How good is generative AI at passing exams? What does this tell us about how we could design better assessments? Join us on Monday 4th December at 2pm GMT (UTC) to discuss a paper on this by Joyce Mahon, Brian Mac Namee and Brett Becker at University College Dublin published at UKICER earlier this year.

All the world’s a stage, and all the men and women merely players; They have their exits and their entrances. And one teacher in their time plays many parts. As students watch academic actors enter and exit their lecture theatres on University campuses around the world, what role can drama play in their teaching and learning? How can theatre and storytelling facilitate students understanding of whatever is they are supposed to be learning?

Why do some students achieve more than others? Students goals, their belief in their ability to reach those goals and their prior experience are key factors. But how do they interplay? Join us for our monthly ACM SIGCSE journal club meetup on Zoom to discuss a prize-winning paper [1] on this topic by Hannu Pesonen, Juho Leinonen, Lassi Haaranen and Arto Hellas from Aalto University in Finland and the University of Auckland.

Microcredentials are mini-qualifications that allow learners to provide evidence of their broader skills alongside their traditional academic awards. How can these awards be integrated into existing educational qualifications?

What is the most dangerous course to teach in Computing? Join us on Monday 7th August at 2pm BST (UTC+1) to discuss an opinion piece by Tony Clear from Auckland University of Technology on this very subject. Tony argues that introductory programming (aka CS1) is the most dangerous course for educators to teach. Do you agree with him?

A good theory can be the most concentrated form of knowledge. By encapsulating an infinite number of cases, a theory can make predictions rather than just describing a finite number of disjointed facts. So how does theory feature in research about assessment and feedback? Join us on Monday 3rd July at 2pm BST (UTC+1) to discuss a paper investigating this question by Juuso Henrik Nieminen, Margaret Bearman &

The textbook has long been a mainstay of education. Although online textbooks can give students easy (and sometimes free) access to increasingly interactive resources, authors have a bewildering array of tools and publishing models to select from.

Programming is hard, or at least it used to be. AI code generators like Amazon’s CodeWhisperer, DeepMind’s AlphaCode, GitHub’s CoPilot, Replit’s Ghostwriter and many others now make programming easier, at least for some people, some of the time. What opportunities and challenges do these new tools present for educators?

Maybe you wrote that code and maybe you didn’t. If AI helped you, such as the OpenAI Codex in GitHub Copilot, how did it solve your problem? How much did Artificial Intelligence help or hinder your solution? Join us to discuss a paper by Michel Wermelinger from the Open University published in the SIGCSE technical symposium earlier this month on this very topic.

It’s all very well getting an AI to write your code for you but neither writing code or reading code are the same as understanding code. So what is going on in novices brains when they learn to actually understand the code they are reading and writing?

More and more software development tools are available in the cloud, with tools like Replit, CodingRooms, GitHub Codespaces, Amazon Web Services Cloud9, JetBrains and Eclipse all offering online tools for developers to code collaboratively in the cloud. Integrated Development Environments (IDEs) which have traditionally been available as “fatter” clients are increasingly available as “thinner” web-based clients running in a browser.