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

该项目为数据化学领域的劳动力发展建立了一个可扩展和可持续的培训平台，名为C2D（化学数据科学家的网络培训）。机器学习（ML）的使用将彻底改变合成化学和许多依赖它的领域，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，现有和未来的员工需要考虑到他们的培训要求、背景和学习偏好的多样性的培训。C2D是一个自适应和个性化的培训平台，需要根据学习者的进度和自动评估他们的学习状态从策划的学习材料中选择个性化的任务。它以满足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、工业界和普通民众。具体的征聘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将使当前和未来的劳动力能够在合成化学中使用ML。Cybertraining for Chemical Data scientists（C2D）平台将自适应学习和评估与个性化建议相结合，以提供有关机器学习在合成化学中应用的培训材料，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结果，旨在围绕最新的心理测量原理和推荐系统开发C2D，以提供个性化教学。从涉及领域专家的调查和焦点小组分析开始，C2D将制定一个蓝图，用于策划课程材料，并将其输入基于连续自适应评估的个性化推荐系统。C2D与许多学术和工业合作伙伴合作，以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素养和化学特定的网络技能相结合，使ML方法在化学中得到广泛采用，并确保依赖合成化学的部门的持续经济竞争力。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.