CyberTraining: Implementation: Medium: C2D - Cybertraining for Chemical Data scientists

网络培训：实施：媒介：C2D - 化学数据科学家的网络培训

• 批准号: 2321054
• 负责人: Xiangliang Zhang
• 金额: $ 100万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321054&HistoricalAwards=false
• 关键词: CyberTraining; Implementation; Medium; C2D; Cybertraining

The project establishes a scalable and sustainable training platform for workforce development in data chemistry, named C2D (Cybertraining for Chemical Data scientists). The use of machine learning (ML) will revolutionize synthetic chemistry and many fields that depend on it, including materials and energy science, information technology and health sciences. To enable this transformation, the existing and future workforce needs training that takes their diversity of training requirements, backgrounds, and learning preferences into account. The C2D is an adaptive and personalized training platform, entailing personalized task selection from curated learning materials based on learners' progress and automated assessment of their learning status. It organizes training videos, reading, and assessments in a way that is responsive to the needs of the learners and will be offered to the academic, industrial, and general population free of charge. Specific recruitment mechanisms will ensure the participation of underrepresented groups. Using a combination of online and in-person training, C2D will empower the current and future workforce to use ML in synthetic chemistry. The Cybertraining for Chemical Data scientists (C2D) platform integrates adaptive learning and assessment with personalized recommendations for training materials regarding the application of machine learning in synthetic chemistry, thereby enabling personalized instruction in the field of data chemistry. The platform is the result of an interdisciplinary collaboration of psychologists, computer scientists and chemists, and aims to develop C2D around the latest psychometric principles and recommender systems for providing personalized instruction. Starting from a survey and focus groups analysis involving domain experts, the C2D will develop a blueprint for curating curricular material that is fed into a personalized recommender system based on continuous adaptive assessment. The C2D works with a number of academic and industrial partners to ensure the wide adoption and sustainability of the platforms. It will promote the development of research workforce integrating core ML literacy and chemistry-specific cyber skills to enable the wide adoption of ML methods in chemistry and ensure the continued economic competitiveness of the sectors that depend on synthetic chemistry.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.

该项目为数据化学领域的劳动力发展建立了一个可扩展且可持续的培训平台，名为 C2D（化学数据科学家网络培训）。机器学习 (ML) 的使用将彻底改变合成化学以及依赖它的许多领域，包括材料和能源科学、信息技术和健康科学。为了实现这一转变，现有和未来的劳动力需要接受培训，考虑到他们的培训要求、背景和学习偏好的多样性。 C2D 是一个自适应和个性化培训平台，需要根据学习者的进度和对其学习状态的自动评估，从精选的学习材料中选择个性化任务。它以响应学习者需求的方式组织培训视频、阅读和评估，并将免费提供给学术界、工业界和普通大众。具体的招募机制将确保代表性不足的群体的参与。通过结合在线和现场培训，C2D 将使当前和未来的员工能够在合成化学中使用机器学习。化学数据科学家网络培训 (C2D) 平台将自适应学习和评估与有关机器学习在合成化学中应用的培训材料的个性化建议相结合，从而实现数据化学领域的个性化教学。该平台是心理学家、计算机科学家和化学家跨学科合作的成果，旨在围绕最新的心理测量原理和推荐系统开发 C2D，以提供个性化指导。从涉及领域专家的调查和焦点小组分析开始，C2D 将开发一个用于策划课程材料的蓝图，该蓝图将输入到基于持续自适应评估的个性化推荐系统中。 C2D 与许多学术和工业合作伙伴合作，确保平台的广泛采用和可持续性。它将促进研究人员的发展，将核心 ML 素养和特定于化学的网络技能相结合，以实现 ML 方法在化学领域的广泛采用，并确保依赖合成化学的行业的持续经济竞争力。该奖项反映了 NSF 的法定使命和通过使用基金会的智力优点和更广泛的影响审查标准进行评估，该项目被认为值得支持。