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pro vyhledávání: '"Hou, Xinying"'
Autor:
Padiyath, Aadarsh, Hou, Xinying, Pang, Amy, Vargas, Diego Viramontes, Gu, Xingjian, Nelson-Fromm, Tamara, Wu, Zihan, Guzdial, Mark, Ericson, Barbara
The capability of large language models (LLMs) to generate, debug, and explain code has sparked the interest of researchers and educators in undergraduate programming, with many anticipating their transformative potential in programming education. Ho
Externí odkaz:
http://arxiv.org/abs/2406.06451
The field of Artificial Intelligence in Education (AIED) focuses on the intersection of technology, education, and psychology, placing a strong emphasis on supporting learners' needs with compassion and understanding. The growing prominence of Large
Externí odkaz:
http://arxiv.org/abs/2405.04645
Recent studies have integrated large language models (LLMs) into diverse educational contexts, including providing adaptive programming hints, a type of feedback focuses on helping students move forward during problem-solving. However, most existing
Externí odkaz:
http://arxiv.org/abs/2404.02213
CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning Programming
Learning to program can be challenging, and providing high-quality and timely support at scale is hard. Generative AI and its products, like ChatGPT, can create a solution for most intro-level programming problems. However, students might use these t
Externí odkaz:
http://arxiv.org/abs/2401.12125
Novice programmers need to write basic code as part of the learning process, but they often face difficulties. To assist struggling students, we recently implemented personalized Parsons problems, which are code puzzles where students arrange blocks
Externí odkaz:
http://arxiv.org/abs/2401.03144
Autor:
Kazemitabaar, Majeed, Hou, Xinying, Henley, Austin, Ericson, Barbara J., Weintrop, David, Grossman, Tovi
As Large Language Models (LLMs) gain in popularity, it is important to understand how novice programmers use them. We present a thematic analysis of 33 learners, aged 10-17, independently learning Python through 45 code-authoring tasks using Codex, a
Externí odkaz:
http://arxiv.org/abs/2309.14049
While open-ended self-explanations have been shown to promote robust learning in multiple studies, they pose significant challenges to automated grading and feedback in technology-enhanced learning, due to the unconstrained nature of the students' in
Externí odkaz:
http://arxiv.org/abs/2306.16639
Publikováno v:
In Water Research 15 June 2024 257
Autor:
Zhang, Xiaoyu, Chen, Dan, Hou, Xinying, Jiang, Na, Li, Yan, Ge, Shijian, Mu, Yang, Shen, Jinyou
Publikováno v:
In Journal of Hazardous Materials 15 October 2023 460
Publikováno v:
In Bioresource Technology October 2023 385