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Yang Shi

Assistant Professor

Utah State University

United States

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About

Yang Shi is an Assistant Professor at North Carolina State University in the United States. His research interests focus on the knowledge component attribution problem in programming, specifically exploring methods and tradeoffs associated with limited labeled data. Yang's recent publications contribute to advancing understanding in this area.

Articles (11)

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2026

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2026

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2026

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2026

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2026

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2026

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2026

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2026

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2025

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2024

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2022

Collaborators (9)

Steven Moore

Assistant Professor

George Mason University

UNITED STATES

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Xiaoyi Tian

Assistant Professor

Kennesaw State University

UNITED STATES

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Renkai Ma

Assistant Professor

University of Cincinnati

UNITED STATES

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Dragan Gasevic

Chair Professor

The University of Hong Kong

HONG KONG

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Paulo Carvalho

Assistant Professor

Carnegie Mellon University

UNITED STATES

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Yan Chen

Assistant Professor

Virginia Tech

UNITED STATES

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Qiao Jin

Assistant Professor

North Carolina State University

UNITED STATES

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Afsaneh Razi

Assistant Professor

Drexel University

UNITED STATES

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Yubo Kou

Assistant Professor

Pennsylvania State University

UNITED STATES

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Yang Shi

Research Interests

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About

Articles (11)

Growing Up (and Old) with AI: Co-Constructing the Future for Family-Centered AI

KDD 2026 Special Day on AI for Education

10th Educational Data Mining in Computer Science Education (CSEDM) Workshop

Designing and Evaluating Next-Generation Learning Interfaces: Linking AI, HCI, and the Learning Sciences

When AI Gets It Wrong: Scaffolding AI Hallucination Detection for Children Through Chatbot Creation

CodeFlow: LLM-Generated Flowchart Feedback for Programming Students

Examining Students' Code Comprehension with LLMs in Block- and Text-Based Programming

Investigating High School Students' Code Comprehension and Strategy Use Across Block-Based and Text-Based Programming

Integrating Expert Knowledge With Automated Knowledge Component Extraction for Student Modeling

The Knowledge Component Attribution Problem for Programming: Methods and Tradeoffs with Limited Labeled Data

Developing Comic-based Learning Toolkits for Teaching Computing to Elementary School Learners

Collaborators (9)

Steven Moore

Xiaoyi Tian

Renkai Ma

Dragan Gasevic

Paulo Carvalho

Yan Chen

Qiao Jin

Afsaneh Razi

Yubo Kou

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