Leveraging Machine Learning to Explore the Effects of the Design2Data Course-based Undergraduate Research Experience

该项目旨在通过让学生参与设计数据（D2D）计划，这是一个全国联网的基于生物化学课程的本科生研究体验（CURE），从而为国家利益服务。该项目将有助于确定学生体验的哪些部分对毕业和进入STEM职业产生积极影响。D2D让学生通过尖端的计算驱动的研究项目探索蛋白质科学。该项目将课程固定在一个由积极主动、早期采用者教师组成的网络中，在不同的STEM学科和水平上教授学生。实践D2D学习经验为学生在这个生物学新时代取得成功做好准备，同时众包数据收集以改进蛋白质建模人工智能方法。庞大而多样化的D2D学生群体为探索CURE教育研究的新颖的基于机器学习的评估方法提供了丰富的机会。在实现这一补助金的目标，该项目将（a）开创一种尖端的方法来调查学生在研究经验中的学习，并将使这些数据分析方法广泛地提供给STEM教育研究人员，（B）为数千名学生创造公平，有意义的研究经验，其中许多人本来没有机会。D2D的本科研究项目锚定程序，和D2D教师网络成员通过将该项目整合到他们的类在美国各地的各种校园促进其实施。参与的学生在功能上表征新的酶突变体，为蛋白质建模利益相关者生成数据，以探索开发更好的功能预测工具，以更快地解决以人为中心的问题。不仅有可能通过该计划有意义地推进科学，而且经验使雇主能够公平地获得尖端的生物技术培训。通过该计划接触许多学生是容易的：D2D很容易融入跨学科的实验室实习环境，从第一年到高级课程。该网络包括40个机构，在资助期间将吸引大约4，000名学生。该项目的目标是使用这个庞大的，多样化的人群来评估与STEM持久性相关的心理社会和行为结果的中介，方法是通过收集具有分层变量和基准尖端机器学习方法的大规模激励数据进行数据分析。该项目活动将（a）支持专门的网络连续性协调，以保持教师参与的当前水平，以及（B）评估活动，以计划和执行全面的数据收集和基于机器学习（ML）的分析，捕获和评估一组深入的离散CURE实施变量。通过这些活动，将通过使其他教育研究人员能够使用尖端的学生学习数据分析方法，促进D2D网络之外有意义的专业发展。最后，该项目将把研究交给成千上万的学生，使他们能够更公平地获得CURE，增加参与研究的学生的多样性。NSF IUSE：EDU计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。该奖项反映了NSF的法定使命，并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，被认为值得支持。

This project aims to serve the national interest by engaging students in the Design to Data (D2D) program, a nationally networked biochemistry Course-based Undergraduate Research Experience (CURE). The project will help to determine which parts of the student experience positively impact graduation and access to STEM careers. D2D engages students in exploring protein science through a cutting-edge computationally driven research project. This project anchors the curriculum across a network of highly motivated, early-adopter faculty members teaching students in varied STEM disciplines and levels. The hands-on D2D learning experience prepares students for success in this new era of biology while crowdsourcing data collection for improved protein modeling artificial intelligence methods. The large, diverse D2D student body offers a rich opportunity to explore novel, machine learning-based assessment methods for CURE education research. In reaching the aims of this grant, this project will (a) pioneer a cutting-edge approach to investigating student learning in research experiences and will make these data analysis methods broadly accessible to STEM education researchers, and (b) create equitable, meaningful research experiences for thousands of students, many of whom would not have otherwise had the opportunity. D2D’s undergraduate research project anchors the program, and D2D faculty network members facilitate its implementation by integrating the project into their classes on a wide variety of campuses across the United States. Participating students functionally characterize novel enzyme mutants generating data for protein modeling stakeholders to explore with the goal of developing better functionally predictive tools for more rapid solutions to human-centered problems. Not only is there potential to meaningfully advance science through the program, but the experience enables equitable access to cutting-edge biotechnology training that is in high demand by employers. Reaching many students with this program is tractable: D2D readily integrates into lab practicum settings across the disciplines and from first-year to senior-level classes. The network includes forty institutions and will engage approximately 4,000 students over the funding period. The project objective is to use this large, diverse population in assessing mediators to psycho-social and behavioral outcomes linked to STEM persistence by collecting multi-level motivational data at scale with layered variables and benchmark cutting edge machine learning methods for the data analysis. The project activities will (a) support dedicated network continuity coordination to maintain current levels of faculty participation, and (b) assessment activities to plan and execute comprehensive data collection and machine learning (ML)-based analysis that captures and evaluates a deep set of discrete CURE-implementation variables. From these activities, meaningful professional development beyond the D2D Network will be promoted by making the cutting-edge student learning data analysis methods accessible to other education researchers. Finally, this project will put research into hands of thousands of students and enable more equitable access to CUREs, increasing the diversity of students participating in research. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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.