Code edit histories can offer instructors valuable insight into students’ problem-solving processes, revealing unproductive behaviors that final code alone cannot capture. For example, a correct solution may contain large copy-and-pasted segments (suggesting the code originated elsewhere) or unguided trial-and-error (suggesting a lack of clear strategy). Timelines are a common way to visualize code histories, but existing timeline visualizations of code or document histories show only when and where edits occurred, not what changed. Without this context, it is difficult to answer key questions about how students invested effort or to infer their intentions. We present CodeStream, a visualization system that augments timelines with situational code annotations, whose granularity and visibility dynamically adapt to scale and interaction state. A comparison study shows that CodeStream enables context-aware navigation of coding histories, supporting fast and accurate pattern identification, and helping instructors reason about students’ coding behaviors and identify who may need intervention.

Monitoring in-class programming exercises can help instructors identify struggling students and common challenges. However, understanding students’ progress can be prohibitively difficult, particularly for multi-faceted problems that include multiple steps with complex interdependencies, have no predictable completion order, or involve evaluation criteria that are difficult to summarize across many students (e.g., exercises building interactive web-based user interfaces). We introduce Spark, a coding exercise monitoring dashboard designed to address these challenges. Spark allows instructors to flexibly group substeps into checkpoints based on exercise requirements, suggests automated tests for these checkpoints, and generates visualizations to track progress across steps. Spark also allows instructors to inspect intermediate outputs, providing deeper insights into solution variations. We also construct a dataset of 40 -minute keystroke coding data from $\mathrm{N}=22$ learners solving two web programming exercises and provide empirical insights into the perceived usefulness of Spark through a within-subjects evaluation with $\mathbf{1 6}$ programming instructors. Index Terms-programming education

Prior research has shown the benefits and promise of allowing instructors in large programming classes to monitor students' coding activity in real-time. However, translating these findings into practical, user-friendly tools remains a challenge. This demonstration showcases VizCode, a tool that allows instructors to monitor students' code in real-time as they edit in the popular Visual Studio Code (VSCode) IDE. VizCode is designed to be practical (integrating with widely-used tools and requiring minimal server overhead), scalable (minimizing network latency by only communicating code changes), and easy to use (requiring minimal setup from students). By focusing on practicality and seamless integration with VSCode, VizCode bridges the gap between research and practice, making it easier for instructors to monitor students' code in real-time.