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，卡内基梅洛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能够使数学学习者受益。项目小组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，反复改进现有的基于计算机的教学材料。这项工作将在中学生中完成，特别是六年级的学生，目标主题将是小数。最近的研究和研究表明，从他人的错误中学习--这是医学教育中使用的一种突出的教学方法，但在其他教育领域不太常用--有相当大的潜力帮助学生学习。然而，错误的例子没有被广泛使用，部分原因是人们还不清楚它们是如何以及为什么提供这种好处的。该项目的更广泛影响是，它将提供改进的数学课，注入错误的例子，审查其有效性的经验证据，这些课程可以部署在万维网上，供大量由于对STEM核心概念理解不足而面临数学成绩低下风险的美国学生使用。该项目还将为如何帮助学生从错误的例子中学习提供一个蓝图，该蓝图可以在其他项目和系统中广泛使用，得到理论和经验证据的支持。该项目由EHR核心研究(ECR)计划资助，该计划支持推进STEM学习基础研究文献的工作。该项目将采用迭代研究计划，其中将对错误例子的在线学习的日志数据进行数据挖掘和分析。该项目团队将从600多名中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，该团队将在为期三年的项目过程中从大约2000名学生那里收集数据，以探索基于修订后的理论对错误例子材料的改进是否会改善学生的学习。将使用逐个学习模型来了解学生是否立即从错误的例子中学习，或者错误的例子是否会导致未来的学习。该项目团队还将使用之前经过验证的混淆和其他情感状态的自动检测器，研究困惑和其他形式的影响是否会中介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例子互动的每个步骤--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。到项目第三年结束时，项目组预计将有一个全面的理论模型，说明用错误的例子学习是如何工作的，以及错误的例子应该如何更普遍地纳入教学。

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.