用可解释的知识组件指标分析编程作业的难度
Analyzing the Difficulty of Programming Assignments with Interpretable Knowledge Component Metrics
AI总结：
研究如何用知识组件（KC）理解编程入门课程作业难度与学生学习困境，分析基于KC的指标，如作业KC数量及连续作业间KC覆盖变化，揭示相关规律并提出分析框架。
摘要：
本文研究知识组件（KCs）如何作为可解释信号，理解编程入门课程作业难度和学生困境。此前基于编程行为的预测模型难解释，本文分析基于KC的指标，如作业KC数量、连续作业间KC覆盖变化，通过数据集揭示规律，提出分析框架，为教师和课程设计者提供实用方法。
英文摘要：
This research paper examines how Knowledge Components (KCs) - fine-grained concepts or skills required to solve programming tasks - can be used as interpretable signals for understanding assignment difficulty and student struggle in introductory programming courses. While prior work has focused on predictive models based on programming behavior, such models are often difficult to interpret and therefore hard to use for instructional decisions. We analyze KC-based metrics, including the number of KCs per assignment and changes in KC coverage between consecutive assignments. We examine correlations between the number of KCs and student performance on the assignment, and analyze changes in KCs across assignments to identify cases where performance declines without new concepts being introduced. Selected assignments are then qualitatively inspected to understand potential design issues. Our results on data from three introductory programming course datasets show that assignments involving more KCs are generally associated with lower performance, and that sudden shifts in required KCs can coincide with disruptions in learning progression. We also identify assignments where performance declines even though no new KCs are introduced, suggesting potential issues in task design or instruction, which could be examined with qualitative analysis. We propose an interpretable framework for analyzing programming assignments using KC-based metrics, with practical implications for instructors and course designers who want to better understand where and why students struggle, and how course materials might be improved. Our method can use KCs defined by an expert or extracted by an LLM, offering instructors an additional way to assess assignment quality beyond average correctness. It can be applied to any course with ordered assignments and measurable performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
