Catalyst Project: Investigating Adaptive Hybrid Learning in College Algebra to Improve STEM Engagement and Persistence for HBCU Students

催化剂项目为历史上的黑人学院和大学（HBCU）提供支持，致力于建立教师的研究能力，以加强科学，技术，工程和数学（STEM）本科教育和研究。预计该奖项将进一步提高教师的研究能力，改善研究和教学机构，并参与本科生的研究经验。斯蒂尔曼学院概述了一种跨学科的方法来研究自我调节学习理论，自适应学习技术和文化相关教学法的结合，以促进学生在大学代数中的成功。具体而言，主要活动包括设计一个理论驱动的混合自适应大学代数课程。预期的结果是促进持续性和成功的STEM为代表性不足的学生作为HBCU.该项目是在使用跨学科研究的前沿结合1）自我调节学习理论从教育心理学，2）自适应学习技术，利用机器学习和数据分析来生成个性化的学习路径;文化相关的教学加上积极的学习，以促进大学代数的数学掌握。该项目的研究目的是开发和研究一种新的，理论驱动的，混合自适应学习模型的有效性，用于大学代数在3个主要领域：1）学习成绩，2）动机，自我效能和成功预期，3）HBCU学生在STEM方面的成功。 第四个目的是探索教师的经验与试点理论驱动的混合自适应学习模式在大学代数课程在HBCU。本研究将采用序贯解释性混合方法研究设计，量化阶段将包括一个单一的组，前后测设计，以评估这种新的教学模式对学生学习和学生动机的有效性。在后续的定性阶段，半结构化的访谈将进行与学生谁是注册在这个新颖的大学代数课程，以及课程的教师。将对数据进行分析，以评估学生的学习成果，并深入了解在其他HBCU全面实施的潜在障碍。这项研究旨在为理解如何在HBCU创新教学方法和支持学生在STEM的学习成果做出重大贡献，以及通过提供更多数据来改进自适应学习平台的机器学习算法，从而为知识库做出贡献。该项目由历史上的黑人学院和大学本科生项目以及刺激竞争力的既定项目共同资助。研究（EPSCoR）。该奖项反映了NSF的法定使命，并被认为是值得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的支持。

Catalyst Projects provide support for Historically Black Colleges and Universities (HBCU) to work towards establishing research capacity of faculty to strengthen science, technology, engineering, and mathematics (STEM) undergraduate education and research. It is expected that the award will further the faculty member's research capability, improve research and teaching at the institution, and involve undergraduate students in research experiences. Stillman College outlines an interdisciplinary approach to investigate the combination of Self-Regulated Learning theory, Adaptive Learning Technology, and Culturally Relevant Pedagogy to promote student success in college algebra. Specifically, the major activities include the design of a theory-driven hybrid adaptive college algebra course. The anticipated outcomes are to advance persistence and success in STEM for underrepresented students as HBCUs.This project is at the forefront of using interdisciplinary research by combining 1) self-regulated learning theory from educational psychology, 2) adaptive learning technology that leverages machine learning and data analytics to produce personalized learning paths and 3) culturally relevant pedagogy coupled with active learning to promote mathematics mastery in college algebra. The research aims of this project are to develop and examine the effectiveness of a novel, theory-driven, hybrid adaptive learning model for college algebra in 3 main areas: 1) academic performance, 2) motivation, self-efficacy, and success expectations, and 3) success in STEM for HBCU students. The fourth aim is to explore the instructor experience with piloting a theory-driven hybrid adaptive learning model in a college algebra course at an HBCU. A sequential explanatory mixed methods study design will be conducted, where the quantitative phase will include a single group, pretest-posttest design to assess effectiveness of this novel teaching model on student learning and student motivation broadly. In the follow-up qualitative phase, semi-structured interviews will be conducted with students who are enrolled in this novel college algebra course, as well as the course instructors. Data will be analyzed to assess student learning outcomes and to gain insight on potential barriers to a full-scale implementation at other HBCUs. This study seeks to make a significant contribution in understanding how to innovate with teaching methods at an HBCU and support student learning outcomes in STEM, as well as contribute to the knowledge base by providing more data to improve the machine learning algorithms for adaptive learning platforms.This project is jointly funded by the Historically Black Colleges and Universities Undergraduate Program and the Established Program to Stimulate Competitive Research (EPSCoR).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.