NSF Award
2321054
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. <br/><br/>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.<br/><br/>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.
