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Frontiers in psychology
2019;10 2858.

Nomograms Predicting Self-Regulated Learning Levels in Chinese Undergraduate Medical Students.

Jun Yang, Guoyang Zhang, Runzhi Huang, Penghui Yan, Peng Hu, Lanting Huang, Tong Meng, Jie Zhang, Ruilin Liu, Ying Zeng, Chunlan Wei, Huixia Shen, Miao Xuan, Qun Li, Meiqiong Gong, Wenting Chen, Haifeng Chen, Kaiyang Fan, Jing Wu, Zongqiang Huang, Liming Cheng, Wenzhuo Yang
Author Information
  1. Jun Yang: Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
  2. Guoyang Zhang: Graduate School of Education, Shanghai Jiao Tong University, Shanghai, China.
  3. Runzhi Huang: Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
  4. Penghui Yan: The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  5. Peng Hu: The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  6. Lanting Huang: School of Finance, Henan University of Economics and Law, Zhengzhou, China.
  7. Tong Meng: Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
  8. Jie Zhang: Key Laboratory of Arrhythmias, Ministry of Education, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
  9. Ruilin Liu: Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
  10. Ying Zeng: Tongji University School of Medicine, Tongji University, Shanghai, China.
  11. Chunlan Wei: Tongji University School of Medicine, Tongji University, Shanghai, China.
  12. Huixia Shen: Tongji University School of Medicine, Tongji University, Shanghai, China.
  13. Miao Xuan: Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
  14. Qun Li: Office of Educational Administration, Naval Medical University, Shanghai, China.
  15. Meiqiong Gong: Graduate School of Education, Shanghai Jiao Tong University, Shanghai, China.
  16. Wenting Chen: Basic Medical College of Fudan University, Shanghai, China.
  17. Haifeng Chen: Office of Educational Administration, Bengbu Medical College, Bengbu, China.
  18. Kaiyang Fan: Office of Educational Administration, Naval Medical University, Shanghai, China.
  19. Jing Wu: The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  20. Zongqiang Huang: The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  21. Liming Cheng: Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
  22. Wenzhuo Yang: Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
PMID: 32010007 DOI: 10.3389/fpsyg.2019.02858

Abstract

PURPOSE: The purpose of this study was to construct a multi-center cross-sectional study to predict self-regulated learning (SRL) levels of Chinese medical undergraduates.
METHODS: We selected medical undergraduates by random sampling from five universities in mainland China. The classical regression methods (logistic regression and Lasso regression) and machine learning model were combined to identify the most significant predictors of SRL levels. Nomograms were built based on multivariable models. The accuracy, discrimination, and generalization of our nomograms were evaluated by the receiver operating characteristic curves (ROC) and the calibration curves and a high quality external validation.
RESULTS: There were 2052 medical undergraduates from five universities in mainland China initially. The nomograms constructed based on the non-overfitting multivariable models were verified by internal validation (C-index: learning motivation: 0.736; learning strategy: 0.744) and external validation (C-index: learning motivation: 0.986; learning strategy: 1.000), showing decent prediction accuracy, discrimination, and generalization.
CONCLUSION: Comprehensive nomograms constructed in this study were useful and convenient tools to evaluate the SRL levels of undergraduate medical students in China.

Keywords

medical undergraduate multi-center cross-sectional study nomogram self-regulated learning validation

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Journal Article
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Word Cloud

Created with Highcharts 10.0.0learningmedicalstudyvalidationSRLlevelsundergraduatesChinaregressionnomograms0multi-centercross-sectionalself-regulatedChinesefiveuniversitiesmainlandNomogramsbasedmultivariablemodelsaccuracydiscriminationgeneralizationcurvesexternalconstructedC-index:motivation:strategy:undergraduatePURPOSE:purposeconstructpredictMETHODS:selectedrandomsamplingclassicalmethodslogisticLassomachinemodelcombinedidentifysignificantpredictorsbuiltevaluatedreceiveroperatingcharacteristicROCcalibrationhighqualityRESULTS:2052initiallynon-overfittingverifiedinternal7367449861000showingdecentpredictionCONCLUSION:ComprehensiveusefulconvenienttoolsevaluatestudentsPredictingSelf-RegulatedLearningLevelsUndergraduateMedicalStudentsnomogram

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