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Tian Song; Hang Zhang; Yijia Xiao – IEEE Transactions on Learning Technologies, 2024
High-quality programming projects for education are critically required in teaching. However, it is hard to develop those projects efficiently and artificially constrained by the lecturers' experience and background. The recent popularity of large language models (LLMs) has led to a great number of applications in the field of education, but…
Descriptors: Artificial Intelligence, Education, Intellectual Disciplines, Undergraduate Students
Tomic, Bojan; Kijevcanin, Anisja; Sevarac, Zoran; Jovanovic, Jelena M. – IEEE Transactions on Learning Technologies, 2023
Soft skills (such as communication and collaboration) are rarely addressed in programming courses, mostly because they are difficult to teach, assess, and grade. A quantitative, modular, AI-based approach for assessing and grading students' collaboration has been examined in this article. The pedagogical underpinning of the approach includes a…
Descriptors: Artificial Intelligence, Grading, Cooperation, Students
Auerbach, Joshua E.; Concordel, Alice; Kornatowski, Przemyslaw M.; Floreano, Dario – IEEE Transactions on Learning Technologies, 2019
It has often been found that students appreciate hands-on work, and find that they learn more with courses that include a project than those relying solely on conventional lectures and tests. This type of project driven learning is a key component of "Inquiry-based learning" (IBL), which aims at teaching methodology as well as content by…
Descriptors: Active Learning, Inquiry, Robotics, Artificial Intelligence

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