Master thesis seminar 2-IKV-921
The purpose of this interdisciplinary course is to provide support to students writing their master thesis in the fields of cognitive science. Students are expected to develop the concept for their master’s thesis, present and discuss their plans for their theses, issues and problems. The course provides room both for presentations and discussions. It also includes participation in the MEi:CogSci conference.
Learning outcomes: After the course, you should be able to: (1) to formulate and follow a scientific question relevant to cognitive science, (2) to plan, conduct, document and present scientific work, (3) to write an extended scientific abstract, (4) to defend your research and constructively deal with critical commentary, (5) to constructively participate in a peer-review process, (6) to get involved in collaborative work in physical and virtual environments, (7) to engage in scientific discourse, (8) to communicate your expertise in order to contribute constructive criticism to the work of others.
- We start on Wednesday 21.2.2018 with an organizational meeting.
|21.2.||Introduction to the course, requirements and grading, plan for the semester.|
|28.2.||Presentations of your mobility projects to 1st year students (joint meeting).|
|07.3.||How to prepare your master thesis. Master Thesis Concept requirement.|
|15.3.||Interdisciplinarity - requirement for your master thesis.|
|25.4.||no seminar (due to students' scientific conference)|
|09.5.||MEi:CogSci conference (abstract, review). Team session on interdisciplinarity.|
|XX.6.||Rehersal of conference talks|
Course requirements and grading
- 40% - quality of the master thesis concept (following the template provided)
- 30% - oral presentations of your thesis in the seminar (shorter and longer)
- 20% - activity during the semester, peer feedback, participation in peer reviews of extended abstracts (for the conference)
- 10% - active participation at MEi:CogSci conference (talk)
- Overall score: A > 90%, B > 80%, C > 70%, D > 60%, E > 50%, else Fx.