Addressing 21st century challenges, professionals competent in science, technology, engineering, and mathematics (STEM) will be indispensable. A stronger individualisation of STEM learning environments is commonly considered a means to help more students develop the envisioned level of competence. However, research suggests that career aspirations are not only dependent on competence but also on STEM identity development. STEM identity development is relevant for all students, but particularly relevant for already under-served students. Focusing solely on the development of competence in the individualisation of STEM learning environments is not only harming the goal of educating enough professionals competent in STEM, but may also create further discrimination against those students already under-served in STEM education. One contemporary approach for individualisation of learning environments is learning analytics. Learning analytics are known to come with the threat of the reproduction of historically grown inequalities. In the research field, responsible learning analytics were introduced to navigate between potentials and threats. In this paper, we propose a theoretical framework that expands responsible learning analytics by the context of STEM identity development with a focus on under-served students. We discuss two major issues and deduce six suppositions aimed at guiding the use of as well as future research on the use of learning analytics in STEM education. Our work can inform political decision making on how to regulate learning analytics in STEM education to help providing a fair chance for the development of STEM identities for all students.
