Learning how to design innovatively is a critical process skill for undergraduate engineers in the 21st century. To this end, our paper discusses the development and validation of a Bayesian network decision support tool that can be used by engineering educators to make recommendations that positively impact the innovativeness of product designs. Our Bayesian network model is based on Dym's design process framework and actual design process data collected from 26 undergraduate engineering capstone teams over multiple terms. Cross validation using all available outcomes data and a sensitivity analysis showed our model to be both accurate and robust. Our model, which is based on data from teams that produced both innovative and non-innovative products, can be used to formatively assess the process used by a design team and the level of innovativeness, thereby contributing to more innovative final design outcomes.