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Browsing by Author "Zhang, Ashley"

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    CFlow: Supporting Semantic Flow Analysis of Students' Code in Programming Problems at Scale
    Zhang, Ashley; Tang, Xiaohang; Oney, Steve; Chen, Yan (ACM, 2024-07-09)
    Introductory programming courses have been growing rapidly, now enrolling hundreds or thousands of students. 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 𝑚𝑛 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.
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    VizProg: Identifying Misunderstandings by Visualizing Students' Coding Progress
    Zhang, Ashley; Chen, Yan; Oney, Steve (ACM, 2023-04-19)
    Programming instructors often conduct in-class exercises to help them identify students that are falling behind and surface students’ misconceptions. However, as we found in interviews with programming instructors, monitoring students’ progress during exercises is difficult, particularly for large classes. We present VizProg, a system that allows instructors to monitor and inspect students’ coding progress in real-time during in-class exercises. VizProg represents students’ statuses as a 2D Euclidean spatial map that encodes the students’ problem-solving approaches and progress in real-time. VizProg allows instructors to navigate the temporal and structural evolution of students’ code, understand relationships between code, and determine when to provide feedback. A comparison experiment showed that VizProg helped to identify more students’ problems than a baseline system. VizProg also provides richer and more comprehensive information for identifying important student behavior. By managing students’ activities at scale, this work presents a new paradigm for improving the quality of live learning.