Computer Science Education (CSEd) researchers have studied early warning systems to identify students in need of support, primarily in introductory CS (CS1) courses. These studies vary widely in the data features used to predict student struggle and course performance, as well as the evaluation of said features through statistical and machine learning models. This can make it difficult for CS instructors outside the research community to interpret and apply these findings in their own classes. In addition to this, instructors, especially those teaching introductory courses, may struggle to provide personalized feedback while adapting to large class sizes. For my dissertation, I will build and evaluate an instructor-facing dashboard that combines statistical findings from the CSEd community with human-centered design and data visualization techniques. Utilizing a large dataset capturing 30 course sections and over 1,100 students, I plan to compare data features from different works, using my findings to inform what insights to include in a new instructor-facing dashboard. Harnessing methods from the Human-Computer Interaction domain, such as semi-structured interviews and usability studies, I will work with current CS1 instructors across multiple universities to create a tool that addresses their needs, ensuring that the dashboard is applicable to many courses and institutions. Expected contributions of my work include the identification and evaluation of features for identifying student struggle, the analysis of how these features can be presented to CS1 instructors, and the design and evaluation of instructor-facing tools for student support.