collaborators

6 papers

cs.HC2025

Dynamite: Real-Time Debriefing Slide Authoring through AI-Enhanced Multimodal Interaction

Panayu Keelawat, David Barron, Kaushik Narasimhan +5

Facilitating class-wide debriefings after small-group discussions is a common strategy in ethics education. Instructor interviews revealed that effective debriefings should highlig…

cs.HC2025

REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation

Xiaohang Tang, Sam Wong, Zicheng He +2

This paper introduces REVA, a human-AI system that expedites instructor review of voluminous AI-generated programming feedback by sequencing submissions to minimize cognitive conte…

cs.HC2024

SPHERE: Scaling Personalized Feedback in Programming Classrooms with Structured Review of LLM Outputs

Xiaohang Tang, Sam Wong, Marcus Huynh +3

Effective personalized feedback is crucial for learning programming. However, providing personalized, real-time feedback in large programming classrooms poses significant challenge…

cs.HC2024

VizGroup: An AI-Assisted Event-Driven System for Real-Time Collaborative Programming Learning Analytics

Xiaohang Tang, Sam Wong, Kevin Pu +3

Programming instructors often conduct collaborative learning activities, like Peer Instruction, to foster a deeper understanding in students and enhance their engagement with learn…

cs.CY2024

The Impact of Group Discussion and Formation on Student Performance: An Experience Report in a Large CS1 Course

Tong Wu, Xiaohang Tang, Sam Wong +3

Programming instructors often conduct collaborative learning activities, such as Peer Instruction (PI), to enhance student motivation, engagement, and learning gains. However, the…

cs.HC2024

CFlow: Supporting Semantic Flow Analysis of Students' Code in Programming Problems at Scale

Ashley Ge Zhang, Xiaohang Tang, Steve Oney +1

The high demand for computer science education has led to high enrollments, with thousands of students in many introductory courses. In such large courses, it can be overwhelmingly…