning management system concept inventory machine learning STEM education |
| title | An application of Bayesian inference to examine student retention and attrition in the STEM classroom |
| title_full | An application of Bayesian inference to examine student retention and attrition in the STEM classroom |
| title_fullStr | An application of Bayesian inference to examine student retention and attrition in the STEM classroom |
| title_full_unstemmed | An application of Bayesian inference to examine student retention and attrition in the STEM classroom |
| title_short | An application of Bayesian inference to examine student retention and attrition in the STEM classroom |
| title_sort | application of bayesian inference to examine student retention and attrition in the stem classroom |
| topic | Bayesian methods retention and attrition learning management system concept inventory machine learning STEM education |
| url | https://www.frontiersin.org/articles/10.3389/feduc.2023.1073829/full |
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