In scientific projects in academic contexts, the research I participate in generally revolves around learning, knowledge-creation, innovation, and curricular design in higher education or adult learning, as well as educational technologies and human-technology interaction. I am usually involved from the very beginning (e.g. research planning phase or writing up a project proposal for external grants/funding) to the end (e.g. project end and final reports, publication of scientific articles). However, I am often also just involved in the data analysis phase. In such projects, I generally am the "data and methods person". This may include coming up with an empirical research design and methodology, conducting the empirical research, and analysing and interpreting the data.

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The methods I use depend on the phenomenon that is of interest to the project, but generally involve qualitative (e.g. open interviews in ethnographic or grounded theory research settings), quantitive (survey/questionnaire construction and its statistical analysis), or data science methods (e.g. social media sentiment analysis). Whenever feasibly and adequate, I combine these methods ("mixed methods" approach) to be able to approach, analyse, and understand a specific phenomenon on different levels of abstraction: from "small-scale" and in-depth qualitative analysis to "large-scale" or "big data" analysis via e.g. natural language processing and sentiment analysis models, as well as all kinds of statistical methods and models "in-between". This has the advantage of approaching a topic from the theory/hypothesis-generating and the theory/hypothesis-testing side.