At the start of the semester, something for all educators seeking guidance on using AI in their courses: At the Otto-Friedrich-Universität Bamberg, we have developed an AI policy generator that helps educators create clear guidelines for students.
I already use AI tools quite extensively in my teaching – from creating exercises to automating feedback. This works well, but I observe that many colleagues either completely avoid the topic or act without a clear strategy and rules.
This is exactly where our generator comes in: It offers about 50 predefined text modules that can be combined into a …
Read More →Yesterday in the Inf-Einf-B Study and Code Space: A student asks about “identifying characteristics” of sorting algorithms. Clearly formulated, exam-oriented.
This encounter highlighted a core dilemma in computer science education. We often reduce complex algorithms to retrievable facts: Bubble Sort - linear runtime in the best case, quadratic in the worst case. Correct, but superficial.
This situation reveals a fundamental problem in computer science education. We package complex algorithms into boxes. We reduce them to bullet points and runtime classes. Students memorize these. …
Read More →7-hour learning marathon with 30 students, 4 tutors, and 1 professor – but was the first “Inf-Einf-B Study and Code Space” worth it? The exam will tell. 🧐
What fascinates me: The enormous range of questions. One moment I’m helping an international student debug their Coleman-Liau Readability Index in VS Code using a mix of German and English, the next I’m showing how to switch between windows on the exam laptops using Alt+Tab and how to select text using Shift and arrow keys. It’s incredible how different the digital skills are among computer science students!
The …
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