arXiv subjects

Ashley Ge Zhang

Publications and source records attributed to Ashley Ge Zhang.

3 recordsLinked to original sources

arXiv2026

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

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.

cs.HC↗

arXiv2026

Editrail: Understanding AI Usage by Visualizing Student-AI Interaction in Code

Programming instructors have diverse philosophies about integrating generative AI into their classes. Some encourage students to use AI, while others restrict or forbid it. Regardless of their approach, all instructors benefit from understanding how their students actually use AI while writing code. Such insight helps instructors assess whether AI use aligns with their pedagogical goals, enables timely intervention when they find unproductive usage patterns, and establishes effective policies for AI use. However, our survey with programming instructors found that many instructors lack visibility into how students use AI in their code-writing processes. To address this challenge, we introduce Editrail, an interactive system that enables instructors to track students' AI usage, create personalized assessments, and provide timely interventions, all within the workflow of monitoring coding histories. We found that Editrail enables instructors to detect AI use that conflicts with pedagogical goals accurately and to determine when and which students require intervention.

cs.HC↗

arXiv2024

CFlow: Supporting Semantic Flow Analysis of Students' Code in Programming Problems at Scale

The high demand for computer science education has led to high enrollments, with thousands of students in many introductory courses. In such large courses, it can be overwhelmingly difficult for instructors to understand class-wide problem-solving patterns or issues, which is crucial for improving instruction and addressing important pedagogical challenges. In this paper, we propose a technique and system, CFlow, for creating understandable and navigable representations of code at scale. CFlow is able to represent thousands of code samples in a visualization that resembles a single code sample. CFlow creates scalable code representations by (1) clustering individual statements with similar semantic purposes, (2) presenting clustered statements in a way that maintains semantic relationships between statements, (3) representing the correctness of different variations as a histogram, and (4) allowing users to navigate through solutions interactively using semantic filters. With a multi-level view design, users can navigate high-level patterns, and low-level implementations. This is in contrast to prior tools that either limit their focus on isolated statements (and thus discard the surrounding context of those statements) or cluster entire code samples (which can lead to large numbers of clusters -- for example, if there are n code features and m implementations of each, there can be m^n clusters). We evaluated the effectiveness of CFlow with a comparison study, found participants using CFlow spent only half the time identifying mistakes and recalled twice as many desired patterns from over 6,000 submissions.

cs.HC↗