CPATH-1: Collaborative Research: a Verification-Driven Learning Model that Enriches CS and Related Undergraduate Programs
CPATH-1:协作研究:丰富计算机科学和相关本科课程的验证驱动学习模型
基本信息
- 批准号:0939102
- 负责人:
- 金额:$ 4.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2009
- 资助国家:美国
- 起止时间:2009-09-01 至 2012-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Computer science foundation-building courses at the freshman and sophomore levels pose the greatest stumbling blocks to undergraduate students' learning. CS student enrollment has declined over 60% nationwide, even though the demand for skilled professionals was increasing. There is an urgent need for a more effective CS learning model. The main purpose of this research is to enrich the context of the CS learning process which is important from motivational and educational perspectives. This project investigates a verification-driven learning model that facilitates students' involvement in real-world computing tasks starting from their early computing courses and continuing throughout their entire studies in computing. This model can significantly reduce the prerequisites for students to study real-world problems in their early years. The students are tasked to validate the functionality of software, execute programs, test parts of systems (pre-decomposed subsystems and components), and locate possible errors. Such seemingly complex high-level tasks can be done by novice students because software verification does not require design and implementation, and can be turned into a learn-by-example process with adequate preparation. This kind of preparation is wrapped in a Verification-Driven Learning Case, which defines the configuration to support a verification-driven learning activity, and consists of elements such as the justification of the system's existence, the requirement specification, description of the functionality, a set of test cases, and the decomposition of the system. The foundation of the verification-driven learning model lies in software testing theories and techniques. Frequent and progressive exercises on verification will prepare the students for formal specifications. To realize the learning model, this project will produce Learning Cases based on faculty research including computer security, bioinformatics, geographic information systems, database and data mining techniques, remote sensing, and fuzzy set techniques. The Learning Cases will expose the students to working software systems that serve a real-world purpose in scientific research, engineering development, or social networks. This project will particularly advocate computer science education in under-represented minority and woman students. This learning approach will also help adult students who have rich experience in various areas but need to reposition themselves in the work force. The final goal of this project is to revitalize the CS programs and produce more competent graduates capable of computational thinking.
一年级和二年级的计算机科学基础课程是本科生学习的最大绊脚石。尽管对熟练专业人员的需求在增加,但全国CS学生的入学人数下降了60%以上。迫切需要一种更有效的CS学习模式。本研究的主要目的是丰富CS学习过程的背景,从动机和教育的角度来看,CS学习过程具有重要意义。这个项目研究了一种验证驱动的学习模式,该模式有助于学生从他们早期的计算课程开始,一直持续到他们在计算方面的整个学习过程,参与真实世界的计算任务。这种模式可以显著降低学生在早年学习现实世界问题的先决条件。学生的任务是验证软件的功能,执行程序,测试系统的部件(预先分解的子系统和组件),并定位可能的错误。这种看似复杂的高级任务可以由新手学生来完成,因为软件验证不需要设计和实现,并且可以在充分准备的情况下变成一个样例学习的过程。这种准备被包装在验证驱动的学习用例中,该用例定义了支持验证驱动的学习活动的配置,并且由诸如系统存在的合理性、需求规范、功能描述、一组测试用例以及系统的分解等元素组成。验证驱动学习模型的基础在于软件测试理论和技术。频繁和渐进的验证练习将使学生为正式规范做好准备。为了实现学习模式,该项目将根据教师的研究产生学习案例,包括计算机安全、生物信息学、地理信息系统、数据库和数据挖掘技术、遥感和模糊集技术。学习案例将使学生接触到在科学研究、工程开发或社交网络中服务于现实世界目的的工作软件系统。该项目将特别提倡在代表不足的少数民族和女学生中开展计算机科学教育。这种学习方式也将帮助在各个领域拥有丰富经验但需要在劳动力中重新定位的成人学生。该项目的最终目标是振兴CS课程,培养更多有能力进行计算思维的毕业生。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Andrea Edwards其他文献
Projection Dual Averaging Based Second-order Online Learning
基于投影对偶平均的二阶在线学习
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Zhong Chen;Huixin Zhan;Victor S. Sheng;Andrea Edwards;Kun Zhang - 通讯作者:
Kun Zhang
An effective cost-sensitive sparse online learning framework for imbalanced streaming data classification and its application to online anomaly detection
一种有效的成本敏感的稀疏在线学习框架,用于不平衡流数据分类及其在在线异常检测中的应用
- DOI:
10.1007/s10115-022-01745-x - 发表时间:
2022 - 期刊:
- 影响因子:2.7
- 作者:
Zhong Chen;Victor S. Sheng;Andrea Edwards;Kun Zhang - 通讯作者:
Kun Zhang
Neurovascular Interactions in the Neurologically Compromised Neonatal Brain.
神经受损的新生儿大脑中的神经血管相互作用。
- DOI:
10.1007/978-1-4939-3023-4_61 - 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Harsimrat Singh;R. Cooper;Chuen;Laura Dempsey;Sabrina Brigadoi;Andrea Edwards;D. Airantzis;N. Everdell;A. Michell;D. Holder;Topun Austin;J. Hebden - 通讯作者:
J. Hebden
Andrea Edwards的其他文献
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{{ truncateString('Andrea Edwards', 18)}}的其他基金
Scholarships to Increase Diversity and Achievement in Computer Sciences, Mathematics, and Physics Majors
提高计算机科学、数学和物理专业多样性和成就的奖学金
- 批准号:
0807105 - 财政年份:2008
- 资助金额:
$ 4.5万 - 项目类别:
Standard Grant
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