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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
Smitha S. Kumar; Michael A. Lones; Manuel Maarek; Hind Zantout – ACM Transactions on Computing Education, 2025
Programming demands a variety of cognitive skills, and mastering these competencies is essential for success in computer science education. The importance of formative feedback is well acknowledged in programming education, and thus, a diverse range of techniques has been proposed to generate and enhance formative feedback for programming…
Descriptors: Automation, Computer Science Education, Programming, Feedback (Response)
Vesin, Boban; Mangaroska, Katerina; Akhuseyinoglu, Kamil; Giannakos, Michail – ACM Transactions on Computing Education, 2022
Online learning systems should support students preparedness for professional practice by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students' development and engagement with programming is a challenging process. Early career computer…
Descriptors: Adaptive Testing, Online Courses, Programming, Computer Science Education
Lagus, Jarkko; Longi, Krista; Klami, Arto; Hellas, Arto – ACM Transactions on Computing Education, 2018
The computing education research literature contains a wide variety of methods that can be used to identify students who are either at risk of failing their studies or who could benefit from additional challenges. Many of these are based on machine-learning models that learn to make predictions based on previously observed data. However, in…
Descriptors: Computer Science Education, Transfer of Training, Programming, Educational Objectives
Fiebrink, Rebecca – ACM Transactions on Computing Education, 2019
This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education,…
Descriptors: Artificial Intelligence, Man Machine Systems, Population Groups, Creativity
Russell, Ingrid; Markov, Zdravko; Neller, Todd; Coleman, Susan – ACM Transactions on Computing Education, 2010
Our approach to teaching introductory artificial intelligence (AI) unifies its diverse core topics through a theme of machine learning, and emphasizes how AI relates more broadly with computer science. Our work, funded by a grant from the National Science Foundation, involves the development, implementation, and testing of a suite of projects that…
Descriptors: Artificial Intelligence, Program Effectiveness, Computer Science, Teaching Methods

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