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

在NSF改善本科STEM教育计划：教育和人力资源(IUSE：EHR)的支持下，该项目旨在通过为本科工程专业学生提供当前和未来就业市场所需的数据科学技能来服务于国家利益。它打算通过开发数据启用的工程研究模块来实现这一目标，这些模块可以用作课程的独立增强或组合成完整的基于课程的本科生研究体验(CURE)。模块化结构将使模块和治疗很容易集成到现有课程中。这些研究体验将为学生提供使用来自使用物联网技术的工业系统中嵌入的传感器的大数据集的实践体验。由于数据来源(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，因此这种方法有望提高学生的学习动机。研究经验将强调数据科学的计算方面，包括机器学习、分类、回归和集群，从而帮助培养有数据能力的工程师。预计这些模块和解决方案还将为工业物联网数据科学研究贡献基础知识。该项目将研究数据支持的研究模块和治疗方法对学生动机、参与度和成就的影响。这项研究的结果可以让更广泛的STEM教育界了解将数据科学教育整合到工程以外的STEM学科的成功方法。除了调查数据支持的研究模块和治疗方法对学生动机、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反思和元认知的假设。所有这些学生特征都将通过标准仪器进行测量。例如，元认知意识问卷将被用来量化学生在一系列课程开始和结束之间元认知意识的变化。该项目的评估将由奥本评估中心进行，评估和项目研究结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的目标非常一致，即为本科生配备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都很受欢迎。NSF IUSE：EHR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。这是一个参与式的学生学习项目，是一个IUSE：EHR的轨道，支持有前途的实践和工具的创建、探索和实施。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。

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.