CNS NRT relationship statistics, by author role.

Assessing and evaluating programmatic outcomes of graduate education programs help stakeholders understand and respond to challenges that emerge over the course of a complex academic program. Given the increased complexity of program activities and outcomes, there is a need for semi-automatic, interactive visual analytics tools that transform data into actionable insights to inform decision-making. This paper documents the results of the evaluation planning and annual workflow setup to create dynamic assessment reports for the Complex Networks and Systems NSF Research Traineeship (CNS NRT) program at Indiana University. The CNS NRT evaluation workflow relied on institutional, survey, and publication data and the freely available tools to guide decision making and communicate the achievements of faculty and doctoral trainees who participated in the program between 2017 and 2024. The evaluation data to date show that the program is judged by participants as meeting its goals and that there has been considerable evidence of research productivity as indicated in the publication data. These clear gains in short-term outcomes show that the CNS NRT is on a pathway to achieving the medium and longer-term goals of the project, which will be examined over a longer period.