Laboratory mambers gave a lecture on network analysis at the School of Applied Data Analysis at RUDN University
From May 28 to 30, Peoples' Friendship University of Russia (RUDN University) hosted an intensive course for the new season of the all-Russian project "School of Applied Data Analysis". Three team members from the International Laboratory of Applied Network Research participated in the event, delivering a lecture entitled "Network Analysis in Education" as part of the school.
About the event
Experts from the University Consortium of Big Data Researchers and partner companies (Cyberia, Antiplagiat, Megaputer) trained participants in collecting, storing, and processing data for educational, scientific, and business purposes. The event was timed to coincide with the IV International University Award in Artificial Intelligence and Big Data, "Gravitation," the final ceremony of which took place on May 29.
On the participation of ANR-Lab team
Our laboratory actively participates in events organized by the University Consortium of Big Data Researchers and the School of Applied Data Analysis. We strive to promote network analysis methodology in the Russian-speaking community of researchers and practitioners. This time, our lecture focused on the application of network analysis to the study of science and education in the broadest context. This methodology can be used to study the structure and dynamics within individual groups (learning communities, classes, teams) and at the organizational level (academic productivity, institutional development). I believe this lecture successfully highlighted the diversity of research project opportunities and research questions that can be addressed using network analysis.
Our lecture highlighted the potential of network analysis for education. We presented various projects and research by our lab's staff and friends. It was interesting to see the audience's interest in the methodology, receiving numerous questions and sharing resources.
At the school, I presented on the use of network analysis for labor market research and career trajectory mapping. All participants expressed interest in network analysis methodology and asked questions about the principles of network visualization. This was particularly valuable to me, as audience engagement helps us better understand which aspects of network analysis are most in demand in labor market research.
For further information
Based on the materials from this lecture, we have prepared a collection of recordings from our research seminars on the application of network analysis in educational research.
- The Structure of Research Interaction between "Leading" and "Advancing" Universities: Similarities and Differences (Natalia Matveeva, HSE University)
How is research collaboration organized within universities? Which structures facilitate academic development, and which do not? Using three groups of universities (leading mature universities, leading young universities, and advancing universities) as examples, this article examines how co-authorship networks influence knowledge sharing and team resilience – video.
- Networks and Emotions: How Do We Infect Each Other with Happiness, Anger, and Loneliness? (Anton Sizov, HSE University)
A review of research on emotional contagion, from outbursts of anger on social media to the long-term impact of happiness and loneliness on social connections. Anton also shares the results of his longitudinal network study of loneliness in student communities – video
- Application of network analysis to construct career trajectories (Daniil Kovalev, HSE)
The Laboratory team is actively developing network-based approaches to labor market research together with the RosNavyk platform. This video discusses how network analysis helps model career trajectories (recorded presentation for the University Consortium of Big Data Researchers) – video
- A study of the relationship between social media activity and academic performance of students at a technical university (Vladislav Sych, Bauman Moscow State Technical University)
How are students' grades related to their activity on VKontakte? The author identifies typical academic performance trajectories using machine learning methods, identifies marker communities, and demonstrates that digital behavior helps more accurately predict academic performance – video
- Patterns of Classmate Preference Structuring in Ethnically Mixed Classes (Artem Oganyan and Asya Karimova)
Using a school in Tajikistan, this article examines how ethnicity and civic/ethnic identity influence friends and the preference to study with classmates – video
We thank our colleagues from the University Consortium of Big Data Researchers and RUDN University for the invitation and warm welcome!
