Collaborative Research: Improving Undergraduate Student Success in Introductory STEM Courses Via Campus Data Systems and Targeted Support for Self-Regulated Learning

该合作项目包括北卡罗来纳州查佩尔山大学（Award DUE-1821594）、拉斯维加斯内华达州大学（UNLV; Award DUE-1821601）和南内华达州学院的研究人员。美国需要更多的STEM专业人员。最初主修STEM的大学生将课程作为离开STEM去追求其他兴趣的主要原因。从讲座转向更吸引人的教学的教师发现，他们的学生更有可能留在STEM专业，但只有当学生知道如何在这些新环境中学习时。不幸的是，许多学生根本没有经历过引人入胜的教学，因此没有发展出充分利用它的知识和技能。该项目将开发模型，以识别入门STEM课程（特别是生物学和解剖学和生理学）中有困难的学生，并将测试干预措施，以帮助这些学生获得他们需要从主动学习课程格式中受益的知识和技能。这项工作应该提供知识，可以用来提高学生的成功和保留在所研究的课程，并应告知其他STEM课程类似的干预措施。在这个项目中，研究人员将结合联合收割机：1）用现有的数据驱动识别有困难的学生，基于网络的早期识别方法; 2）通过一项强有力的举措支持苦苦挣扎的大学生，重点是留住传统上没有坚持STEM领域的学生。具体来说，UNLV以前的一个项目，学习理论和分析作为改善本科STEM教育的指南（LearningTAG），已经开发了一种数据驱动的方法，用于识别和直接干预有困难的学生。 此外，北卡罗来纳大学教堂山分校的终点线项目发现，第一代大学生从早期干预、可访问的学术教练和积极学习的STEM教室中受益最多。通过整合终点线项目的发现，可以扩展LearningTAGs方法，以更好地为苦苦挣扎的学生服务。研究人员将（1）在斯坦福大学开发和测试UNLV的LearningTAGs预测模型和数字干预（另一个高度选择性的公共机构）以及南内华达州学院（公开招生的两年制学院）;（2）利用终点线项目关于学术辅导的调查结果来测试各种支持干预措施（即在线自主学习教学模块、学术辅导和补充教学）;以及（3）确定不同学生群体的支持效能是否存在差异，包括来自STEM中代表性不足的群体的学生和第一代大学生。在这个项目中测试和完善的校园数据基础设施和学生支持平台应该提供一个可以在其他学院和大学复制的模型，使用大学现有的学习管理系统的数据。这个奖项反映了NSF的法定使命，并通过使用基金会的智力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 collaborative project includes investigators at the University of North Carolina at Chapel Hill (Award DUE-1821594), the University of Nevada at Las Vegas (UNLV; Award DUE-1821601), and the College of Southern Nevada. The United States has an ongoing need for more STEM professionals. College students who initially major in STEM cite the coursework as a major reason for leaving STEM to pursue other interests. Instructors who move away from lectures to more engaging kinds of instruction find that their students are more likely to stay in STEM majors, but only when the students know how to learn in these new environments. Unfortunately, many students have simply not experienced engaging instruction and therefore have not developed the knowledge and skills to take full advantage of it. This project will develop models to identify struggling students in introductory STEM courses (especially biology and anatomy and physiology) and will test interventions to help these students gain the knowledge and skills they need to benefit from active-learning course formats. This work should provide knowledge that can be used to increase students' success and retention in the courses under study, and should inform similar interventions in other STEM courses.In this project, the investigators will combine: 1) identifying struggling students with an existing data-driven, web-based approach for early identification and 2) support of struggling college students with a robust initiative focused on retaining students who traditionally have not persisted in STEM fields. Specifically, a previous project at UNLV, Learning Theory and Analytics as Guides to Improve Undergraduate STEM Education (LearningTAGs), has developed a data-driven approach for identifying and directly intervening with struggling students. In addition, the Finish Line Project at UNC-Chapel Hill has found that first-generation college students benefit most from early intervention, accessible academic coaches, and active-learning STEM classrooms. The LearningTAGs methods can be expanded to better serve struggling students by integrating findings from the Finish Line Project. The researchers will (1) develop and test UNLV's LearningTAGs prediction modeling and digital intervention at UNC (another highly selective, public institution) as well as at the College of Southern Nevada (an open enrollment two-year college); (2) leverage Finish Line Project findings about academic coaching to test various support interventions (i.e. online self-regulated learning instructional modules, academic coaching, and supplemental instruction); and (3) identify whether support efficacy varies across different groups of students, including students from groups that are underrepresented in STEM and first-generation college students. The campus data infrastructure and student support platform that is tested and refined in this project should provide a model that can be replicated at other colleges and universities, using the universities' existing data from learning management systems.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.