Using Evidence-Based Educational Practices to Prepare and Support Highly Qualified Students with Financial Need for Careers in Computing and Cyber-Security

该项目将通过德克萨斯州和加利福尼亚州的两所四年制院校和两所合作的两年制学院组成的联盟建立一个奖学金计划：德克萨斯大学埃尔帕索分校(UTEP)、加州州立大学斯坦尼斯劳斯分校(CSU-S)、埃尔帕索社区学院(EPCC)和默塞德学院(MC)。该项目将培养低收入、有学术天赋的学生进入计算机科学和网络安全等高需求领域的工作。S-STEM学者将参加联合课程活动，积极参与专业发展活动，支持他们通过本科学习，特别是从两年制大学到四年制院校，再到高级学位。该项目将借鉴由国家科学基金会(CNS-1042341)资助的拉美裔服务机构计算联盟的有效做法和战略。作为CAHSI的牵头机构，UTEP将根据CAHSI支持学生成功的成熟实践，协调该项目活动的实施。这些活动包括：专注于学生专业、沟通和研究/工作技能发展的亲和力研究模式；提供获取资源、信息和指导的指导；以及CAHSI峰会，为学生提供与行业代表建立网络、参加技术研讨会以及为面试和实习做准备的机会。该项目将有一个专门的网站，由每个参与的四年制院校托管，并将使用社交媒体来接触潜在的申请者。此外，每所四年制院校将采用面对面的招聘活动，包括每年为期两天的活动，面向来自地区高中、大学和合作两年制学院的潜在申请者。该活动将包括高度互动的活动，以培养人们对计算机学位的兴趣，并讨论职业道路，包括工业、政府、非政府组织和创业机会的工作。这些活动还将强调高级教育的重要性。该项目将调查以证据为基础的综合实践与目标学生群体的有效性，并将有助于现有的知识库，对导致招收、留住和获得目标学生群体的计算机学位的因素有新的理解。特别是，它将确定学生在多大程度上将网络安全作为专业领域，以及哪些活动对学生特别有吸引力。这项研究将利用社会文化、情境学习的视角来考察S-STEM学者如何发展和扩大计算实践社区。研究方法包括：参与者观察法、半结构式个人和团体访谈法、文献分析法(包括机构研究数据分析)和调查法。分析将包括归纳和演绎的编码实践，持续的比较分析，以及对学生成绩差异的统计分析(例如，转化率、GPA、研究生入学意向)。这项研究研究将向教育研究和计算机科学社区通报创建计算机包容性学习环境所需的具体要素，同时还将建立一个急需的关于各种因素的知识基础，以促进循证改革的采用和可持续性，这些改革服务于低收入、高资质的计算学生。

This project will establish a scholarship program through a consortium of two four-year institutions in Texas and California and two partner two-year colleges: the University of Texas at El Paso (UTEP), California State University-Stanislaus (CSU-S), El Paso Community College (EPCC), and Merced College (MC). The project will prepare low-income, academically talented students to enter the workforce in the high demand areas of computer science and cyber-security. S-STEM scholars will participate in co-curricular activities that actively engage them in professional development activities that support their trajectories through undergraduate studies, in particular from two-year colleges to four-year institutions, and onto advanced degrees. The project will build on the effective practices and strategies of the Computing Alliance of Hispanic-Serving Institutions (CAHSI), funded by NSF (CNS-1042341). As the lead institution of CAHSI, UTEP will coordinate the implementation in this project of activities based on CAHSI's proven practices that support student success. Such activities include: the Affinity Research model that focuses on student professional, communication, and research/workplace skills development; mentoring that provides access to resources, information, and guidance; and the CAHSI Summit that provides opportunities for students to network with industry representatives, participate in technical workshops, and prepare for interviews and internships. The project will have a dedicated website hosted by each participating four-year institution, and it will use social media to reach potential applicants. In addition, each four year institution will employ face-to-face recruitment activities that include an annual two-day event for a pool of potential applicants from area high schools, the university, and partnering two-year colleges. The event will include highly interactive activities to build interest in computing degrees and discuss career pathways, including jobs in industry, government, non-governmental organizations, and entrepreneurial opportunities. The activities will also stress the importance of advanced education.The project will investigate the effectiveness of integrated evidence-based practices with the targeted student population and will contribute to the existing knowledge base with new understandings regarding factors that lead to the recruitment, retention, and degree attainment of the target population of students in computing. In particular, it will identify to what extent students pursue cyber-security as an area of expertise and which activities are particularly compelling for students. The research will utilize a sociocultural, situated learning lens to examine how S-STEM scholars develop and expand communities of computing practice. Research methods include the following: participant observation, semi-structured individual and group interviews, document analysis (including institutional research data analysis), and survey methods. Analysis will involve inductive and deductive coding practices, constant comparative analysis, and statistical analysis of differences in student outcomes (e.g., transfer rate, GPA, intentions for graduate school). The research study will inform the education research and CS communities about the specific elements that are needed to create inclusive learning environments in computing, while also building a much-needed foundation of knowledge on factors that facilitate the adoption and sustainability of evidence-based reforms that serve low income, highly qualified students in computing.