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

CATALYST项目支持历史悠久的黑人学院和大学(HBCU)致力于建立教职员工的研究能力，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职员工的研究能力，改善学院的研究和教学，并让本科生参与研究经验。斯蒂尔曼学院概述了一种跨学科的方法，研究自我调节学习理论、适应性学习技术和与文化相关的教育学相结合，以促进学生在大学代数方面的成功。具体来说，主要活动包括设计一门以理论为导向的混合自适应大学代数课程。预计的结果是促进代表不足的学生在STEM中的持久力和成功。该项目走在使用跨学科研究的前沿，通过结合1)来自教育心理学的自我调节学习理论，2)利用机器学习和数据分析来产生个性化学习路径的自适应学习技术，以及3)与文化相关的教学法与主动学习相结合，以促进大学代数的数学掌握。本项目的研究目的是开发和检验一种新颖的、理论驱动的、混合式大学代数适应性学习模式在三个主要方面的有效性：1)学业成绩，2)动机、自我效能感和成功期望，3)HBCU学生在STEM中的成功。第四个目标是探索在HBCU的大学代数课程中试验理论驱动的混合自适应学习模式的教师体验。将进行顺序解释性混合方法研究设计，其中定量阶段将包括单一小组、前测-后测设计，以全面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，将对参加这门新颖的大学代数课程的学生以及课程教师进行半结构化访谈。将对数据进行分析，以评估学生的学习结果，并洞察其他HBCU全面实施的潜在障碍。这项研究旨在为理解如何创新HBCU的教学方法和支持STEM的学生学习成果做出重大贡献，并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项目和既定的激励竞争研究计划(EPSCoR)共同资助。该奖项反映了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。

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.