Learning Depends on Knowledge: Using Interaction Designs and Machine Learning to Contrast the Testing and Worked Example Effects

学习取决于知识：使用交互设计和机器学习来对比测试和工作示例的效果

• 批准号: 1824257
• 负责人: Ken Koedinger
• 金额: $ 71.24万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824257&HistoricalAwards=false
• 关键词: Learning; Depends; Knowledge; Using; Interaction

A distinctive characteristic of human learning is its capability to flexibly acquire a wide range of rich and complex forms of knowledge (e.g., first and second languages) and acquiring new and accumulated knowledge (e.g., learning physics is easier after having learned algebra). An adequate explanation of human learning must address how existing knowledge changes the way we learn so that we achieve knowledge goals and in specific contexts. This project aims to discover and specify how human learning processes operate differently under different contexts, depending upon what content is being learned. This research contributes to improved educational practices by specifying how learning processes are influenced by knowledge acquisition in a systematic and replicable way. This research will enhance our understanding of successful learning and optimal performance.The project explores learning processes by contrasting learning from retrieval practice and learning from studying examples. The goal of this project is to resolve and clarify how these processes compete for cognitive resources, including attention and working memory, in ways that depend on the knowledge content to be learned. This research examines (1) the learning processes involved in learning from retrieval practice and from worked examples, (2) how these learning processes work differently when applied to different knowledge content, and (3) the computational mechanisms of learning that give rise to learning different content. The researchers use a combination of experiments in which the learning approach is varied along with the materials being studied and machine learning models. It will demonstrate knowledge-learning dependence by showing that one learning process (e.g., retrieval practice) produces better learning outcomes than another (e.g., example encoding) in some knowledge contexts but the reverse occurs in other knowledge contexts. By implementing the studies as part of a machine learning architecture, this research will provide computational evidence and theoretical insight into the hypothesized knowledge-learning dependence framework. The machine learning architecture developed may be used as an educational and research tool in learning sciences, and the project involves training new learning scientists.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人类学习的一个独特特征是它可以灵活地获取广泛而复杂的知识形式（例如，第一语言和第二种语言），并获得新的和积累的知识（例如，学习物理学在学习代数后更容易）。对人类学习的充分解释必须解决现有知识如何改变我们学习的方式，以便我们实现知识目标和特定环境。该项目旨在根据所学的内容来发现并指定在不同情况下的人类学习过程如何不同。这项研究通过指定学习过程如何以系统和可复制的方式来影响学习过程，从而有助于改善教育实践。这项研究将增强我们对成功学习和最佳表现的理解。该项目通过从检索实践中进行对比学习并从学习示例中学习来探讨学习过程。该项目的目的是通过取决于要学习的知识内容的方式来解决和阐明这些过程如何争夺认知资源，包括注意力和工作记忆。这项研究研究（1）从检索实践和工作示例中学习涉及的学习过程，（2）当应用于不同知识内容时，这些学习过程的工作方式有所不同，以及（3）学习的计算机制引起了学习不同的内容。研究人员结合了实验的组合，其中学习方法随着所研究材料和机器学习模型而变化。它将通过证明一个学习过程（例如检索实践）在某些知识环境中（例如，示例编码）产生更好的学习成果来证明知识学习的依赖性，但相反发生在其他知识环境中。通过作为机器学习体系结构的一部分实施研究，该研究将为假设的知识学习依赖框架提供计算证据和理论见解。开发的机器学习体系结构可以用作学习科学领域的教育和研究工具，该项目涉及培训新的学习科学家。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来支持的。审查标准。

项目成果

Self-regulated spacing in a massive open online course is related to better learning

• DOI: 10.1038/s41539-020-0061-1
• 发表时间: 2020-01-01
• 期刊: NPJ SCIENCE OF LEARNING
• 影响因子: 4.2
• 作者: Carvalho, Paulo F.;Sana, Faria;Yan, Veronica X.
• 通讯作者: Yan, Veronica X.

Investigating Differential Error Types Between Human and Simulated Learners

• DOI: 10.1007/978-3-030-52237-7_47
• 发表时间: 2020-06-09
• 期刊: Artificial Intelligence in Education
• 作者: Weitekamp D;Ye Z;Rachatasumrit N;Harpstead E;Koedinger K
• 通讯作者: Koedinger K

An Interaction Design for Machine Teaching to Develop AI Tutors

• DOI: 10.1145/3313831.3376226
• 发表时间: 2020-01-01
• 期刊: PROCEEDINGS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'20)
• 作者: Weitekamp, Daniel, III;Harpstead, Erik;Koedinger, Ken R.
• 通讯作者: Koedinger, Ken R.

Square it up!: How to model step duration when predicting student performance

平方起来！：在预测学生表现时如何对步骤持续时间进行建模

• DOI: 10.1145/3303772.3303827
• 发表时间: 2019
• 期刊: Proceedings of the 9th International Conference on Learning Analytics & Knowledge
• 作者: Chounta, Irene-Angelica;Carvalho, Paulo F.
• 通讯作者: Carvalho, Paulo F.

Drinking Our Own Champagne: Analyzing the Impact of Learning-by-doing Resources in an E-learning Course

喝我们自己的香槟：分析电子学习课程中边做边学资源的影响

• 发表时间: 2021
• 期刊: Companion Proceedings of the 11thInternational Conference on Learning Analytics & Knowledge LAK20
• 作者: Hou, Xinying;Carvalho, Paulo F.;Koedinger, Kenneth R.
• 通讯作者: Koedinger, Kenneth R.