Engaged Student Learning: Coalition for Undergraduate Computational Data-enabled Science & Engineering (CDSE) Education

该项目将在四个合作校区开发一个虚拟系，为那些太小而无法支持这类系的校区的学生提供计算机科学教育。这个新部门将专注于分析“大数据”--大量的计算和观测数据--这些数据在STEM中正变得越来越普遍。网络学习技术，如录音讲座、存档材料、博客参与和主动学习方法将结合在一起，提供一套涵盖气象学、环境科学、生物和化学的大数据科学课程。通过将不同校区的学生合并到相同的课程中，可以克服个别校区资源最少、潜在招生人数有限的问题。特别是，该项目将专注于开发生物和地球科学课程，这两个领域的学生不会被传统的计算机科学课程吸引。该项目将开发一种灵活、混合的学习模式和有效的学习评估工具，可以跨多个学科和机构实施。该项目的主要目标和相应目标是：1)利用促进机构间和学科间合作的主动学习和研究性教学方法，在数学建模、数据挖掘、基因组学和生物信息学以及大气和水球学问题方面，开发和实施高质量的相关计算和数据科学与工程(CDSE)课程。2)使用创新的基于网络的技术，开发和实施学习评估工具，以衡量不同背景和背景的学生的成绩。3)开发、实施和测试扩展的CDSE教学网络，在该网络中，资源共享允许各种规模和类型的机构一致和可持续地提供CDSE课程。来自不同校区的教师将以同行教学/同行评审的模式配对进行课程设计和实施。包括来自不同机构的教师对确保：(1)每个教师将获得教授另一位教师最初开发的新课程的知识和经验；(2)同行对课程进行彻底审查和修订。该联盟将通过在线网络、新的联盟伙伴、会议和出版物分享其在建立机构间教学效率、本科生研究机会和学习评估方面的发现。

This project will develop a virtual department across four partner campuses to provide computer science education to students at campuses that are individually too small to support this kind of department. The new department will focus on the analysis of "big data" - large sets of computational and observational data - that are becoming increasingly prevalent in STEM. Cyber-learning techniques such as recorded lectures, archived materials, blog participation, and active learning approaches will be combined to offer a set of classes in big data science spanning meteorology, environmental science, biology and chemistry. By combining students from different campuses into the same courses, problems with minimal resources and limited potential enrollments on the individual campuses can be overcome. In particular, the project will focus on developing courses in biology and earth science, areas where students are not attracted by traditional computer science classes. The project will develop a flexible, blended learning model and effective learning assessment tools that can be implemented across multiple disciplines and institutions. The major goals and corresponding objectives of the project are to:1) Develop and implement high quality and relevant Computational and Data-Enabled Science and Engineering (CDSE) courses in mathematical modeling, data mining, genomics and bioinformatics, and problems in atmospheric and hydrospheric science using active learning and research-based teaching methodologies that promote inter-institutional and interdisciplinary collaboration. 2) Use innovative web-based technologies, to develop and implement learning assessment tools to gauge achievement of students from diverse backgrounds and contexts. 3) Develop, implement, and test an expanded CDSE pedagogical network in which resource sharing allows institutions of all sizes and types to consistently and sustainably offer CDSE coursework. Instructors from different campuses will be paired in a peer teaching/peer review model for course design and implementation. Including pairs of instructors from different institutions ensures that (1) each instructor will gain the knowledge and experience to teach a new course that is originally developed by the other instructor; and (2) the courses are thoroughly reviewed and revised by peers. The coalition will share its discoveries in building inter-institutional teaching efficiency, undergraduate research opportunities, and learning assessment via online networks, new coalition partners, conferences, and publications.