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

Tong Wu, Xiaohang Tang, Sam Wong, Xi Chen, Clifford A. Shaffer, Yan Chen

TL;DR

This paper investigates how group discussion and group formation affect student performance in a large CS1 course. Using an 11-session experiment with two grouping methods (random and expertise-balanced) and explicit expert presence criteria, the authors analyze 255 groups and 788 participating students, collecting chat and code submissions via VizPI. They find that active discussions do not consistently improve outcomes for struggling students, and the mere presence of expert peers does not guarantee gains; grouping method shows no robust advantage. The results emphasize the context-dependent nature of collaborative coding, highlight challenges in scaling expert-guided group work, and suggest focusing on interaction quality and targeted scaffolding to maximize learning gains.

Abstract

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.

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

TL;DR

This paper investigates how group discussion and group formation affect student performance in a large CS1 course. Using an 11-session experiment with two grouping methods (random and expertise-balanced) and explicit expert presence criteria, the authors analyze 255 groups and 788 participating students, collecting chat and code submissions via VizPI. They find that active discussions do not consistently improve outcomes for struggling students, and the mere presence of expert peers does not guarantee gains; grouping method shows no robust advantage. The results emphasize the context-dependent nature of collaborative coding, highlight challenges in scaling expert-guided group work, and suggest focusing on interaction quality and targeted scaffolding to maximize learning gains.

Abstract

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.
Paper Structure (23 sections, 6 figures, 1 table)

This paper contains 23 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Experiment Setup
  4. CS1 Class
  5. In-class Exercises
  6. Data Collection System
  7. Grouping Mechanisms
  8. Results & lessons learned
  9. Basic Stats & Data Cleaning
  10. Group and Discussion Definitions
  11. Impact of Active and Relevant Group Discussions on Students' Coding Performance
  12. Active vs. Inactive Groups
  13. Relevant vs. Irrelevant Discussions
  14. Implications of Findings and Lessons Learned
  15. The effectiveness of different grouping methods (random vs. expertise-balanced) on discussion activity and student coding performance
  16. ...and 8 more sections

Figures (6)

  • Figure 1: High-level experimental workflow
  • Figure 2: Instructor and student UI of VizPI
  • Figure 3: Groups activity and improvement
  • Figure 4: Active and improvement
  • Figure 5: Relevant and improvement
  • ...and 1 more figures