  There are inherent challenges to interdisciplinary research collaboration,
such as bridging cognitive gaps and balancing transaction costs with
collaborative benefits. This raises the question: Does interdisciplinary
research necessarily result in interdisciplinary collaborations? This study
aims to explore this question and assess collaboration preferences in
interdisciplinary research at the individual, dyadic, and team level by
examining mixing patterns in a collaboration network. Using a network of over
2,000 researchers from the field of artificial intelligence in education, we
find that "interdisciplinarity" is demonstrated by diverse research experiences
of individual researchers rather than diversity among researchers within
collaborations. We also examine intergroup mixing by applying a novel approach
to classify the active and non-active researchers in the collaboration network
based on participation in multiple teams. We find a significant difference in
indicators of academic performance and experience between the clusters of
active and non-active researchers, suggesting intergroup mixing as a key factor
in academic success. Our results shed light on the nature of team formation in
interdisciplinary research, as well as highlight the importance of
interdisciplinary programs.
