As educational data science (EDS) evolves and its related fields continue to advance, it is imperative to employ EDS to solve real-world educational challenges. One such challenge is to research how students learn and study effectively in digital learning environments and apply those findings to better their learning resources. The volume of educational data collected by digital platforms is growing tremendously, so it is a pivotal moment for EDS to be applied with an ethical approach in which the best interests of the learner are kept at the forefront. Learning engineering provides a practice and process to engage in EDS with a student-centered approach. In this work, we exemplify how the learning engineering process (LEP) guided large-scale data analyses to advance learning science (i.e., the doer effect), developed new artificial intelligence (AI)–based learning tools, and scaled both effective learning methods in natural educational contexts and automated data analysis methods—all in the service of students. The examples of analyses in this chapter serve to showcase how EDS—applied as a part of learning engineering—can validate learning science theory and advance the state of the art in learning technology.