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

doi:10.3389/fpsyg.2019.02858

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

Front Psychol. 2020 Jan 15:10:2858. doi: 10.3389/fpsyg.2019.02858. eCollection 2019.

Authors

Jun Yang¹, Guoyang Zhang², Runzhi Huang^{1

3

4}, Penghui Yan⁵, Peng Hu⁵, Lanting Huang⁶, Tong Meng^{1

3

4}, Jie Zhang⁷, Ruilin Liu¹, Ying Zeng⁸, Chunlan Wei⁸, Huixia Shen⁸, Miao Xuan^{1

9}, Qun Li¹⁰, Meiqiong Gong², Wenting Chen¹¹, Haifeng Chen¹², Kaiyang Fan¹⁰, Jing Wu⁵, Zongqiang Huang⁵, Liming Cheng^{1

3

4}, Wenzhuo Yang¹

Affiliations

¹ Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
² Graduate School of Education, Shanghai Jiao Tong University, Shanghai, China.
³ Division of Spine Surgery, Department of Orthopedics, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
⁴ Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Shanghai, China.
⁵ The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
⁶ School of Finance, Henan University of Economics and Law, Zhengzhou, China.
⁷ Key Laboratory of Arrhythmias, Ministry of Education, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
⁸ Tongji University School of Medicine, Tongji University, Shanghai, China.
⁹ Department of Endocrinology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
¹⁰ Office of Educational Administration, Naval Medical University, Shanghai, China.
¹¹ Basic Medical College of Fudan University, Shanghai, China.
¹² Office of Educational Administration, Bengbu Medical College, Bengbu, China.

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.