Collaborative research: Fostering conceptual understanding and skill with an intelligent tutoring system for equation solving

协作研究：通过求解方程的智能辅导系统培养概念理解和技能

• 批准号: 1760922
• 负责人: Vincent Aleven
• 金额: $ 97.52万
• 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: 2018-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1760922&HistoricalAwards=false
• 关键词: Collaborative; research; Fostering; conceptual; understanding

This Education and Human Resources Core Research project addresses a persistent challenge in designing effective mathematics instruction: how to foster deep learning of concepts and skills, with strong connections among them. This challenge arises especially in middle school and high school algebra; far too often, students in algebra courses come away with only a moderate level of skill at procedures (e.g., equation solving) with almost no conceptual understanding of why these procedures work. This lack of deep understanding is unfortunate, because algebra is an important stepping stone to advanced mathematics as well as to future educational and employment opportunities. Thus, there is a great need for algebra instruction that helps students acquire well-integrated knowledge of key concepts and problem-solving procedures. This project tackles this issue using, as a platform, a software-based intelligent tutoring system that will extend an existing tutoring system for algebra. The new, enhanced tutoring system will support both practice in solving equations and new conceptually-oriented algebra activities. It will provide detailed and targeted guidance that adapts to individual students' errors, strategies, and developing algebra knowledge. Lab-based and classroom studies will investigate three key questions: (1) What mix of conceptual and procedural activities best helps students acquire deep skill and conceptual knowledge of algebra? (2) How frequently should students shift back and forth between the two types of activities? (3) How effective is the new tutoring system compared to commonly-used software, namely, Khan Academy, or a standard intelligent tutoring system that provides procedural practice only? The project will lead to new knowledge about how to create effective instruction for middle school and high school algebra, together with new intelligent tutoring software that embodies this knowledge.Based on past research, it is commonly accepted that in many domains, gains in conceptual knowledge can lead to gains in procedural knowledge, and vice versa. Further, there is some evidence that instruction that shifts back and forth between conceptually-oriented activities and procedurally-oriented activities is especially effective. However, it is not yet known what sorts of activities best support the development of conceptual knowledge, or how they should be integrated with activities that target learning of procedures so that students might make connections between the two. To address these issues, the project will extend existing tutoring system for equation solving (called Lynnette) with conceptual activities, based on past research that suggests that worked examples, self-explanation, visual representations (namely, bar diagrams), and activation of prior knowledge can be effective for promoting gains in conceptual knowledge. In doing so, this will leverage an established technical infrastructure for intelligent tutoring systems research and development, called CTAT/Tutorshop. The project will conduct two lab studies and two classroom studies to investigate what mix of conceptually-oriented activities is the most effective complement to the system's current set of procedurally-oriented activities, how frequently students should shift back and forth between procedurally- and conceptually-oriented activities, and how effective the resulting tutoring system is, compared to two control conditions in which students work with commonly used types of software for algebra learning (Khan Academy and Lynnette). The project will generate new knowledge about how to design effective instruction that helps students acquire well-integrated conceptual and procedural knowledge that is effective in real educational settings. This knowledge could lead to better, more conceptually-oriented instruction in algebra, and it has the potential to improve instruction in other STEM learning environments. The project will also create a new intelligent tutoring system for algebra that is much more effective than current systems. The system will be made available to teachers and schools free of charge.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.

这一教育和人力资源核心研究项目解决了在设计有效的数学教学方面的一个长期挑战：如何促进对概念和技能的深入学习，这些概念和技能之间有很强的联系。这一挑战尤其出现在初中和高中的代数中；很多时候，代数课程的学生在程序(如方程求解)方面只有中等水平的技能，几乎没有概念上理解为什么这些程序有效。这种缺乏深刻理解是不幸的，因为代数是通往高等数学以及未来教育和就业机会的重要垫脚石。因此，非常需要代数教学，帮助学生获得关键概念和解决问题程序的完整知识。这个项目使用一个基于软件的智能辅导系统作为平台来解决这个问题，该系统将扩展现有的代数辅导系统。新的、增强的辅导系统将支持解方程的练习和新的以概念为导向的代数活动。它将提供详细和有针对性的指导，以适应个别学生的错误、策略和发展代数知识。基于实验室和课堂的研究将探讨三个关键问题：(1)什么是概念性和程序性活动的组合，最能帮助学生获得深厚的代数技能和概念性知识？(2)学生应该多频繁地在这两种类型的活动之间来回转换？(3)与常用的软件，即可汗学院，或只提供程序性练习的标准智能教学系统相比，新的教学系统的效率如何？该项目将带来关于如何创建有效的初中和高中代数教学的新知识，以及体现这些知识的新的智能辅导软件。基于过去的研究，人们普遍认为，在许多领域，概念知识的获得可以导致程序性知识的获得，反之亦然。此外，有一些证据表明，在面向概念的活动和面向程序的活动之间来回转换的教学特别有效。然而，目前还不知道什么样的活动最能支持概念性知识的发展，或者它们应该如何与以程序学习为目标的活动相结合，以便学生可以在两者之间建立联系。为了解决这些问题，该项目将根据过去的研究，在现有的方程求解辅导系统(称为Lynnette)的基础上，扩展概念活动，这些研究表明，工作示例、自我解释、视觉表示(即条形图)和激活先前的知识可以有效地促进概念知识的获得。通过这样做，这将利用已建立的智能教学系统研究和开发的技术基础设施，称为CTAT/Tutorshop。该项目将进行两个实验室研究和两个课堂研究，以调查什么是面向概念的活动的组合是对系统当前的面向程序的活动集最有效的补充，学生应该多频繁地在面向程序的活动和面向概念的活动之间来回转换，以及所产生的辅导系统的有效性，与学生使用常用的代数学习软件(Khan Academy和Lynnette)的两种控制条件进行比较。该项目将产生关于如何设计有效教学的新知识，帮助学生获得在实际教育环境中有效的整合良好的概念和程序知识。这些知识可能会带来更好的、更具概念性的代数教学，并有可能改善其他STEM学习环境中的教学。该项目还将创建一种新的智能代数辅导系统，该系统比现有系统更有效。该系统将免费提供给教师和学校。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Students’ self-regulated use of diagrams in a choice-based intelligent tutoring system

学生在基于选择的智能辅导系统中自我调节图表的使用

• 发表时间: 2022
• 期刊: ICLS 2022
• 作者: Nagashima, T.;Tseng, S.;Ling, E.;Bartel, A. N.;Vest, N. A.;Silla, E. M.;Alibali, M. W.;Aleven, V.
• 通讯作者: Aleven, V.

Rethinking technology-based educational studies in the evolving classroom environment: An interview study with US teachers

在不断变化的课堂环境中重新思考基于技术的教育研究：对美国教师的访谈研究

• DOI: 10.22318/icls2021.933
• 发表时间: 2021
• 期刊: 15th International Conference of the Learning Sciences – ICLS 2021
• 作者: Nagashima, T.;Yadav, G.;Aleven, V.
• 通讯作者: Aleven, V.

Promoting students’ self-regulated learning choices with diagrams in intelligent tutoring software

通过智能辅导软件中的图表促进学生自主学习选择

• 发表时间: 2023
• 期刊: Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023
• 作者: Nagashima, T.;Zheng, B.;Tseng, S.;Ling E.;Aleven, V.
• 通讯作者: Aleven, V.

Self-Explanation of worked examples integrated in an intelligent tutoring system enhances problem solving and efficiency in algebra

智能辅导系统中集成的工作示例的自我解释可提高代数问题的解决能力和效率

• 发表时间: 2022
• 期刊: Proceedings of the 44th Annual Conference of the Cognitive Science Society
• 作者: Vest, N. A.:;Bartel, A. N.;Nagashima, T.;Aleven, V.;Alibali, M. W.
• 通讯作者: Alibali, M. W.

Can Crowds Customize Instructional Materials with Minimal Expert Guidance?: Exploring Teacher-guided Crowdsourcing for Improving Hints in an AI-based Tutor

群体可以在最少的专家指导下定制教学材料吗？：探索教师引导的众包以改进基于人工智能的导师的提示

• DOI: 10.1145/3449193
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Yang, Kexin Bella;Nagashima, Tomohiro;Yao, Junhui;Williams, Joseph Jay;Holstein, Kenneth;Aleven, Vincent
• 通讯作者: Aleven, Vincent