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

该项目将通过得克萨斯州和加州的两个四年制机构和两个合作伙伴两年制学院的联盟建立奖学金计划：得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）。该项目将为低收入、学术才华的学生做好准备，让他们进入计算机科学和网络安全等高需求领域的劳动力市场。S-STEM学者将参加课外活动，积极参与专业发展活动，支持他们通过本科学习的轨迹，特别是从两年制大学到四年制大学，并获得高级学位。该项目将建立在西班牙裔服务机构计算联盟（CAHSI）的有效做法和战略的基础上，由NSF（CNS-1042341）资助。 作为CAHSI的牵头机构，UTEP将根据CAHSI支持学生成功的行之有效的做法，协调该项目活动的实施。这些活动包括：亲和研究模型，侧重于学生的专业，沟通和研究/工作场所技能的发展;提供资源，信息和指导的指导;和CAHSI峰会，为学生提供与行业代表建立联系的机会，参加技术研讨会，并为面试和实习做准备。该项目将有一个专门的网站，由每个参与的四年制机构主办，它将使用社交媒体来接触潜在的申请人。此外，每个四年制院校将采用面对面的招聘活动，其中包括每年为期两天的活动，为来自地区高中，大学和合作两年制学院的潜在申请人提供服务。该活动将包括高度互动的活动，以建立对计算学位的兴趣，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等教育的重要性，该项目将调查与目标学生群体相结合的循证实践的有效性，并将对导致目标学生群体在计算机领域的招聘、保留和学位获得的因素有新的理解，从而有助于现有的知识基础。特别是，它将确定学生在多大程度上追求网络安全作为一个专业领域，以及哪些活动对学生特别有吸引力。该研究将利用社会文化，情境学习透镜，研究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、半结构化的个人和团体访谈法、文献分析法（包括机构研究数据分析法）和问卷调查法。分析将涉及归纳和演绎的编码实践，不断的比较分析，以及对学生成绩差异的统计分析（例如，转学率，GPA，研究生院意向）。该研究将告知教育研究和CS社区创建计算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知识基础，促进采用和可持续的循证改革，为低收入，高素质的学生提供服务。

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.