Document Type : Letter to Editor
Authors
- EHSAN TOOFANINEJAD 1
- MASOMEH KALNTARION 2
- ZEINAB HOSSEINI MOTLAGH 2
- SEYYED KAZEM BANIHASHEM 3, 4
- OMID NOROOZI 5
1 Department of eLearning in Medical Sciences, School of Medical Education and Learning Technologies, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2 Department of Medical Education, School of Medical Education and Learning Technologies, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3 Education and Learning Sciences, Wageningen University and Research, Wageningen, Netherlands Department of Online Learning and Instruction, Open Universiteit, Heerlen, The Netherlands
4 Department of Online Learning and Instruction, Open Universiteit, Heerlen, Netherlands
5 Education and Learning Sciences, Wageningen University and Research, Wageningen, Netherlands
Abstract
The medical education scenery is continually changing, calling for innovative ways to support learning processes and outcomes. This letter is devoted to introducing the potential of social network analysis (SNA) as an efficient educational innovation to support medical education. Through the investigation of the connections and interactions within a network, SNA brings to light complex social dynamics that can scaffold learning processes and outcomes in medical education. In this letter, we explain the role of SNA in fostering collaboration, engagement, knowledge sharing, and professionalized education among medical students and educators. We also talk about the role of node analysis (individuals) and edge analysis (relationships) to determine influential structures, knowledge brokers, and blocks in collaboration. We discuss that personalized insights gained from SNA can guide educators to provide timely and tailored interventions. In addition, SNA can reveal hidden curricula and informal networks of learning, and evaluate teamwork and communication skills efficiently. The letter also outlines how SNA can be used to deal with students’ loneliness in social learning settings and professional fatigue through the early establishment of supportive networks and peer mentoring occasions. Backed up with evidence from the relevant research, this letter accentuates the positive impact of SNA on medical education by capitalizing on the formation of interpersonal relationships and collaboration. We conclude that by gaining a deep insight into the social connection dynamics, educators can support the knowledge exchange and support the learning processes and outcomes of medical students and future health professionals.
Highlights
Dear Editor
The ever-evolving landscape of medicine demands innovative approaches to medical education. Social Network Analysis (SNA) emerges as a promising tool to optimize collaboration, engagement, knowledge sharing, and professional development among medical students. SNA examines connections and interactions within a network ( 1 ). By treating the interactions among medical students and educators as a dynamic learning network, SNA offers a significant potential to support learning processes and outcomes.
Through analyzing nodes (individuals) and edges (connections), SNA reveals interaction patterns and information flow. In the context of medical education, nodes represent medical students, educators, healthcare professionals, and other medical education stakeholders. On the other hand, edges represent connections among the nodes, which can range from friendships to study group collaborations or knowledge exchange. These connections shape social and professional interactions, impacting learning, teamwork, and overall skill development. SNA creates pathways for collaboration, mentorship, and support, fostering a culture of continuous learning and growth ( 2 ).
A key strength of SNA is its ability to identify influential individuals, knowledge brokers, and potential barriers to collaboration ( 3 ). Educators can leverage this insight to design targeted interventions that optimize learning environments and enhance teamwork and collaboration ( 4 ). For instance, SNA can reveal hidden study groups or identify isolated students. This information can be used to foster collaboration among isolated students or connect them with mentors within the network. This personalized approach allows educators to move beyond a one-size-fits-all model and tailor learning experiences to the specific needs of individuals arising from social connections.
SNA can also be utilized to analyze the information flow and knowledge exchange patterns among students. By mapping connections, it can reveal who is central to knowledge dissemination, which groups are well-connected, and where there might be gaps in sharing critical medical knowledge ( 5 ). Furthermore, SNA holds promise for addressing social isolation and professional burnout by assessing the strength and density of social connections. This assessment allows educators to proactively intervene and create supportive networks and peer mentoring opportunities ( 2 ). While SNA offers benefits to medical education, its implementation faces certain challenges, such as the complexity of social networks, the need for specialized software and analytical skills, as well as potential ethical concerns about data privacy and confidentiality ( 6 , 7 ). These challenges can be addressed through targeted faculty development, adoption of user-friendly SNA tools, and implementation of robust data governance policies to ensure ethical data usage and protect student privacy.
In conclusion, SNA is a powerful tool for optimizing medical education by leveraging the dynamics of interpersonal relationships and collaboration. By understanding and harnessing the potential of SNA, educators can foster supportive networks, facilitate knowledge exchange, and ultimately enhance the overall learning experiences for medical students and future healthcare professionals.
Authors’ Contributions
All authors contributed to the discussion, read and approved the manuscript, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Conflict of interest
The authors declare that they have no conflicts of interest.None.
References
- Dado M, Bodemer D. A review of methodological applications of social network analysis in computer-supported collaborative learning. Educational Research Review. 2017; 22:159-80.
- Isba R, Woolf K, Hanneman R. Social network analysis in medical education. Med Educ. 2017; 51(1):81-8.
- Cvitanovic C, Cunningham R, Dowd AM, Howden SM, Van Putten EI. Using social network analysis to monitor and assess the effectiveness of knowledge brokers at connecting scientists and decision‐makers: An Australian case study. Environmental Policy and Governance. 2017; 27(3):256-69.
- Kantek F, Yesilbas H, Yildirim N, Dundar Kavakli B. Social network analysis: Understanding nurses' advice-seeking interactions. Int Nurs Rev. 2023; 70(3):322-8.
- Grunspan DZ, Wiggins BL, Goodreau SM. Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research. CBE Life Sci Educ. 2014; 13(2):167-79.
- Steinert Y, Fontes K, Mortaz-Hejri S, Quaiattini A, Nooraie RY. Social Network Analysis in Undergraduate and Postgraduate Medical Education: A Scoping Review. Acad Med. 2023; 16:10-97.
- Isba R, Woolf K, Hanneman R. Social network analysis in medical education. Med Educ. 2017; 51(1):81-8.
)