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Neil C. C. Brown; Pierre Weill-Tessier; Juho Leinonen; Paul Denny; Michael Kölling – ACM Transactions on Computing Education, 2025
Motivation: Students learning to program often reach states where they are stuck and can make no forward progress--but this may be outside the classroom where no instructor is available to help. In this situation, an automatically generated next-step hint can help them make forward progress and support their learning. It is important to know what…
Descriptors: Artificial Intelligence, Programming, Novices, Technology Uses in Education
Haldeman, Georgiana; Babes-Vroman Monica; Tjang, Andrew; Nguyen, Thu D. – ACM Transactions on Computing Education, 2021
Autograding systems are being increasingly deployed to meet the challenges of teaching programming at scale. Studies show that formative feedback can greatly help novices learn programming. This work extends an autograder, enabling it to provide formative feedback on programming assignment submissions. Our methodology starts with the design of a…
Descriptors: Student Evaluation, Feedback (Response), Grading, Automation
Vrieler, Tina; Salminen-Karlsson, Minna – ACM Transactions on Computing Education, 2022
The aim of this conceptual article is to provide a framework and a lens for educators in diversifying and making CS education more inclusive. In this article, we conceptualize the notion of computer science capital (CSC), which extends Bourdieu's sociological theory of capital and Archer et al.'s work on "science capital." The CSC…
Descriptors: Sociocultural Patterns, Computer Science Education, Teaching Methods, Inclusion

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