Education Research

Automated feedback generation plays a crucial role in enhancing personalized learning experiences in computer science education. Among different types of feedback, next-step hint feedback is particularly important, as it provides students with actionable steps to progress towards solving programming tasks. This study investigates how students interact with an AI-driven next-step hint system in an in-IDE learning environment. We gathered and analyzed a dataset from 34 students solving Kotlin tasks, containing detailed hint interaction logs. We applied process mining techniques and identified 16 common interaction scenarios. Semi-structured interviews with 6 students revealed strategies for managing unhelpful hints, such as adapting partial hints or modifying code to generate variations of the same hint. These findings, combined with our publicly available dataset, offer valuable opportunities for future research and provide key insights into student behavior, helping improve hint design for enhanced learning support.

Computer science education is a dynamic field with many aspects that influence the learner's path. While these aspects are usually studied in depth separately, it is also important to carry out broader large-scale studies that touch on many topics, because they allow us to put different results into each other's perspective. Past large-scale surveys have provided valuable insights, however, the emergence of new trends (e.g., AI), new learning formats (e.g., in-IDE learning), and the increasing learner diversity highlight the need for an updated comprehensive study. To address this, we conducted a survey with 18,032 learners from 173 countries, ensuring diverse representation and exploring a wide range of topics --- formal education, learning formats, AI usage, challenges, motivation, and more. This paper introduces the results of this survey as an open dataset, describes our methodology and the survey questions, and highlights, as a motivating example, three possible research directions within this data: challenges in learning, emerging formats, and insights into the in-IDE format. The dataset aims to support further research and foster advancements in computer education.

Engaging students with effective learning materials continues to be a significant challenge in programming education. Analogies are commonly used to simplify complex topics, enabling learners to relate unfamiliar concepts to familiar ones. Additionally, visual representations of these analogies can enhance engagement and improve the overall learning experience. This work presents a prototype of a novel AI tool that generates analogy-based explanations and corresponding video animations for programming education. The tool leverages Large Language Models (LLMs) for analogy generation and a structured animation workflow for visualization. This poster invites discussion on the effectiveness of AI-generated educational content and its implications for programming education.

GenAI is playing an increasingly important role in computing courses at all levels, offering new opportunities to support teaching and learning. However, using GenAI effectively raises important concerns regarding trust, academic integrity, and broader social and ethical dimensions. This Working Group was formed to report on the current state of the art in using GenAI in upper-level computing courses to aid educators. The working group will undertake a methodological review of published work and solicit input from the computing educational community as part of the report.