# SPARK: Real-Time Monitoring of Multi-Faceted Programming Exercises

arXiv: https://arxiv.org/abs/2601.22256 · HTML: https://mlsanity.com/paper/2601.22256/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2026.

## Built on by (most cited first, 5 of 5)

- [Comparing Human Oversight Strategies for Computer-Use Agents](https://mlsanity.com/paper/2604.04918/) (2026)
- [Exploring LLM Agent Designs and Interaction Modalities for Scientific Visualization](https://mlsanity.com/paper/2604.27996/) (2026)
- [HiLSVA: Design and Evaluation of a Human-in-the-Loop Agentic System for Scientific Visualization](https://mlsanity.com/paper/2606.26614/) (2026)
- [AI-Mediated Feedback Improves Student Revisions: A Randomized Trial with FeedbackWriter in a Large Undergraduate Course](https://mlsanity.com/paper/2602.16820/) (2026)
- [Stayin' Aligned Over Time: Towards Longitudinal Human-LLM Alignment via Contextual Reflection and Privacy-Preserving Behavioral Data](https://mlsanity.com/paper/2605.04029/) (2026)

## Abstract (arXiv)

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 N=22 learners solving two web programming exercises and provide empirical insights into the perceived usefulness of SPARK through a within-subjects evaluation with 16 programming instructors.