Dataset Information

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

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.

SUBMITTER: Yang J

PROVIDER: S-EPMC6974523 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Yang Jun J Zhang Guoyang G Huang Runzhi R Yan Penghui P Hu Peng P Huang Lanting L Meng Tong T Zhang Jie J Liu Ruilin R Zeng Ying Y Wei Chunlan C Shen Huixia H Xuan Miao M Li Qun Q Gong Meiqiong M Chen Wenting W Chen Haifeng H Fan Kaiyang K Wu Jing J Huang Zongqiang Z Cheng Liming L Yang Wenzhuo W

Frontiers in psychology 20200115

<h4>Purpose</h4>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.<h4>Methods</h4>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 mult ...[more]

PMID: 32010007

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Project description:ContextMedical students are expected to self-regulate their learning within complex and unpredictable clinical learning environments. Research increasingly focuses on the effects of social interactions on the development of self-regulation in workplace settings, a notion embodied within the concept of co-regulated learning (CRL). Creating workplace learning environments that effectively foster lifelong self-regulated learning (SRL) requires a deeper understanding of the relationship between CRL and SRL. The aim of this study was therefore to explore medical students' perceptions of CRL in clinical clerkships and its perceived impact on the development of their SRL.MethodsWe conducted semi-structured interviews with 11 purposively sampled medical students enrolled in clinical clerkships at one undergraduate competency-based medical school. Data collection and analysis were conducted iteratively, informed by principles of constructivist grounded theory. Data analysis followed stages of open, axial and selective coding, which enabled us to conceptualise how co-regulation influences the development of students' self-regulation.ResultsData revealed three interrelated shifts in CRL and SRL as students progressed through clerkships. First, students' CRL shifted from a focus on peers to co-regulation with clinician role models. Second, self-regulated behaviour shifted from being externally driven to being internally driven. Last, self-regulation shifted from a task-oriented approach towards a more comprehensive approach focusing on professional competence and identity formation. Students indicated that if they felt able to confidently and proactively self-regulate their learning, the threshold for engaging others in meaningful CRL seemed to be lowered, enhancing further development of SRL skills.ConclusionsFindings from the current study emphasise the notion that SRL and its development are grounded in CRL in clinical settings. To optimally support the development of students' SRL, we need to focus on facilitating and organising learners' engagement in CRL from the start of the medical curriculum.

Project description:BackgroundIn the final year of medical school, the educational focus is on experiences in the clinical environment. This is where students acquire most of their practical knowledge for their future career and need to optimise their Self-Regulated Learning (SRL) strategies. Hence, the current study aims to explore which SRL strategies medical students use during their clerkships in different learning settings.MethodsStructured interviews were conducted between May 2019 and December 2020 with 43 medical students during their final year in Munich, Germany. The students were surveyed about their SRL strategies. The transcribed data were thematically analysed using the measurements Strategy Use (SU) and Strategy Frequency (SF).ResultsInterview data were organized into 11 SRL strategy categories. The most used SRL strategy in general was "seeking information in the internet in form of a text" (SU: 1; SF: 2.605), with an e-learning tool; followed by "seeking social assistance from doctors" (SU: 0.977; SF: 1.884), and "seeking information in books" (SU: 0.884; SF: 1.419). There were differences in the usage of SRL in different learning contexts between female and male students. For example, 95.3% of students are "seeking social assistance from doctors" when having difficulties on the ward, but only 55.8% when they need help with written tasks (e.g. medical letter). The results show a difference in SRL usage when preparing for oral-practical (79.1% books) and written (97.7% e-learning tool) exam. However, it also appears that some students do not have SRL strategies for certain situations, mostly due to a lack of time.ConclusionMedical students in the clinical phase are adapting their SRL strategy to the learning situation. To better support students´ SRL, it is necessary to ensure availability for their preferred resources: e-learning tool and experienced physicians as supervisors. Future research should focus on strategies to handle the limited time during clerkships.

Dataset Information

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

Publications

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

Similar Datasets

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