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首页> 外文期刊>Journal of the American Medical Informatics Association : >Predictors of student success in graduate biomedical informatics training: introductory course and program success.
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Predictors of student success in graduate biomedical informatics training: introductory course and program success.

机译：研究生生物医学信息学培训中学生成功的预测因素：入门课程和课程成功。

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OBJECTIVE: To predict student performance in an introductory graduate-level biomedical informatics course from application data. DESIGN: A predictive model built through retrospective review of student records using hierarchical binary logistic regression with half of the sample held back for cross-validation. The model was also validated against student data from a similar course at a second institution. MEASUREMENTS: Earning an A grade (Mastery) or a C grade (Failure) in an introductory informatics course. RESULTS: The authors analyzed 129 student records at the University of Texas School of Health Information Sciences at Houston (SHIS) and 106 at Oregon Health and Science University Department of Medical Informatics and Clinical Epidemiology (DMICE). In the SHIS cross-validation sample, the Graduate Record Exam verbal score (GRE-V) correctly predicted Mastery in 69.4%. Undergraduate grade point average (UGPA) and underrepresented minority status (URMS) predicted 81.6% of Failures. At DMICE, GRE-V, UGPA, and prior graduate degree significantly correlated with Mastery. Only GRE-V was a significant independent predictor of Mastery at both institutions. There were too few URMS students and Failures at DMICE to analyze. Course Mastery strongly predicted program performance defined as final cumulative GPA at SHIS (n=19, r=0.634, r2=0.40, p=0.0036) and DMICE (n=106, r=0.603, r2=0.36, p<0.001). CONCLUSIONS: The authors identified predictors of performance in an introductory informatics course including GRE-V, UGPA and URMS. Course performance was a very strong predictor of overall program performance. Findings may be useful for selecting students for admission and identifying students at risk for Failure as early as possible.
机译：目的：根据应用数据来预测研究生水平的生物医学信息学入门课程的学生表现。设计：一种预测模型，该模型是通过使用分层二进制逻辑回归对学生记录进行回顾性审查而建立的，其中一半样本被保留以进行交叉验证。该模型还针对第二所大学类似课程的学生数据进行了验证。度量：在信息学入门课程中获得A级（精通）或C级（失败）。结果：作者分析了德克萨斯州休斯敦大学健康信息科学学院（SHIS）的129条学生记录，以及俄勒冈健康科学大学医学信息学和临床流行病学系（DMICE）的106条学生记录。在SHIS交叉验证样本中，研究生成绩考试的口头分数（GRE-V）正确地预测了掌握程度为69.4％。本科平均绩点（UGPA）和代表性不足的少数民族地位（URMS）预测失败率为81.6％。在DMICE，GRE-V，UGPA和以前的研究生学位与精通程度显着相关。在这两个机构中，只有GRE-V是掌握信息的重要独立预测因子。 URMS学生和DMICE的失败人数很少，无法分析。 Course Mastery强烈预测了程序性能，定义为SHIS（n = 19，r = 0.634，r2 = 0.40，p = 0.0036）和DMICE（n = 106，r = 0.603，r2 = 0.36，p <0.001）的最终累积GPA。结论：作者确定了信息学入门课程（包括GRE-V，UGPA和URMS）中绩效的预测因素。课程绩效是整体课程绩效的非常有力的预测指标。调查结果可能有助于选择入学学生，并尽早确定有失败风险的学生。

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