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Students' Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming
The rapid evolution of artificial intelligence (AI), specifically large language models (LLMs), has opened opportunities for various educational applications. This paper explored the feasibility of utilizing ChatGPT, one of the most popular LLMs, for
Externí odkaz:
http://arxiv.org/abs/2312.11567
Data-driven programming feedback systems can help novices to program in the absence of a human tutor. Prior evaluations showed that these systems improve learning in terms of test scores, or task completion efficiency. However, crucial aspects which
Externí odkaz:
http://arxiv.org/abs/2208.05326
Publikováno v:
Western Journal of Emergency Medicine, Vol 12, Iss 3, Pp 354-356 (2011)
Recent advances in the understanding of elder mistreatment have demonstrated that financial exploitation tends to be one of the most common forms of mistreatment affecting older populations. Agencies such as the World Bank and World Health Organizati
Externí odkaz:
https://doaj.org/article/be08605e778244f1ac682bc8dd53d94a
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) m
Externí odkaz:
http://arxiv.org/abs/2206.03545
Autor:
Daubert, Melissa A., Stebbins, Amanda, Peragallo-Urrutia, Rachel, Chiswell, Karen, Loop, Matthew S., Harding, Ceshae, Price, Thomas, Wang, Tracy Y.
Publikováno v:
In American Heart Journal July 2024 273:130-139
Programming environments such as Snap, Scratch, and Processing engage learners by allowing them to create programming artifacts such as apps and games, with visual and interactive output. Learning programming with such a media-focused context has bee
Externí odkaz:
http://arxiv.org/abs/2104.11812
Autor:
Wang, Wengran, Kwatra, Archit, Skripchuk, James, Gomes, Neeloy, Milliken, Alexandra, Martens, Chris, Barnes, Tiffany, Price, Thomas
Open-ended programming increases students' motivation by allowing them to solve authentic problems and connect programming to their own interests. However, such open-ended projects are also challenging, as they often encourage students to explore new
Externí odkaz:
http://arxiv.org/abs/2104.11806
Understanding students' misconceptions is important for effective teaching and assessment. However, discovering such misconceptions manually can be time-consuming and laborious. Automated misconception discovery can address these challenges by highli
Externí odkaz:
http://arxiv.org/abs/2103.04448
Instructors have limited time and resources to help struggling students, and these resources should be directed to the students who most need them. To address this, researchers have constructed models that can predict students' final course performan
Externí odkaz:
http://arxiv.org/abs/2102.05765
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