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Mixing Patterns in Interdisciplinary Co-Authorship Networks at Multiple Scales
1Unit of Human Communication, Development, and Information Sciences, Faculty of Education, The University of Hong Kong, Hong Kong, China.
Scientific Reports
|May 9, 2020
Summary
Interdisciplinary research collaboration thrives on individual diverse experiences, not necessarily diverse teams. Active researchers in collaboration networks show higher academic success, highlighting the importance of interdisciplinary training.
Area of Science:
- Education
- Computer Science
- Artificial Intelligence
Background:
- Interdisciplinary research faces challenges like cognitive gaps and balancing costs/benefits.
- The extent to which collaboration leads to disciplinary diversity is unclear.
Purpose of the Study:
- To explore disciplinary mixing patterns in interdisciplinary research.
- To assess collaborative preferences at individual, dyadic, and team levels.
- To investigate the relationship between intergroup mixing and academic success.
Main Methods:
- Analysis of a coauthor network in artificial intelligence in education.
- Examination of disciplinary mixing patterns at multiple scales (individual, dyadic, team).
- Classification of active vs. non-active researchers based on team participation.
Main Results:
- Disciplinary diversity is primarily reflected in individual researchers' varied experiences.
- Diversity within research pairs or groups is less indicative of overall disciplinary breadth.
- Active researchers demonstrated significantly higher academic performance and experience compared to non-active researchers.
Conclusions:
- Individual research experience diversity is key in interdisciplinary collaboration.
- Intergroup mixing is crucial for academic success in emerging research fields.
- Interdisciplinary training is vital for fostering effective collaboration and success.
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