The course introduces theory and methods of special relevance for research about sustainable energy. As a course participant, you are thereby prepared for your degree project, according to the requirements of KTH Royal Institute of Technology, and for a future career full of possibilities in research and development:
Research starts with an understanding of the research subject, followed by a clear definition of research questions and methods to address these. Of importance to the research process is also sources of data, and to treat and analyse data and results. Whether qualitative or quantitative, experimental or theoretical methods are used, scientific research requires systematic treatment of data and methods so that the research results can be validated and knowledge be built. An important starting point is that new knowledge is based on previous knowledge in a perpetual process.
The intended learning outcomes of the course are about how you can problematise in a defined field, and from that retrieve relevant research questions and formulate objectives for your research. As a support to the process, the course trains you to carry out a literature survey with high quality that also becomes a part of the research process. This is connected to learning outcomes 1 and 2.
Furthermore, the course focuses on critical assessment of methods -- you should learn to analyse and evaluate the research process as a whole and carefully consider alternative methods in relation to the objectives and research questions that are treated. Likewise, at the end of the course, you should be able to discuss and propose how both sustainable development and ethical dimensions should be handled in relation to a specific research assignment. This is also reflected in the intended learning outcomes 3 and 4 of the course.
Furthermore, we practice critical assessment of results so that you after completing the course can evaluate and analyse research results, your own or that of others, and assess their relevance and weaknesses. Data and result analysis, interpretation and evaluation as well as analysis of statistical relevance is therefore also included in the course. Here, e.g. regression analysis and correlations is applied, as well as statistical evaluations such as covariance and standard deviation, and this is assessed according to intended learning outcome 5.