Code comprehension has been shown to be challenging and important for a positive learning outcome. Students don't always understand the code they write. This has been exacerbated by the advent of large language models that automatically generate code that may or may not be correct. Now students don't just have to understand their own code, but they have to be able to critically analyze automatically generated code as well. To help students with code comprehension, instructors often use notional machines. Notional machines are used not only by instructors to explain code, but also in activities or exam questions given to students. Traditionally, these questions involve code that was not written by students. However, asking questions to students about their own code (Questions on Learners' Code, QLCs) has been shown to strengthen their code comprehension. This poster presents an approach to combine notional machines and QLCs to automatically generate personalized questions about learners' code based on notional machines. Our aim is to understand whether notional machine-based QLCs are effective. We conducted a pilot study with 67 students to test our approach, and we plan to conduct a comprehensive empirical evaluation to study its effectiveness.

Programming is increasingly being taught in high schools and in non-CS majors at universities. However, the interests of this wide population are often different from those of students who decided to study computer science. Programming graphics is a great way to cater to these interests and motivate students to learn programming with examples that go beyond the more traditional "Hello World" or mathematical exercises. In this workshop, we demonstrate how to use PyTamaro, a Python library designed to teach programming with graphics to novices. PyTamaro's design removes the need to use and explain sophisticated programming language features and directs learners' attention towards fundamental concepts of programming. The workshop combines unplugged activities and Python programming. Graphics are first created using colored paper cutouts and cards that represent PyTamaro functions; then, the composition is written as a Python program. The programming part will be carried out on the PyTamaro web platform, which also contains more than a hundred activities of varying levels of difficulty and on different themes that instructors can directly use or take inspiration from.

Knowledge about misconceptions is an important element of pedagogical content knowledge. The computing education research community collected a large body of research on misconceptions, using a diverse set of definitions and approaches. Inspired by this prior work, we present an actionable definition of misconceptions, focused on the area most commonly studied: programming and programming languages. We then introduce an organizational structure for collections of programming language misconceptions. We study how existing collections fit our organization, and we present a curated inventory of programming language misconceptions that aims to follow our definition and structure. Our inventory goes beyond traditional programming misconception collections. It connects misconceptions to the authoritative specifications of languages, to places they may be triggered in textbooks, to research papers that discuss them, and it provides support for integrating programming language misconceptions into educational platforms.