How Active User Count Relates to Group Outcomes in Collaborative Programming: An Implicit Scripts Perspective

Collaboration scripts structure how learners work together by prescribing roles, tasks, and expected contributions. When scripts are implicit rather than explicit, learners decide how to enact them, which can shift the number of active participants and potentially outcomes. Prior work suggests sequential tasks may not benefit from multiple simultaneous actors due to the misalignment with the implicit script. Building on this, we test how the number of active users relates to time-on-task and group performance. Using panel data from three sequential Jupyter-notebook assignments, we estimate within-group fixed effects models to examine early-stage and aggregated activity signals. We find that active user count is significantly associated with both outcomes, while early signals show a modest correlation with group scores.