Promoting the Development of STEM Tech Employability Skills: A Review of Practices and Needs in the ATE Community

认识到高素质技术人员队伍的重要性，行业和教育领导者期待社区和技术学院的技术人员计划的创新，经验教训，最佳实践，以及培养关键学术和职业技能（非技术技能）的指导，这些技能有助于国家科学，技术，工程和数学（STEM）基础设施的健康和安全。先进技术教育（ATE）领域的从业者和政策制定者迫切需要重新设想学习、环境和工作场所，以更好地支持个人建立关键和复杂的非技术就业技能。基于对研究和实践文献的回顾，与解决问题相关的认知就业技能似乎享有相对强大的投资水平和可见性，但与人际关系技能和应对和保持积极“心态”（例如，积极的态度，毅力和灵活性）的个人技能相关的就业技能可能非正式地和不受关注。然而，与技术技能不同，就业技能是不明确的。定性研究的结果将为教育从业者、政策制定者和雇主提供基于研究的指导，以便就如何促进就业技能的发展和倡导所需资源做出明智的决定。为了解决技术教育学术和职业发展方面的文献差距，目标研究项目地址将以定性研究研究为特色，检查和综合有关就业技能的文献和专家知识。探索性实证研究主要探讨三个问题。什么是就业技能？在研究中确定的学习原则，以及为什么它们在技术环境中很重要，例如信息技术（IT）和先进制造（AM）？在开发和衡量IT和AM的就业技能方面，有哪些做法？这些做法如何具体支持代表性不足群体的需求？在劳动力规划实践、研究和政策中，未来发展和评估就业技能的最有希望的方向是什么？将使用三种定性研究方法来回答研究问题。这一总体方法复制了之前对支持长期失业和就业不足的个人以及未被充分代表的少数民族成人学生接受高等教育和初级就业的一般计划的调查所采用的方法。叙述性研究综合是由Cooper（1998,2006）和Campbell及其同事（1992）提出的研究综合的概念化所决定的。与劳动力发展、技术人员教育和行业选定行业领域专家的访谈将加深对就业技能的理解。对研究和专家知识综合的系统检查和迭代改进将评估数据饱和度（例如，了解需要多少次访谈才能为可复制的发现提供足够的代码和主题），并确保支持稳定数据类别的发展。

Recognizing the importance of a highly qualified technician workforce, industry and education leaders look to community and technical colleges' technician programs for innovations, lessons learned, best practices, and guidance on cultivating critical academic and career skillsets (non-technical skills) that contribute to the health and security of the nation's Science, Technology, Engineering, and Mathematics (STEM) infrastructure. Practitioners in the Advanced Technological Education (ATE) community and policy makers face an urgency to re-envision learning and environments and workplaces to better support individuals in building critical and complex non-technical employability skillsets. Based on a review of the research and practitioner literature, the cognitive employability skills associated with problem solving appear to enjoy a relatively robust level of investment and visibility, but the employability skills associated with interpersonal skills and the intrapersonal skills of coping and maintaining a positive "mindset" (e.g., positive attitude, persistence, and flexibility) are likely to occur informally and under the radar. However, unlike technical skills, employability skills are ill defined. Findings from the qualitative research study will yield research-based guidance for practitioners in education, policy makers, and employers to make informed decisions on how to promoted the development of employability skills and to advocate for needed resources. Addressing a gap in the literature on academic and career development in technician education, the targeted research project addresses will feature a qualitative research study that examines and synthesizes the literature and expert knowledge on employability skills. Three questions are investigated in the exploratory empirical research study. What are the employability skills? learning principles identified in the research and why are they important in technical contexts, such and Information Technology (IT) and Advanced Manufacturing (AM)? What are the practices used to develop ad measure employability skills in IT and AM and how do the specifically support the needs of underrepresented groups? What are the most promising directions for future development and evaluation o employability skills in workforce programming practice, research, and policy? Three qualitative research methods will be used to answer the research questions. This overall approach replicates the approach one taken in a previous investigation into general programs for supporting chronically unemployed and underemployed individuals and underrepresented minority adult students in post-secondary education and entry-level employment. The narrative research synthesis is informed by the conceptualization of research syntheses proposed by Cooper (1998, 2006) and Campbell and associates (1992). The Interviews with expert informants in workforce development, technician education, and the industry selected industry sectors will deepen an understanding of employability skills. The systematic examination and iterative refinement of the synthesis of the research and expert knowledge will assess data saturation (e.g., knowing how many interviews are needed to provide sufficient codes and themes for replicable findings) and ensure support the development of stable data categories.