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

合作研究：通过校园数据系统和对自我调节学习的有针对性的支持，提高本科生在 STEM 入门课程中的成功率

基本信息

批准号：
1821601
负责人：
Jonathan Hilpert
金额：
$ 99.04万
依托单位：
University of Nevada Las Vegas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1821601&HistoricalAwards=false
关键词：
Collaborative Research Improving Undergraduate Student

项目摘要

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.

这一合作项目包括北卡罗来纳大学教堂山分校(应获奖-1821594)、内华达大学拉斯维加斯分校(UNLV；应获奖-1821601)和南内华达学院的研究人员。美国一直需要更多的STEM专业人员。最初主修STEM的大学生表示，课程作业是离开STEM追求其他兴趣的主要原因。从讲课转向更有吸引力的教学的教师发现，他们的学生更有可能留在STEM专业，但前提是学生知道如何在这些新环境中学习。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。该项目将开发模型来识别STEM入门课程(特别是生物学、解剖学和生理学)中的困难学生，并将测试干预措施，以帮助这些学生获得他们从主动学习课程形式中受益所需的知识和技能。在这个项目中，调查人员将结合：1)利用现有的数据驱动、基于网络的方法识别困难学生，以便及早识别；2)支持困难大学生，采取强有力的举措，重点留住那些传统上没有坚持STEM领域的学生。具体地说，联合国志愿人员组织以前的一个项目--学习理论和分析作为改善本科生STEM教育的指南(LearningTAGs)--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、可获得的学术教练和主动学习的STEM课堂中受益最大。LearningTAGs方法可以通过整合终点线项目的结果来扩展，以更好地服务于苦苦挣扎的学生。研究人员将(1)在北卡罗来纳大学(另一所高度选择性的公共机构)以及南内华达大学(一所开放招生的两年制大学)开发和测试UNLV的LearningingTAGs预测模型和数字干预；(2)利用Finish Line项目关于学术指导的结果来测试各种支持干预措施(如在线自我调节学习教学模块、学术指导和补充指导)；以及(3)确定支持效率是否在不同的学生群体中有所不同，包括STEM中代表性不足的学生和第一代大学生。在该项目中测试和改进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，使用大学从学习管理系统中获得的现有数据。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。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Modeling temporal self-regulatory processing in a higher education biology course

高等教育生物学课程中时间自我调节处理的建模

DOI：
10.1016/j.learninstruc.2019.04.002
发表时间：
2019
期刊：
Learning and Instruction
影响因子：
6.2
作者：
Greene, Jeffrey A.;Plumley, Robert D.;Urban, Christopher J.;Bernacki, Matthew L.;Gates, Kathleen M.;Hogan, Kelly A.;Demetriou, Cynthia;Panter, Abigail T.
通讯作者：
Panter, Abigail T.

Relations between undergraduates’ self-regulated learning skill mastery during digital training and biology performance

本科生数字化培训中自我调节学习技能掌握与生物表现之间的关系

DOI：
10.1007/s11409-023-09356-9
发表时间：
2023
期刊：
Metacognition and Learning
影响因子：
3.3
作者：
Bernacki, Matthew L.;Cogliano, Megan Claire;Kuhlmann, Shelbi L.;Utz, Jenifer;Strong, Christy;Hilpert, Jonathan C.;Greene, Jeffrey A.
通讯作者：
Greene, Jeffrey A.

Coping with the transition to remote instruction: Patterns of self-regulated engagement in a large post-secondary biology course