Collaborative Research: Using Educational Data Mining Techniques to Uncover How and Why Students Learn from Erroneous Examples

在这个研究项目中，卡内基麦罗大学的一个团队和宾夕法尼亚大学的一个团队将使用数据挖掘技术来探索交互式错误示例如何以及为什么会使数学学习者受益。项目团队将根据对数百名使用这些材料学习的学生的日志数据的分析，迭代地改进现有的、基于计算机的教学材料。这项工作将与中学数学学生，特别是六年级的学生一起完成，目标主题将是小数。最近的研究表明，从别人的错误中学习————医学教育中使用的一种主要教学方法，但在其他教育领域不太常用————对帮助学生学习具有相当大的潜力。然而，错误的例子并没有被广泛使用，部分原因是人们还不明白它们是如何以及为什么提供这种好处的。该项目更广泛的影响是，它将提供改进的数学课程，注入错误的例子，并通过经验证据对其有效性进行审查，这些课程可以在全球网络上部署给大量由于对STEM核心概念理解不足而面临数学成绩不佳风险的美国学生。该项目还将提供一个蓝图，可广泛用于其他项目和系统，并得到理论和经验证据的支持，以帮助学生从错误的例子中学习。该项目由EHR核心研究（ECR）项目资助，该项目支持推进STEM学习基础研究文献的工作。该项目将采用迭代研究计划，其中将对在线学习错误示例的日志数据进行数据化和分析。项目团队将从600多名中学数学学生的日志数据开始，这些学生使用错误的例子在在线教学技术和游戏中学习。然后，该团队将在这个为期三年的项目中收集大约2000多名学生的数据，以探索基于修订后的理论对错误示例材料的改进是否能改善学生的学习。实时学习模型将用于了解学生是立即从错误的例子中学习，还是错误的例子会导致未来的学习。项目团队还将研究困惑和其他形式的情感是否会通过使用先前验证过的困惑和其他情感状态的自动检测器来成功地从错误的例子中学习。项目团队还将调查学生与错误示例交互的每个步骤—例如，识别错误，修复错误和解释错误—如何促进学习和积极影响。到项目的第三年结束时，项目团队预计会有一个全面的理论模型，说明错误示例的学习是如何工作的，以及错误示例应该如何更普遍地融入教学。

In this research project, a team from Carnegie Mellow University and a team from the University of Pennsylvania will use data mining techniques to explore how and why interactive erroneous examples can benefit mathematics learners. The project team will iteratively refine existing, computer-based pedagogical materials based on an analysis of log data of hundreds of students who have used those materials to learn. The work will be done with middle school mathematics students, specifically sixth grade students, and the target topic will be decimals. Recent research and studies have indicated that learning from the errors of others -- a prominent pedagogical approach used in medical education, but less commonly used in other areas of education -- has considerable potential to help students learn. Yet, erroneous examples are not used widely, in part because it is not yet understood how and why they provide this benefit. The broader impact of the project is that it will provide improved mathematics lessons, infused with erroneous examples, vetted with empirical evidence of their effectiveness, which can be deployed on the world-wide web to the significant number of U.S. students who are at risk of poor mathematics achievement due to poor understanding of core STEM concepts. The project will also provide a blueprint that can be used widely in other projects and systems, supported by both theory and empirical evidence, for how to help students learn from erroneous examples. The project is funded by the EHR Core Research (ECR) program, which supports work that advances the fundamental research literature on STEM learning.The project will employ an iterative research plan in which log data from online learning of erroneous examples will be datamined and analyzed. The project team will start with log data from over 600 middle school mathematics students who have used erroneous examples to learn in online instructional technology and games. The team will then collect data from approximately two thousand more students over the course of the three-year project to explore whether enhancements to the erroneous examples materials based on revised theory improves student learning. Moment-by-moment learning models will be used to understand whether students learn immediately from erroneous examples, or whether erroneous examples lead to future learning. The project team will also study whether confusion and other forms of affect mediate successful learning from erroneous examples, using previously validated automated detectors of confusion and other affective states. The project team will also investigate how each of the steps of the student interaction with the erroneous examples -- for example, identifying errors, fixing errors, and explaining errors -- promote learning and positive affect. By the end of the third year of the project, the project team anticipates having a comprehensive theoretical model of how learning with erroneous examples works and how erroneous examples should be integrated into instruction more generally.