SBIR Phase I: eLearning --- Training and Enabling the Manufacturing Workforce: Computer Aided Decision Tools for Training and Assessment Optimization

SBIR 第一阶段：电子学习 --- 培训和支持制造劳动力：用于培训和评估优化的计算机辅助决策工具

• 批准号: 0637288
• 负责人: Chris Chiu
• 金额: --
• 依托单位: ACCU MEASUREMENT AND TESTING, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0637288&HistoricalAwards=false
• 关键词: SBIR; Phase; eLearning; Training; Enabling

This Small Business Innovation Research Phase I project sets out to improve the skills and knowledge of the manufacturing workforce and to maximize resources for retraining and reemploying the manufacturing workforce by means of the development of instructional and educational systems and assessment technology. Specifically, it sets out to solve a computational problem in raking, a methodology that (1) has a demonstrated potential to empower eLearning for the manufacturing industry, (2) can enable timely optimization of manufacturing training resources, (3) match skill sets of employees in shrinking industries to those in growing industries for purposes of unemployment reduction or prevention, (4) identify successful career paths, and (5) accurately assess complex science and engineering skills.. The proposed raking methodology aims to substantially reduce time and resources needed for analyzing empirical data collected in the manufacturing workforce. When integrating the methodology with learning systems, manufacturers will be able to target workforce development efforts at the K-16 level. The proposed methodology aims to solve large-scale raking problems and can be applied in multiple disciplines which use raking to solve problems needing computational intensive methodology (e.g., optimize training resources for sales representatives in the pharmaceutical and medicine manufacturing industry; empower collaborative learning and instruction of science related subjects via the Internet).

该小企业创新研究第一阶段项目旨在提高制造业劳动力的技能和知识，并通过开发教学和教育系统以及评估技术，最大限度地利用资源来再培训和再雇用制造业劳动力。具体来说，它旨在解决 raking 中的计算问题，这种方法 (1) 具有增强制造业电子学习能力的潜力，(2) 可以及时优化制造培训资源，(3) 将萎缩行业的员工技能与成长行业的员工相匹配，以减少或预防失业，(4) 确定成功的职业道路，(5) 准确评估复杂的科学和工程技能。 拟议的倾斜方法旨在大幅减少分析制造劳动力中收集的经验数据所需的时间和资源。当将该方法与学习系统相结合时，制造商将能够针对 K-16 级别的劳动力发展工作。所提出的方法旨在解决大规模的 raking 问题，并且可以应用于使用 raking 来解决需要计算密集型方法的问题的多个学科（例如，优化制药和医药制造行业销售代表的培训资源；通过互联网增强科学相关学科的协作学习和指导）。