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

该项目旨在通过促进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，为国家利益服务。许多州继续用辅修课程取代补习数学课程，这些课程在常规课程会议之外提供额外的支持。这一策略在提高学生成功率方面显示出了巨大的希望。许多共同条件模型使用人工智能（AI）技术工具，自适应地在学生需要更多练习的领域提供适当难度的问题。尽管这些工具被广泛使用，但对于这些工具如何在必要的课程中实施，还没有进行系统的检查，这使得了解哪些实践可能需要额外的指导变得具有挑战性。该项目将通过专门研究知识空间评估和学习（ALEKS）系统来解决这一需求，该系统在美国的各种机构中广泛使用。这项调查将集中在几个关键的研究问题上：1)ALEKS是如何在一年级的代数和统计课程中实施的，学生使用ALEKS的最常见方式是什么？2)什么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的必修课程和其他积极的学生成果相关？该项目将跨越两个阶段，探索在代数和统计学课程中使用ALEKS和其他自适应人工智能工具。在项目的第一阶段，研究人员将对所有在必修课程中使用ALEKS的两年制和四年制高等教育机构进行调查和管理。在第二阶段，研究人员将从第一阶段的受访者中招募三家合作机构，并进行深入的案例研究，以深入探索ALEKS在一系列环境中的使用。这项研究将产生关于如何实现像ALEKS这样的基于技术的工具的重要新知识，并描述学生使用平台的常见方式，这反过来可以为同修课程的设计提供信息。研究结果还将允许像ALEKS这样的人工智能工具的开发人员设计通用实现和学生使用模式，这将提高这些工具的实用性。NSF IUSE: EDU项目支持研究和开发项目，以提高所有学生STEM教育的有效性。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。该奖项反映了美国国家科学基金会的法定使命，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.