The course is designed to prepare students for writing their master's thesis. After agreeing on a topic with their supervisor beforehand, students will develop that topic into a concept for a master's thesis. During the seminar, students will gain insights into the social scientific research process, project (self-)management, scientific work, the development of adequate research questions, the structuring of a scientific thesis, and how to link theory with methodology. Students will then develop a proposal for their thesis, which they will present and open up to peer-feedback.

Upon completing the course, students will be able to:

• conceptualize a master's thesis,
• manage their thesis workflow,
• link theory with methodology,
• develop an adequate problem statement and research questions,
• open up their concept to peer-feedback.

As with all PI courses, attendance is a requirement to pass this course. As a special concession, you may miss up to 2 meetings of your LV. However, if you miss a third, you will automatically fail the course. This condition applies whatever the reasons for your absences may have been.

The course is designed as a colloquium that will consist of three parts. In the first part, students will get (re-)introduced to scientific work and project management related to writing a master's thesis. The second part is devoted to  students presenting their project ideas and opening them up to peer-feedback.

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The Master's Seminar is a PI class, which means that you need to satisfy the attendance requirement mentioned above (see 'Regelung zur Anwesenheit'). Assessment and your final score are made up of three elements: 

• Proposal for a master's thesis (50%)
• Presentation of the master's proposal (30%)
• Prticipation in class, which involves providing peer-feedback on the projects of fellow students (20%)

Grade scale:

Less than 60 percent: 5
60 and above: 4
70 and above: 3
80 and above: 2
90 and above: 1

A note on plagiarism and AI-generated content

Please note that the work you submit in this class has to be your own work, it has to comply with WU policies regarding plagiarism, cheating and collusion, and it may be subject to potential plagiarism checks. Using AI-generated content in work that you submit for assessment is strongly discouraged. AI-generated content here includes written output that is produced through the use of AI tools such as ChatGPT, Gemini, Grammarly or DeepL.

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

You can talk to your instructor Professor Ursula Lutzky immediately before or after class, or contact her by email (ursula.lutzky@wu.ac.at ).