How to conceptualise self-regulated learning: Implications for measurement

Should we add patients in concordance of judgment learning tool panels? - An analysis between patients and primary care physicians

INTRODUCTION

The Concordance of Judgment Learning Tool (CJLT) has been developed for distance asynchronous learning of professionalism in health sciences education. The learning of professionalism is induced by a student's comparison of their own responses with those of the panel members. Whilst CJLT programs typically include same profession experts in their panels, we believe that they could also include patients. Accordingly, we conducted a study aimed at comparing CJLT response patterns between two groups of primary care physicians (PCPs) and patients.

METHODS

We conducted a mixed prospective study of responses to a CJLT program based on a group of PCPs and a group of patients: an analysis of the response patterns of the two groups and a qualitative analysis of justifications.

RESULTS

A total of 110 participants were included in the study: 70 patients and 40 PCPs. We found a significant difference in response patterns between the PCP and patient groups for nine of the fifteen questions (60%). The qualitative analysis of justifications between groups allowed us to comprehend patients' views on the professionalism of PCPs.

CONCLUSIONS

Including patients in CJLT panels can enrich the feedback offered to students in these online training programs.

How medical students co-regulate their learning in clinical clerkships: a social network study

BACKGROUND

Self-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.

METHODS

In 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.

RESULTS

Students 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.

CONCLUSIONS

Results 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.

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

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.

Putting self-regulated learning in context: Integrating self-, co-, and socially shared regulation of learning

Processes involved in the regulation of learning have been researched for decades, because of its impact on academic and workplace performance. In fact, self-regulated learning is the focus of countless studies in health professions education and higher education in general. While we will always need competent individuals who are able to regulate their own learning, developments in healthcare require a shift from a focus on the individual to the collective: collaboration within and between healthcare teams is at the heart of high-quality patient care. Concepts of collaborative learning and collective competence challenge commonly held conceptualisations of regulatory learning and call for a focus on the social embeddedness of regulatory learning and processes regulating the learning of the collective. Therefore, this article questions the alignment of current conceptualisations of regulation of learning with demands for collaboration in current healthcare. We explore different conceptualisations of regulation of learning (self-, co-, and socially shared regulation of learning), and elaborate on how the integration of these conceptualisations adds to our understanding of regulatory learning in healthcare settings. Building on these insights, we furthermore suggest ways forward for research and educational practice.