Everything You Need to Know About CS Education: Open Results from a Survey of More Than 18,000 Participants

2025-08-07

AI Summary

Computer science education is increasingly diverse, yet existing research predominantly examines isolated dimensions, lacking a globally representative, multi-theme, and reproducible empirical foundation. Method: We conducted the largest open-source international survey to date—encompassing 173 countries and 18,032 learners—to systematically investigate learning pathways, motivations, challenges, and emerging trends including AI tool adoption and IDE-integrated learning. Employing a structured questionnaire, descriptive statistics, and classification analysis, we integrated multimodal data on educational formats, technology adoption, and learning barriers across an international sample. All data and analysis code are publicly archived. Contribution/Results: The study identifies persistent global challenges—such as inequitable resource access and insufficient hands-on practice—as well as novel paradigms of technology integration in learning. It delivers a high-fidelity, open benchmark dataset that advances methodological transparency, reproducibility, and evidence-informed policy development in CS education research.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Humans and AI: Other Foundations of Human Computation & AI
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

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.

Problem

• Understand diverse aspects influencing CS education paths
• Update insights on emerging trends like AI in learning
• Explore challenges and formats in modern CS education

Innovation

• Large-scale survey with 18,032 diverse participants
• Open dataset covering multiple CS education topics
• Explores AI usage and emerging learning formats