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      Predicting time to graduation at a large enrollment American university

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          Abstract

          The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto’s Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).

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          Most cited references54

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          SMOTE: Synthetic Minority Over-sampling Technique

          An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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            Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls

            Most studies have some missing data. Jonathan Sterne and colleagues describe the appropriate use and reporting of the multiple imputation approach to dealing with them
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              Applied Logistic Regression

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2020
                13 November 2020
                : 15
                : 11
                : e0242334
                Affiliations
                [1 ] Department of Physics, Centre for Computing in Science Education, University of Oslo, Blindern, Oslo, Norway
                [2 ] Department of Mathematics and Statistics, University of Oslo, Blindern, Oslo, Norway
                [3 ] Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan, United States of America
                [4 ] National Superconducting Cyclotron Laboratory and Facility for Rare Ion Beams, Michigan State University, East Lansing, Michigan, United States of America
                [5 ] CREATE for STEM Institute, Michigan State University, East Lansing, Michigan, United States of America
                Eötvös Loránd University, HUNGARY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-2258-3836
                https://orcid.org/0000-0002-7441-6880
                https://orcid.org/0000-0003-0717-4583
                Article
                PONE-D-20-14023
                10.1371/journal.pone.0242334
                7665823
                33186404
                d1e0c1c1-09d6-4927-a532-86422364001d
                © 2020 Aiken et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 13 May 2020
                : 30 October 2020
                Page count
                Figures: 10, Tables: 0, Pages: 28
                Funding
                MDC - The Michigan State University College of Natural Sciences including the STEM Gateway Fellowship and the Lappan-Phillips Foundation https://msu.edu. MDC - the Association of American Universities https://www.aau.edu/. MDC, MJH - the Norwegian Agency for Quality Assurance in Education (NOKUT), which supports the Center for Computing in Science Education. https://www.nokut.no/en/. MJH - INTPART project of the Research Council of Norway (Grant No. 288125). https://www.forskningsradet.no/en/call-for-proposals/2019/funding-for-international-partnerships/. MJH - U.S. National Science Foundation (Grant No. PHY-1404159). https://www.nsf.gov/index.jsp. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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