To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where students learn fundamental engineering concepts and apply them to an innovation project with the goal of producing value outsidethe classroom. The model has been fairly successful, but questions still remain about how to best support students and instructors in open-ended innovation spaces. To answer these questions, learning analytics and educational data mining (LA/EDM) techniques were used to better understand student innovation in IBL settings. LA/EDM is a growing field with the goal of collecting and interpreting large amounts of educational data to support student learning. In this work, five LA/EDM algorithms and tools were developed: 1) the IBL framework which groups student actions into illustrative categories specific to innovation environments, 2) a classifier model that automatically groups student text into the categories of the framework, 3) classifier models that leverage the IBL framework to predict student success, 4) clustering models that group students with similar behavior, and 5) epistemic network analysis models that summarize temporal student behavior. For each of the five algorithms/tools, the design, development, assessment, and resulting implications are presented. Together, the results paint a picture of the affordances and challenges of teaching and learning innovation. The main insights gained are how language and temporal behavior provide meaningful information about students’ learning and innovation processes, the unique challenges that result from incorporating open-ended innovation into the classroom, and the impact of using LA/EDM tools to overcome these challenges.