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

协作研究：使用教育数据挖掘技术揭示学生如何以及为何从错误示例中学习

• 批准号: 1661153
• 负责人: Ryan Baker
• 金额: $ 58.46万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661153&HistoricalAwards=false
• 关键词: Collaborative; Research; Using; Educational; Data

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.

在这个研究项目中，卡内基梅隆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使数学学习者受益。该项目小组将根据对使用这些材料学习的数百名学生的日志数据的分析，反复改进现有的计算机教学材料。这项工作将与中学数学学生，特别是六年级的学生，目标主题将是小数。最近的研究表明，从他人的错误中吸取教训-这是医学教育中使用的一种重要教学方法，但在其他教育领域较少使用-具有相当大的帮助学生学习的潜力。然而，错误的例子并没有被广泛使用，部分原因是人们还不知道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，通过经验证据对其有效性进行审查，这些课程可以在万维网上部署给大量美国学生，这些学生由于对STEM核心概念的理解不佳而面临数学成绩不佳的风险。该项目还将提供一个蓝图，可广泛用于其他项目和系统，由理论和经验证据支持，如何帮助学生从错误的例子中学习。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，其中将对错误示例的在线学习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，该团队将在三年的项目过程中从大约2000多名学生那里收集数据，以探索基于修订理论的错误示例材料的增强是否会改善学生的学习。每时每刻的学习模型将用于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。该项目团队还将研究困惑和其他形式的影响是否会调解从错误的例子中成功学习，使用先前验证的困惑和其他情感状态的自动检测器。项目团队还将调查学生与错误示例互动的每个步骤-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，项目团队预计将有一个全面的理论模型，说明如何使用错误的例子进行学习，以及如何将错误的例子更普遍地融入教学中。

项目成果

Focused self-explanations lead to the best learning outcomes in a digital learning game

有针对性的自我解释可以在数字学习游戏中带来最佳的学习成果

• 发表时间: 2022
• 期刊: Proceedings of the 16th International Conference on Learning Science (ICLS 2022
• 作者: McLaren, Bruce M.;Richey, J. Elizabeth;Nguyen, Huy Anh;Mogessie, Michael
• 通讯作者: Mogessie, Michael

More confusion and frustration, better learning: The impact of erroneous examples

• DOI: 10.1016/j.compedu.2019.05.012
• 发表时间: 2019-10-01
• 期刊: COMPUTERS & EDUCATION
• 影响因子: 12
• 作者: Richey, J. Elizabeth;Andres-Bray, Juan Miguel L.;McLaren, Bruce M.
• 通讯作者: McLaren, Bruce M.

Extending deep knowledge tracing: Inferring interpretable knowledge and predicting post-system performance

扩展深度知识追踪：推断可解释的知识并预测系统后性能

• 发表时间: 2020
• 期刊: Proceedings of the 28th International Conference on Computers in Education (ICCE 2020
• 作者: Scruggs, R.

Confrustion in Learning from Erroneous Examples: Does Type of Prompted Self-explanation Make a Difference?

从错误例子中学习的困惑：提示性自我解释的类型有影响吗？

• DOI: 10.1007/978-3-030-23204-7_37
• 发表时间: 2020
• 期刊: LNAI 11625
• 作者: J. Elizabeth Richey, Bruce M.

The Impact of Gender in Learning With Games: A Consistent Effect in a Math Learning Game

• DOI: 10.4018/ijgbl.309128
• 发表时间: 2022-01
• 期刊: Int. J. Game Based Learn.
• 作者: H. Nguyen;Xinying Hou;J. Richey;B. McLaren