The Role of a Technology-Based Adaptive Learning and Assessment Program in Supporting Mathematics and Statistics Corequisite Courses

该项目旨在通过加深我们对基于人工智能的技术工具如何支持学生在早期本科代数和统计课程中取得成功的理解来服务于国家利益。许多州继续用在常规课程会议之外提供额外支持的必修课程取代补救数学课程。 这一战略在提高学生成功率方面显示出了巨大的希望。 许多共存模型使用人工智能（AI）技术工具，在学生需要更多练习的领域自适应地提供适当难度的问题。尽管这些工具被广泛使用，但还没有系统地审查这些工具是如何在必备课程中实施的，这使得了解哪些做法可能需要额外的教学成为一项挑战。该项目将通过专门研究知识空间评估和学习（ALEKS）系统来满足这一需求，该系统在美国各地的各种机构中使用。本次调查将集中在几个关键的研究问题：1）如何在第一年的代数和统计课程，学生利用ALEKS最常见的方式是什么？2)哪些因素影响学生和教师使用ALEKS的方式？3)学生使用ALEKS的方式在多大程度上与代数和统计学必修课程的成功以及其他积极的学生成果有关？该项目将探索在两个阶段的代数和统计学课程中使用ALEKS和其他自适应AI工具。在第一个项目阶段，研究人员将制定和管理一项调查，所有两年制和四年制的中学后机构，正在使用ALEKS的共存课程。在第二阶段，研究人员将从第一阶段的受访者中招募三个合作机构，并进行深入的案例研究，以便深入探索ALEKS在一系列环境中的使用。这项研究将产生重要的新知识，如ALEKS技术为基础的工具是如何实施和表征学生使用该平台的常见方式，这反过来又可以通知共存课程的设计。研究结果还将允许像ALEKS这样的人工智能工具的开发人员在设计时考虑到常见的实现和学生使用模式，这将提高这些工具的实用性。NSF IUSE：EDU计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to serve the national interest by advancing our understanding of how artificial intelligence-based technology tools can support student success in early undergraduate algebra and statistics courses. Many states continue to replace remedial mathematics courses with corequisite courses that provide additional support beyond regular course meetings. This strategy has shown significant promise in improving student success rates. Many corequisite models use artificial intelligence (AI) technology tools that adaptively provide problems at appropriate levels of difficulty in areas where students need more practice. Despite their widespread use, there has not been a systemic examination of how such tools are being implemented in corequisite courses which makes it challenging to understand what practices might warrant additional instruction. This project will address that need by specifically studying the Assessment and Learning in Knowledge Spaces (ALEKS) system, which is in use at a wide variety of institutions across the United States. This investigation will focus on several key research questions: 1) How is ALEKS being implemented in first-year algebra and statistics courses and what are the most common ways students utilize ALEKS? 2) What factors influence the ways students and instructors use ALEKS? 3) To what extent are the ways students use ALEKS associated with success in algebra and statistics corequisite courses and other positive student outcomes? This project will explore the use of ALEKS and other adaptive AI tools in corequisite algebra and statistics classes across two phases. In the first project phase, researchers will develop and administer a survey to all 2-year and 4-year postsecondary institutions that are using ALEKS in corequisite courses. In the second phase, researchers will recruit three partner institutions from phase one respondents and conduct deep case studies that will allow for in-depth exploration of ALEKS use in a range of settings. This study will produce important new knowledge on how technology-based tools like ALEKS are implemented and characterize common ways students use the platform, which can in turn inform the design of corequisite courses. Findings will also allow developers of AI tools like ALEKS to design with common implementations and student use patterns in mind, which stands to improve the utility of those tools. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.