Browsing by Author "Wong, Sam"

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    The Impact of Group Discussion and Formation on Student Performance: An Experience Report in a Large CS1 Course
    Wu, Tong; Tang, Xiaohang; Wong, Sam; Chen, Xi; Shaffer, Clifford A.; Chen, Yan (ACM, 2025-02-12)
    Programming instructors often conduct collaborative learning activities, such as Peer Instruction (PI), to enhance student motivation, engagement, and learning gains. However, the impact of group discussion and formation mechanisms on student performance remains unclear. To investigate this, we conducted an 11- session experiment in a large, in-person CS1 course. We employed both random and expertise-balanced grouping methods to examine the efficacy of different group mechanisms and the impact of expert students’ presence on collaborative learning. Our observations revealed complex dynamics within the collaborative learning environment. Among 255 groups, 146 actively engaged in discussions, with 96 of these groups demonstrating improvement for poor-performing students. Interestingly, our analysis revealed that different grouping methods (expertise-balanced or random) did not significantly influence discussion engagement or poor-performing students’ improvement. In our deeper qualitative analysis, we found that struggling students often derived benefits from interactions with expert peers, but this positive effect was not consistent across all groups.We identified challenges that expert students face in peer instruction interactions, highlighting the complexity of leveraging expertise within group discussions.
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    VizGroup: An AI-assisted Event-driven System for Collaborative Programming Learning Analytics
    Tang, Xiaohang; Wong, Sam; Pu, Kevin; Chen, Xi; Yang, Yalong; Chen, Yan (ACM, 2024-10-13)
    Programming instructors often conduct collaborative learning activities, like Peer Instruction, to foster a deeper understanding in students and enhance their engagement with learning. These activities, however, may not always yield productive outcomes due to the diversity of student mental models and their ineffective collaboration. In this work, we introduce VizGroup, an AI-assisted system that enables programming instructors to easily oversee students’ real-time collaborative learning behaviors during large programming courses. VizGroup leverages Large Language Models (LLMs) to recommend event specifications for instructors so that they can simultaneously track and receive alerts about key correlation patterns between various collaboration metrics and ongoing coding tasks. We evaluated VizGroup with 12 instructors in a comparison study using a dataset collected from a Peer Instruction activity that was conducted in a large programming lecture. The results showed that VizGroup helped instructors effectively overview, narrow down, and track nuances throughout students’ behaviors.
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    VizPI: A Real-Time Visualization Tool for Enhancing Peer Instruction in Large-Scale Programming Lectures
    Tang, Xiaohang; Chen, Xi; Wong, Sam; Chen, Yan (ACM, 2023-10-29)
    Peer instruction (PI) has shown significant potential in facilitating student engagement and collaborative learning. However, the implementation of PI for large-scale programming lectures has proven challenging due to difficulties in monitoring student engagement, discussion topics, and code changes. This paper introduces VizPI, an interactive web tool that enables instructors to conduct, monitor, and assess PI for programming exercises in real-time. With features that visualize the progress of student discussions and code submissions, VizPI allows for more effective oversight of PI activities and the provision of personalized feedback at scale. Our work aims to transform the pedagogical approach to PI in programming education, making it more engaging and adaptable to student needs.