42 citations · 125 across the 6 of their papers we have counts for
6 papers
Personalized Parsons Puzzles as Scaffolding Enhance Practice Engagement Over Just Showing LLM-Powered Solutions
Xinying Hou, Zihan Wu, Xu Wang +1
As generative AI products could generate code and assist students with programming learning seamlessly, integrating AI into programming education contexts has driven much attention…
Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course
Aadarsh Padiyath, Xinying Hou, Amy Pang +6
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 an…
Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novices
Ruiwei Xiao, Xinying Hou, John Stamper
Recent studies have integrated large language models (LLMs) into diverse educational contexts, including providing adaptive programming hints, a type of feedback focuses on helping…
Integrating Personalized Parsons Problems with Multi-Level Textual Explanations to Scaffold Code Writing
Xinying Hou, Barbara J. Ericson, Xu Wang
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 P…
CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning Programming
Xinying Hou, Zihan Wu, Xu Wang +1
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…
Understanding the Effects of Using Parsons Problems to Scaffold Code Writing for Students with Varying CS Self-Efficacy Levels
Xinying Hou, Barbara J. Ericson, Xu Wang
Introductory programming courses aim to teach students to write code independently. However, transitioning from studying worked examples to generating their own code is often diffi…