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Targeted Infusion Project: Infusion of Computational Science Based Data Analytics Coursework and Research to Strengthen and Enrich the Lane College Undergraduate Chemistry Program

Targeted Infusion Project: Infusion of Computational Science Based Data Analytics Coursework and Research to Strengthen and Enrich the Lane College Undergraduate Chemistry Program
有针对性的注入项目:注入基于计算科学的数据分析课程和研究,以加强和丰富莱恩学院本科化学项目
批准号:
1912682
负责人:
Aminah Farrakhan-Gooch
金额:
$39.88万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-07-15 至 2023-06-30

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中文摘要
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英文摘要
The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Targeted Infusion Projects supports the development, implementation, and study of evidence-based innovative models and approaches for improving the preparation and success of HBCU undergraduate students so that they may pursue STEM graduate programs and/or careers. The project at Lane College will provide students with exposure to skill-building techniques that will benefit them throughout their matriculation as well as beyond their baccalaureate studies through introduction to computational chemistry and data analytics, research opportunities and scholarly engagement/dissemination opportunities. As the interest in STEM majors has grown over the years, so has the need for non-traditional, interdisciplinary preparation of students working towards the baccalaureate. The overall goal of the project is to increase the interest, preparation and inclusion of students from groups underrepresented in the field of chemistry, specifically computational chemistry and related areas. This goal will be accomplished by 1) exposing and training students in computational science techniques; 2) increasing the number of students conducting scientific research through mentored research experiences; 3) encouraging the engagement of undergraduate students into scientific discourse and dissemination of data and 4) increasing the number of students attending graduate school for a chemistry-related discipline. This project will serve as a case study on the preparation of students who, historically, have not been exposed to STEM education or research opportunities. These efforts will also increase the body of scientific knowledge in the areas of computational chemistry and data science.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.
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