跨学科合作网络中的混合模式：通过多个镜头评估跨学科性
Mixing Patterns in Interdisciplinary Collaboration Networks: Assessing Interdisciplinarity Through Multiple Lenses
摘要：
跨学科研究合作存在固有的挑战，例如弥合认知差距，平衡交易成本与合作利益。这就提出了一个问题：跨学科研究是否必然导致跨学科合作？本研究的目的是探讨这个问题，并评估在跨学科研究的个人，二元和团队层面的合作偏好，通过研究混合模式的合作网络。使用一个由教育领域人工智能的2,000多名研究人员组成的网络，我们发现“跨学科性”是由单个研究人员的不同研究经验所证明的，而不是合作中研究人员之间的多样性。我们还研究组间混合应用一种新的方法来分类的积极和非积极的研究人员在合作网络的基础上参与多个团队。我们发现一个显着的差异，积极和非积极的研究人员集群之间的学习成绩和经验的指标，这表明组间混合作为学术成功的一个关键因素。我们的研究结果揭示了跨学科研究中团队形成的本质，并强调了跨学科项目的重要性。
英文摘要：
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.
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