Data-driven teaching in STEM subjects

The digital transformation offers diverse opportunities for school and higher education. Although the integration of digital technologies in mathematics and science teaching as a teaching and learning medium already has a long tradition, there is a lack of studies, especially in the STEM field, that embed data-supported learning analytics methods in subject didactic concepts, evaluate their didactic opportunities and limitations, and at the same time consider the aspects of data protection and fairness.

The aim of the interdisciplinary doctoral programme LernMINT is to explore the opportunities, limitations and risks of data-supported teaching and learning: What opportunities does the digital transformation offer for increasing quality and efficiency in schools and universities in the area of tension between subject didactics, learning analytics and data protection? Potentials for the use of data- and computer-supported, intelligent methods from the fields of learning analytics and educational data mining in school teaching and in the transition to higher education, which have been largely unused up to now, are to be developed and used to support teachers and learning. The focus of the interdisciplinary doctoral programme will be on the targeted development and subject-didactic evaluation of data-supported, intelligent methods and their meaningful inclusion in the teaching of STEM subjects.