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

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.