In-depth understanding of nature of science (NOS) moves beyond knowledge of discrete NOS aspects to consider connections among NOS aspects. This study presents three teacher cases pre to post a nature of science graduate course to explore the potential of an innovative analytic approach that focuses on the connections individual learners make among NOS aspects, using individual epistemic network analysis (iENA). Participants’ pre- and post-course Views of Nature of Science (VNOS) responses were analyzed using both traditional ratings of individual aspects as naïve, transitional, or informed. Then, the responses were coded for NOS connections, which were then modeled using iENA. Compared to traditional VNOS ratings of separate NOS aspects, iENA-based network models added association levels and clustering of NOS aspects. This clarified differences among the participants. Participants’ post-course models showed more and more highly associated NOS aspects and increased clustering of NOS aspects, indicating more in-depth and integrated understandings. Additional improvements were recognized beyond those identified through traditional VNOS analysis for one participant who began the course with mostly informed understandings. Consideration of connections may be crucial to guide more effective interventions.

对自然科学（NOS）的深入理解超出了对NOS各个方面的了解，而不再考虑NOS各个方面之间的联系。这项研究提出了三个教师案例，这些案例在发布自然科学研究生课程之前就已经开始探索一种创新的分析方法的潜力，该方法着重于使用个体认知网络分析（iENA）的个人学习者在NOS方面之间的联系。使用天真的，过渡的或知情的各个方面的传统评级来分析参与者的课程前和课程后的自然科学观（VNOS）响应。然后，将响应编码为NOS连接，然后使用iENA对其进行建模。与单独的NOS方面的传统VNOS等级相比，基于iENA的网络模型增加了NOS方面的关联级别和集群。这澄清了参与者之间的差异。参与者的课程后模型显示出越来越多的NOS方面相关性，并且NOS方面的聚类增加，表明了更深入和更全面的理解。除了通过传统的VNOS分析所确定的那些改进之外，还有其他改进得到了认可，这些改进是在对参与者进行了大部分了解的基础上开始的。考虑联系可能对于指导更有效的干预至关重要。除了通过传统的VNOS分析所确定的那些改进之外，还有其他改进得到了认可，这些改进是在对参与者进行了大部分了解的基础上开始的。考虑联系可能对于指导更有效的干预至关重要。除了通过传统的VNOS分析所确定的那些改进之外，还有其他改进得到了认可，其中一名参与者在开始学习该课程时大多具有充分的了解。考虑联系可能对于指导更有效的干预至关重要。