Program overview The MSc is an interdisciplinary, research-focused master’s run by the Munich Center of the Learning Sciences (MCLS). It combines core knowledge and methods from psychology and educational science with a clear emphasis on learning, human development, and empirical research. The curriculum centers on topics such as human development, cognition and instruction, motivation and emotion, and computational approaches including machine learning, AI, and modelling of cognition.
Interdisciplinary research culture The programme brings together researchers and methods from multiple fields—psychology, educational science, neuroscience, biology, mathematics, medicine, and computer science—to tackle complex, real-world questions about learning (for example systemic issues like educational drop-out rates). MCLS maintains a collaboration network of more than 30 universities internationally recognised for work in the learning sciences, which strengthens research opportunities and exchange.
Teaching, training and student support Designed for a selective group of motivated students, the MSc emphasizes state-of-the-art, empirical and quantitative research training, plus transferable skills relevant to the learning sciences. Courses are delivered mainly as seminars, small-group classes and colloquia rather than large lectures, and use active learning methods such as problem-based learning and peer-to-peer teaching to encourage discussion and close interaction with instructors. Incoming students receive orientation through a “Welcome Week,” and additional course-specific tutoring (e.g., statistics) and academic advising are provided by the programme coordinator, instructors, and guest professors.
Key facts and practical notes
Program structure and focus
This MSc is built from 13 study modules: six compulsory modules that establish core knowledge and skills, plus a set of elective project and elective-perspective modules that let you specialise. Core aims are to train you in contemporary theories of learning and development, rigorous empirical research and diagnostics, applied assessment, and the practical and professional skills needed to translate research into real-world settings (including a supervised internship and an independent master’s thesis).
Core (compulsory) modules
Elective project specialisations
Students specialise by taking two of the elective project modules. Each elective project involves conducting an empirical research project (literature review, study planning and design, data collection and analysis, and written and oral reporting):
Elective-perspective modules
Additional elective modules (each 6 ECTS) let you broaden your perspective on education and practice:
Key learning outcomes (what you will be able to do)
Concise requirements
This MSc programme uses a two-step admissions procedure. First, your documents and prior coursework/experience are reviewed to determine eligibility. Applicants who pass this initial assessment are then invited to a selection interview as the second step.
The document review looks for a relevant undergraduate degree plus demonstrated prior knowledge in three specific areas (learning sciences; scientific research methods & statistics; and academic skills). For each area the programme specifies minimum ECTS credits and also recommends higher credit totals to improve your chances of admission. A short test of scientific understanding and reasoning, a motivation letter, and an up-to-date CV are also required during the first stage.
If your application proceeds past the document/test stage, you will take part in the selection interview, which completes the eligibility procedure.
Requirements (document review / first stage)
Second stage
Winter Semester (International)
31 May 2026
Winter Semester (EU/EEA)
1 March 2026
Graduates are prepared for research and development roles in academia, educational research institutes, and R&D departments of ed-tech companies where skills in empirical methods, assessment, and computational modelling are valued. The programme’s emphasis on interdisciplinary methods and AI applications also suits positions in learning analytics, instructional design, and technology-enhanced learning teams.
The degree provides a solid foundation for doctoral studies in learning sciences, cognitive science, educational psychology or related fields, and for professional careers in policy advising, educational consulting, and specialised diagnostic or intervention services in educational and clinical settings.
Carl von Ossietzky University of Oldenburg — Oldenburg
International Psychoanalytic University Berlin — Berlin
SRH University — Heidelberg
Leipzig University — Leipzig