Data-Enabled Engineering Projects for Undergraduate Data Science and Engineering Education

在美国国家科学基金会改善本科STEM教育计划：教育与人力资源（IUSE: EHR）的支持下，该项目旨在通过为工科本科学生提供当前和未来就业市场所需的数据科学技能，为国家利益服务。它打算通过开发数据支持的工程研究模块来实现这一目标，这些模块可以作为课程的独立增强功能，也可以组装成完整的基于课程的本科生研究经验（CUREs）。模块化结构将使模块和课程易于集成到现有课程中。这些研究经验将为学生提供使用使用物联网技术的工业系统中嵌入的传感器的大型数据集的实践经验。这种方法有望提高学生的积极性，因为数据的来源（例如，支持wi - fi的小麦水分检测系统）与学生相关且易于理解。研究经验将强调数据科学的计算方面，包括机器学习、分类、回归和聚类，从而帮助培养具有数据能力的工程师。预计这些模块和CUREs还将为工业物联网数据科学研究提供基础知识。该项目将研究数据支持的研究模块和CURES对学生动机、参与度和成就的影响。这项研究的结果可以为更广泛的STEM教育界提供将数据科学教育融入工程以外的STEM学科的成功方法。除了调查数据支持的研究模块和CUREs对学生动机，参与度和成就的影响外，该项目还将测试数据支持的工程研究经验将增强学生的反思和元认知的假设。所有这些学生的特征都将通过标准仪器来测量。例如，元认知意识量表将被用来量化学生在一组课程开始和结束之间的元认知意识的变化。该项目的评估将由奥本评估中心进行，评估和项目研究成果将通过奥本大学主办的网站、会议报告和出版物进行传播。该项目的目标与NSF的目标一致，即为本科生提供数据科学和工程技能，这些技能在当前和未来的就业市场上需求量很大。NSF IUSE: EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。这是一个参与的学生学习项目，一个IUSE: EHR跟踪，支持有前途的实践和工具的创建、探索和实现。该奖项反映了美国国家科学基金会的法定使命，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。

With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE: EHR), this project aims to serve the national interest by equipping undergraduate engineering students with the data science skills needed by current and future job markets. It intends to accomplish this goal by developing data-enabled engineering research modules that can be used as standalone enhancements to courses or assembled into complete course-based undergraduate research experiences (CUREs). The modular structure will make the modules and CUREs easy to integrate into existing courses. These research experiences will provide students with hands-on experience using large datasets from sensors embedded in industrial systems that use Internet-of-Things technologies. This approach is expected to increase student motivation, since the source of the data (e.g., Wi-Fi-enabled wheat moisture detection systems) is relevant to and easily understood by students. The research experiences will emphasize computational aspects of data science including machine learning, classification, regression, and clustering, thus helping to prepare data-capable engineers. It is expected that the modules and CUREs will also contribute fundamental knowledge to industrial Internet-of-Things data science research. The project will study the impact of the data-enabled research modules and CURES on student motivation, engagement, and achievement. Results of this research could inform the broader STEM education community about successful approaches for integrating data science education into STEM disciplines beyond engineering.In addition to investigating the impact of the data-enabled research modules and CUREs on student motivation, engagement, and achievement, the project will also test the hypothesis that data-enabled engineering research experiences will enhance students' reflection and metacognition. All of these student characteristics will be measured via standard instruments. For example, the Metacognition Awareness Inventory will be used to quantify changes in students' metacognition awareness between the beginning and end of a set of courses. Evaluation of the project will be conducted by the Auburn Center for Evaluation and the evaluation and project research findings will be disseminated through an Auburn University-hosted website, conference presentations, and publications. The project goals align well with NSF's goal of equipping undergraduate students with data science and engineering skills that are in high demand in the current and future job markets. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This is an Engaged Student Learning Project, an IUSE: EHR track that 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.