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

Self-regulated learners actively manage their learning, using strategies to achieve academic goals and adapt to changing demands. This study constructed nomograms to predict self-regulated learning (SRL) levels in Chinese medical undergraduates, using data from five universities. The nomograms combined regression and machine learning to identify significant predictors of SRL, showing good accuracy and generalization.

• 1
  GPA, learning environment, study time, and interest in medicine significantly impact both learning motivation and strategy levels.
• 2
  Traditional teaching methods negatively affect learning strategy levels, while active teaching modes like PBL improve it.
• 3
  Appropriate extracurricular activities (5–10 h per week) serve as a protective factor for learning strategy level.