融合两种方法，精准诊断学生解题步骤中的错误。
Combining model tracing and constraint-based modeling for multistep strategy diagnoses
- 将模型追踪与约束建模结合，识别连续或合并的解题步骤
- 在2136个解题步骤中实现100%与教师判断一致
- 适合教育技术、智能辅导系统研究者使用
模型追踪和基于约束的建模是诊断分步任务中学生输入的两种方法。模型追踪能识别学生连续采取的解题步骤，而基于约束的建模可在多个步骤合并为一步时仍进行诊断。本文提出一种融合两种范式的方法：将约束定义为学生输入与策略中某一步骤共有的属性，从而在学生偏离策略时（即使合并多步）也能提供诊断。本研究设计并评估了多步策略诊断系统。以一个包含2136个学生解二次方程步骤的现有数据集为例进行验证。为对比人工诊断，两名教师对随机抽样的70个偏离步骤和70个策略应用步骤进行了编码。结果显示，系统诊断与教师编码在全部140个学生步骤上完全一致。
原文摘要 · Abstract (English)
Model tracing and constraint-based modeling are two approaches to diagnose student input in stepwise tasks. Model tracing supports identifying consecutive problem-solving steps taken by a student, whereas constraint-based modeling supports student input diagnosis even when several steps are combined into one step. We propose an approach that merges both paradigms. By defining constraints as properties that a student input has in common with a step of a strategy, it is possible to provide a diagnosis when a student deviates from a strategy even when the student combines several steps. In this study we explore the design of a system for multistep strategy diagnoses, and evaluate these diagnoses. As a proof of concept, we generate diagnoses for an existing dataset containing steps students take when solving quadratic equations (n=2136). To compare with human diagnoses, two teachers coded a random sample of deviations (n=70) and applications of the strategy (n=70). Results show that that the system diagnosis aligned with the teacher coding in all of the 140 student steps.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。