Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning systems to identify which students are at risk and intervene to support better outcomes. In K-12, it has become common practice to re-build and validate these detectors, district-by-district, due to different data semantics and risk factors for students in different districts. As these detectors become more widely used, however, it becomes desirable to also apply detectors in school districts without sufficient historical data to build a district-specific model. Novel approaches that can address the complex data challenges a new district presents are critical for extending the benefits of early warning systems to all students. Using an ensemble-based algorithm, we evaluate a model averaging approach that can generate a useful model for previously-unseen districts. During the ensembling process, our approach builds models for districts that have a significant amount of historical records and integrates them through averaging. We then use these models to generate predictions for districts suffering from high data missingness. Using this approach, we are able to predict student-at-risk status effectively for unseen districts, across a range of grade ranges, and achieve prediction goodness comparable to previously published models predicting at-risk. [For the full proceedings, see ED599096.]