Pulla: A Parsons Problem Tool for Fine-Grained Behavioral Tracing and Instructor-Facing Problem-Solving Analysis

2026-09-14

AI Summary

为解决帕森斯问题工具对解题过程可见性不足的问题，本文介绍了一种名为Pulla的新工具，通过收集细粒度的交互数据来分析学生解题行为模式。

Abstract

Existing Parsons problem tools primarily focus on correctness, indicating whether a student solved a problem, but providing limited visibility into the underlying problem-solving process. We address this gap by introducing Pulla, a Parsons problem tool that instruments programming assignments to capture fine-grained interaction data. These behavioral traces allow the system to surface recurring difficulty patterns, giving instructors actionable insights to inform targeted intervention decisions. This paper describes our experience in developing and deploying Pulla. We deployed the tool in two university courses: an upper-division software design course at the University of Houston (United States) and an introductory programming course at Aalto University (Finland). By analyzing the data collected, we identified common difficulty patterns, including misidentifying exception types, confusing return with the throw/raise mechanism, and incorrect control-flow ordering.

Problem

• Parsons problem
• fine-grained behavioral tracing
• instructor-facing analysis
• difficulty patterns
• programming assignments

Innovation

• fine-grained interaction data
• behavioral tracing
• problem-solving analysis
• targeted intervention