Dataset Information

Relationships between medical students' co-regulatory network characteristics and self-regulated learning: a social network study.

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 | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Relationships between medical students' co-regulatory network characteristics and self-regulated learning: a social network study.

Bransen Derk D Govaerts Marjan J B MJB Sluijsmans Dominique M A DMA Donkers Jeroen J Van den Bossche Piet G C PGC Driessen Erik W EW

Perspectives on medical education 20210430 1

<h4>Introduction</h4>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.<h4>Methods</h4>The authors surveyed 403 undergraduate medical students during t ...[more]

PMID: 33929685

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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:BackgroundSelf-regulated learning is a key competence to engage in lifelong learning. Research increasingly acknowledges that medical students in clerkships need others to regulate their learning. The concept of "co-regulated learning" captures this act of regulating one's learning by interacting with others. To effectively cultivate such skills in students, we need to increase our understanding of co-regulated learning. This study aimed to identify the purposes for which students in different phases of clinical training engage others in their networks to regulate their learning.MethodsIn this social network study, we administered a questionnaire to 403 medical students during clinical clerkships (65.5% response rate). The questionnaire probed into the composition of students' co-regulatory networks and the purpose for which they engaged others in specified self-regulated learning activities. We calculated the proportion of students that engaged others in their networks for each regulatory activity. Additionally, we conducted ANOVAs to examine whether first-, second-, and third-year students differed in how they used their networks to support self-regulation.ResultsStudents used others within their co-regulatory networks to support a range of self-regulated learning activities. Whom students engaged, and the purpose of engagement, seemed to shift as students progressed through clinical training. Over time, the proportion of students engaging workplace supervisors to discuss learning goals, learning strategies, self-reflections and self-evaluations increased, whereas the proportion of students engaging peers to discuss learning strategies and how to work on learning goals in the workplace decreased. Of all purposes for which students engaged others measured, discussing self-reflections and self-evaluations were consistently among the ones most frequently mentioned.ConclusionsResults reinforce the notion that medical students' regulation of learning is grounded in social interactions within co-regulatory networks students construct during clerkships. Findings elucidate the extent to which students enact self-regulatory learning within their co-regulatory networks and how their co-regulatory learning behaviors develop over time. Explicating the relevance of interactions within co-regulatory networks might help students and supervisors to purposefully engage in meaningful co-regulatory interactions. Additionally, co-regulatory interactions may assist students in regulating their learning in clinical workplaces as well as in honing their self-regulated learning skills.

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.

Project description:BackgroundStudents with developed self-regulated learning (SRL) skills demonstrate an ability to set individualized educational goals, select optimal learning strategies for reaching these goals, and reflect on overall progress. The primary aims of this study were to investigate first-year medical students' perceived utility of a self-regulated learning-informed intervention and to assess the impact of its implementation on students' intended use of SRL throughout medical school.MethodsA two-part educational intervention focused on SRL skill development was carried out at Harvard Medical School during the start of the 2021 academic year. For the first component of the intervention, 169 first-year medical students engaged in an interactive class session structured around SRL concept videos, a brief lecture, small group discussions and individual reflection. Students completed pre- and post-intervention surveys which inquired about learners' current and anticipated application of SRL skills. During the second component of the intervention, 15 first-year medical students participated in a set of one-on-one academic SRL coaching sessions. All coaching participants completed follow-up semi-structured interviews.ResultsA statistically significant increase was observed between students' use of skills in all domains of self-regulated learning prior to the intervention and their anticipated use of these skills following the intervention. Prior to the intervention, 60.1% (n = 92) of students reported utilizing evidence-based learning strategies, compared to 92.8% (n = 142) of students (p < 0.001) who anticipated applying this SRL skills at the completion of the classroom session. Six core themes emerged from qualitative analysis of the post-intervention survey including learning plan development, accountability and progress tracking, goals for growth, engagement through active learning, routine reflection, and adapting to the curriculum.ConclusionsBoth classroom-based learning sessions and one-on-one academic coaching programs are feasible approaches for encouraging the use of self-regulated learning techniques in the preclinical setting.

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.

Project description:IntroductionSelf-regulated learning is a cyclical process of forethought, performance, and self-reflection that has been used as an assessment tool in medical education. No prior studies have evaluated SRL processes for answering multiple-choice questions (MCQs) and most evaluated one or two iterations of a non-MCQ task. SRL assessment during MCQs may elucidate reasons why learners are successful or not on these questions that are encountered repeatedly during medical education.MethodsInternal medicine clerkship students at three institutions participated in a SRL microanalytic protocol that targeted strategic planning, metacognitive monitoring, causal attributions, and adaptive inferences across seven MCQs. Responses were transcribed and coded according to previously published methods for microanalytic protocols.ResultsForty-four students participated. In the forethought phase, students commonly endorsed prioritizing relevant features as their diagnostic strategy (n = 20, 45%) but few mentioned higher-order diagnostic reasoning processes such as integrating clinical information (n = 5, 11%) or comparing/contrasting diagnoses (n = 0, 0%). However, in the performance phase, students' metacognitive processes included high frequencies of integration (n = 38, 86%) and comparing/contrasting (n = 24, 55%). In the self-reflection phase, 93% (n = 41) of students faulted their management reasoning and 84% (n = 37) made negative references to their abilities. Less than 10% (n = 4) of students indicated that they would adapt their diagnostic reasoning process for these questions.DiscussionThis study describes in detail student self-regulatory processes during MCQs. We found that students engaged in higher-order diagnostic reasoning processes but were not explicit about it and seldom reflected critically on these processes after selecting an incorrect answer. Self-reflections focused almost exclusively on management reasoning and negative references to abilities which may decrease self-efficacy. Encouraging students to identify and evaluate diagnostic reasoning processes and make attributions to controllable factors may improve performance.

Dataset Information

Relationships between medical students' co-regulatory network characteristics and self-regulated learning: a social network study.

Introduction

Methods

Results

Discussion

Publications

Relationships between medical students' co-regulatory network characteristics and self-regulated learning: a social network study.

Similar Datasets

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