Relationships between medical students' co-regulatory network characteristics and self-regulated learning: a social network study.
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ABSTRACT: Introduction
Recent conceptualizations of self-regulated learning acknowledge the importance of co-regulation, i.e., students' interactions with others in their networks to support self-regulation. Using a social network approach, the aim of this study is to explore relationships between characteristics of medical students' co-regulatory networks, perceived learning opportunities, and self-regulated learning.Methods
The authors surveyed 403 undergraduate medical students during their clinical clerkships (response rate 65.5%). Using multiple regression analysis, structural equation modelling techniques, and analysis of variance, the authors explored relationships between co-regulatory network characteristics (network size, network diversity, and interaction frequency), students' perceptions of learning opportunities in the workplace setting, and self-reported self-regulated learning.Results
Across all clerkships, data showed positive relationships between tie strength and self-regulated learning (β = 0.095, p < 0.05) and between network size and tie strength (β = 0.530, p < 0.001), and a negative relationship between network diversity and tie strength (β = -0.474, p < 0.001). Students' perceptions of learning opportunities showed positive relationships with both self-regulated learning (β = 0.295, p < 0.001) and co-regulatory network size (β = 0.134, p < 0.01). Characteristics of clerkship contexts influenced both co-regulatory network characteristics (size and tie strength) and relationships between network characteristics, self-regulated learning, and students' perceptions of learning opportunities.Discussion
The present study reinforces the importance of co-regulatory networks for medical students' self-regulated learning during clinical clerkships. Findings imply that supporting development of strong networks aimed at frequent co-regulatory interactions may enhance medical students' self-regulated learning in challenging clinical learning environments. Social network approaches offer promising ways of further understanding and conceptualising self- and co-regulated learning in clinical workplaces.
SUBMITTER: Bransen D
PROVIDER: S-EPMC8733107 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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Project description: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.
| S-EPMC6974523 | biostudies-literature
Project description:BackgroundSelf-regulated learning (SRL) is an important contributing element to the academic success of students. Literature suggests that the understanding of SRL among medical students is obscure as there is still some uncertainty about whether high performing medical students use SRL. This study explored the characteristics of high performing medical students from the SRL perspective to gain a better understanding of the application of SRL for effective learning.MethodsTwenty-one students who scored at the 90th percentile in written knowledge-based assessment consented to participate in this study. Each student wrote a guided reflective journal and subsequently attended a semi-structured interview. Students were prompted to explain the rationales for their answers. The data were then analysed using thematic analysis to identify patterns among these students from the SRL perspective. Two coders analysed the data independently and discussed the codes to reach a consensus.ResultsHigh performing students set goals, made plans, and motivated themselves to achieve the goals. They put consistent efforts into their studies and applied effective learning strategies. They also employed coping mechanisms to deal with challenges. High performing students regularly evaluated their performance and adopted new strategies.ConclusionsThis study reported that high performing students applied SRL and described the rationales of practice. Medical schools could design SRL-driven interventions to enhance the learning experiences of medical students. Recommendations are made for students on how to apply SRL.
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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.
| S-EPMC10464390 | biostudies-literature
Project description:Self-regulated learning is the individual's ability to effectively use various strategies to reach their learning goals. We conducted this scoping review to explore what has been found regarding self-regulated learning in the clinical environment and how this was measured.Using Arksey and O'Malley's five-stage framework, we searched three medical and educational databases as well as Google Scholar for literature on the self-regulated learning of medical students in the clinical environment published between 1966 and February 2017. After results were screened and relevant studies were identified, the data was summarised and discursively reported.The search resulted in 911 articles, with 14 articles included in the scoping review after the inclusion criteria was applied. Self-regulated learning was explored in these studies in various ways including qualitative, quantitative and mixed methods. Three major findings were found: 1) levels of self-regulated learning change in the clinical environment, 2) self-regulated learning is associated with academic achievement, success in clinical skills and mental health and 3) various factors can support self-regulated learning levels in medical students.Most of articles exploring the self-regulated learning of medical students during the clinical years have been published in the last 5 years, suggesting a growing interest in the area. Future research could explore the self-regulated learning levels of medical students during the clinical years using a longitudinal approach or through the use of novel qualitative approaches.
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Project description:A burgeoning area of research is using social network analysis to investigate college students' substance use behaviors. However, little research has incorporated students' perceived peer drinking norms into these analyses. The present study investigated the association between social network characteristics, alcohol use, and alcohol-related consequences among first-year college students (N = 1,342; 81% of the first-year class) at one university. The moderating role of descriptive norms was also examined. Network characteristics and descriptive norms were derived from participants' nominations of up to 10 other students who were important to them; individual network characteristics included popularity (indegree), network expansiveness (outdegree), relationship reciprocity, and network density. Descriptive norms were defined as participants' average perceived binge drinking frequency among their nominated peers. Network autocorrelation models revealed that indegree and descriptive norms were positively associated with participants' average number of drinks per week, binge drinking frequency, and alcohol-related consequences. Indegree and outdegree interacted with descriptive norms, such that when participants perceived less frequent binge drinking among their peers, outdegree was associated with less alcohol consumption but not consequences. When participants perceived more frequent binge drinking among their peers, indegree and outdegree were associated with more alcohol consumption but not consequences. The present results suggest that being popular and believing that heavy episodic drinking is normative among one's peers are associated with greater alcohol risk. Further, alcohol risks associated with nominating more peers may be enhanced or lessened depending on students' peer drinking norms. Implications for future research and interventions are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
| S-EPMC6296870 | biostudies-literature
Project description:Expression profiles of 22 reference Arabidopsis immunity mutants were collected using the Arabidopsis Pathoarray 464_001 (GPL3638) in order to build a network model predicting the Arabidopsis immune signaling network. Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. Here, we demonstrate that use of mRNA profiling to collect and analyze detailed descriptions of changes in the network state resulting from specific network perturbations is a powerful and economical strategy to elucidate regulatory relationships among the components of a complex signaling network. Specifically, we studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with the Pto DC3000 AvrRpt2 and used as detailed descriptions of the network states resulting from specific genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting network model accurately predicted 22 of 23 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; (ii) negative regulatory relationships are common between signaling sectors. One case of a novel negative regulatory relationship, between the early microbe-associated molecular pattern (MAMP)-activated sector and the salicylic acid (SA)-mediated sector, was further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector-switching" network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness. Keywords: Responses of reference Arabidopsis immunity mutants to Pseudomonas syringae pv. tomato DC3000 carrying avrRpt2 This experiment consists of two (group00) or three (group01-04) biological replicates of each genotype (total 25 genotypes [3 are multiple mutants, which were removed for the network modeling, but were used for normalization]). For each genotype, two leaves per plant were pooled from three pots to prepare total RNA.
2010-07-22 | E-GEOD-19663 | biostudies-arrayexpress
Project description:IntroductionSelf-regulated learning is one of the influencing factors in successful learning. There is a need to understand the factors influencing self-regulated learning in undergraduate medical students in implementing a student-centred learning curriculum. This scoping review aims to systematically identify and map undergraduate medical students' self-regulated learning during student-centred learning implementation and to learn the tools used to measure it.Methods and analysisThe scoping review will use Arksey and O'Malley's proposed five-stage framework guideline. A search for published literature between 2012 and 2022 in the five electronic databases of EBSCOHost, Science Direct, Scopus, ProQuest and PubMed will be conducted using keywords to identify relevant studies. Articles will be limited to the English language. Mendeley software will eliminate duplicates, and results will be exported into abstracts for two stages of the screening process: title and abstract reviews and full-text reviews. The selection process of eligible literature will be reported using the Preferred Reporting Items of Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist recommendations. Studies using qualitative and quantitative data from all world regions will be considered. To be included, studies should examine medical students' self-regulated learning in a student-centred learning approach curriculum. A data chart will capture relevant information from all the included studies. As a scoping review, no assessment of the studies' quality and risk of bias will be made. Results will be presented in tabular form and a narrative report.Ethics and disseminationNo Ethics Committee or Institutional Board approval is required. The findings of this study will be disseminated through peer-reviewed publications, conference presentations and professional networks.
| S-EPMC10582992 | biostudies-literature
Project description:IntroductionThe Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite this, many healthcare workers do not have a seasonal influenza vaccination. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. We examine the effects of social networks on influenza vaccination decision and disease dynamics.MethodsWe used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students' beliefs about vaccination behaviours. We then developed a model which simulated influenza outbreaks to study the effects of preferentially vaccinating individuals within this network.ResultsOf the 253 eligible students, 217 (86%) provided relational data, and 65% of responders had received a seasonal influenza vaccination. Students who were vaccinated were more likely to think other medical students were vaccinated. However, there was no clustering of vaccinated individuals within the medical student social network. The influenza simulation model demonstrated that vaccination of well-connected individuals may have a disproportional effect on disease dynamics.ConclusionsThis medical student population exhibited vaccination coverage levels similar to those seen in other healthcare groups but below recommendations. However, in this population, a lack of vaccination clustering might provide natural protection from influenza outbreaks. An individual student's perception of the vaccination coverage amongst their peers appears to correlate with their own decision to vaccinate, but the directionality of this relationship is not clear. When looking at the spread of disease within a population it is important to include social structures alongside vaccination data. Social networks influence disease epidemiology and vaccination campaigns designed with information from social networks could be a future target for policy makers.
| S-EPMC4599893 | biostudies-literature
Project description:Expression profiles of 22 reference Arabidopsis immunity mutants were collected using the Arabidopsis Pathoarray 464_001 (GPL3638) in order to build a network model predicting the Arabidopsis immune signaling network. Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. Here, we demonstrate that use of mRNA profiling to collect and analyze detailed descriptions of changes in the network state resulting from specific network perturbations is a powerful and economical strategy to elucidate regulatory relationships among the components of a complex signaling network. Specifically, we studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with the Pto DC3000 AvrRpt2 and used as detailed descriptions of the network states resulting from specific genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting network model accurately predicted 22 of 23 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; (ii) negative regulatory relationships are common between signaling sectors. One case of a novel negative regulatory relationship, between the early microbe-associated molecular pattern (MAMP)-activated sector and the salicylic acid (SA)-mediated sector, was further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a "sector-switching" network, which effectively balances two apparently conflicting demands, robustness against pathogenic perturbations and moderation of negative impacts of immune responses on plant fitness. Keywords: Responses of reference Arabidopsis immunity mutants to Pseudomonas syringae pv. tomato DC3000 carrying avrRpt2
2010-07-22 | GSE19663 | GEO