For this seminar I prepared by reading the material, choosing two different papers to analyze as well as attending all the lectures.
The first lecture was by professor Haibo Li about design research. Not too much of the lecture was donned to theory behind design research, but had a more practical approach to design research without really touching on why and when design research is a good choice. Instead he focused on the practicalities and with an approach more related to entrepreneurship than research.
The second lecture that we had was more informative on the subject, but also a little harder to grasp since the lecturer hadn’t had time to prepare something. However I felt that the lecture was quite illuminating on the subject. Before the lecture I saw potential in prototyping for research but gained a deeper understanding at the lecture. The lecturer put a big emphasis on design research being design to shape a prototype to search for answers. That we shape a controlled condition that we can then investigate. And in design research it is the process that is the empirical data.
The lecturer also pointed to something I find very interesting, that design in research is a very powerful tool because it allows you to create new scenarios and alternative behaviors that we can observe and interact with. Another interesting thing he dwelled on was the difference between design in general and in research. Here he proposed that we need analysis to make it into research in order to gain knowledge, and that we use design to analyze problems. But in order to do so we need to be fully aware if why we design and not lose track of the specific answer we’re looking for.
Yet another thing he brought up which I feel is very important is the common misconception that it isn’t real research if you try the prototype yourself, but that you need other people to try it. However that is not true, using it yourself can of course lead to new insights. It’s also related to what we’ve said in previous seminars about sample population, they don’t have to be random in every way, they need to be relevant for the study. So if they have attributes that may mess up answers, they should be excluded. In the same way you as a tester can be relevant and bring new insights when using your own prototype.