Collaborative Research to Understand the Role of Culture, Identity, Epistemology and Bi-Cultural Efficacy in American Indian Educational and Professional Success in STEM

合作研究了解文化、身份、认识论和双文化功效在美洲印第安人 STEM 教育和职业成功中的作用

• 批准号: 1251516
• 负责人: Douglas Medin
• 金额: $ 31.2万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1251516&HistoricalAwards=false
• 关键词: Collaborative; Research; Understand; Role; Culture

This project is an empirical research study using a resiliency-based framework to investigate the factors that contribute to American Indian and Alaska Native (AI-AN) success and achievement in STEM education and careers. The focus is on what makes people successful rather than what makes them fail. It was developed through a partnership between the American Indian Science and Engineering Society (AISES), the Office for Community Health (OCH) at the University of New Mexico (UNM), and Northwestern University. The project is also interdisciplinary, partnering researchers from Anthropology, Psychology and Economics/Public Health with AISES to contribute a unique perspective on Native participation in STEM. The aim of this research is to identify the role of culture, individual identity, epistemology and bi-cultural efficacy in this process. The guiding hypothesis is that AI-AN success in STEM is influenced by dynamics of culture, epistemology and individual identity, with the role of bi-cultural efficacy being particularly significant. The researchers are interested in knowing: (a) What factors positively influence AI-AN success in STEM?; (b) What role do the dynamics of culture, epistemology and individual identity play in AI-AN success in STEM and how are these dynamics played-out in individual experience?; and (c) Does bi-cultural efficacy play a role in this process, and if so, what is that role? The proposed collaboration uses a mixed methods design, beginning with an ethnographic approach that will build on advances in knowledge from the extensive literature on deficits and barriers, and nascent research on epistemology, adding new empirical data. The research consciously privileges voices of Native scholars whose experience is not well represented in mainstream perspectives or taken into consideration by policy makers. Adaptive project design through iterative data collection and analysis with synthesis and incorporation of findings from different components ongoing throughout the study will allow for dynamic inclusion of participant input. The study includes four separate but integrated research components: (1) 75 in-depth ethnographic interviews with 25 AI-AN STEM professionals and AISES members, conducted in three iterative sets of 25 each; (2) Three AI-AN undergraduate Student Research Scientists (SRS) from UNM will be mentored in the conduct of 36 peer interviews (12 each) with other AI-AN students; (3) Secondary analysis of unique AISES organizational archive with 35 years of information on more than 6000+ AI-AN involved in STEM; and (4) a preliminary survey will be conducted in year three of the project with 25 AISES members to validate measures of bi-cultural efficacy that contribute to AI-AN success in STEM. The Principal Investigators will mentor Native scholars to develop protocols, collect and analyze data, present findings, and participate as members of the Research Team. A Project Summit in Year Three will disseminate project findings to a broad group of AISES stakeholders. This project will also structure a new role for AISES to play in the STEM community by leveraging the unique position of AISES as a national AI-AN STEM organization and developing data collection protocols and data collection tools the organization can continue to use for research in the future. Results of this research will increase understanding of how AI-AN individuals leverage personal and cultural assets in a way that embraces a congruency between Indigenous culture (Native science) and Western science as they achieve success in STEM. This information will contribute to the literature analyzing issues in AI-AN education and under-representation in STEM and indicate directions for future research. Most importantly, this research may lay the foundation for increasing the proportion of AI-AN scientists. Factors that contribute to success and achievement of AI-AN in STEM are often unrecognized, underappreciated or poorly understood. The improved coherence of interventions that will result from better conceptualization of the strengths and needs of AI-AN students will provide a roadmap for developing best-practice and model-driven programming within universities, improve AI-AN educational outcomes in STEM, and in turn, contribute to improvements in AI-AN individual and community well-being.

本项目是一项实证研究，使用基于弹性的框架来调查影响美国印第安人和阿拉斯加原住民（AI-AN）在STEM教育和职业生涯中取得成功和成就的因素。重点是什么让人们成功，而不是什么让他们失败。它是通过美国印第安人科学与工程学会（AISES）、新墨西哥大学社区卫生办公室（OCH）和西北大学之间的伙伴关系开发的。该项目也是跨学科的，来自人类学、心理学和经济学/公共卫生的研究人员与AISES合作，为当地人参与STEM提供独特的视角。本研究的目的是确定文化、个人认同、认识论和双文化效能在这一过程中的作用。指导性假设是AI-AN在STEM中的成功受到文化、认识论和个人身份的动态影响，其中双文化效能的作用尤为显著。研究人员感兴趣的是：(a)哪些因素对AI-AN在STEM中的成功产生积极影响？(b)文化、认识论和个人身份的动态在AI-AN在STEM中的成功中发挥了什么作用，这些动态在个人经验中是如何发挥作用的？(c)双文化效能在这一过程中是否起作用，如果起作用，作用是什么？拟议的合作使用混合方法设计，从民族志方法开始，该方法将建立在关于缺陷和障碍的广泛文献的知识进步和认识论的新生研究的基础上，增加新的经验数据。研究有意识地给予本土学者以特权，而这些学者的经历在主流观点中没有得到很好的体现，也没有得到政策制定者的考虑。通过反复的数据收集和分析，综合和整合整个研究过程中不同组成部分的发现，进行适应性项目设计，将允许动态纳入参与者的输入。该研究包括四个独立但综合的研究组成部分：(1)对25名AI-AN STEM专业人员和AISES成员进行75次深入的民族志访谈，分三组进行，每组25人；(2)三名来自新墨西哥大学的AI-AN本科生研究科学家（SRS）将接受指导，与其他AI-AN学生进行36次同行访谈（每人12次）；(3)对参与STEM的6000多个AI-AN的35年信息的独特AISES组织档案进行二次分析；(4)在项目的第三年，将与25名AISES成员进行初步调查，以验证有助于AI-AN在STEM中取得成功的双文化效能措施。首席研究员将指导当地学者制定协议，收集和分析数据，提出研究结果，并作为研究小组成员参与。第三年的项目高峰会将向广泛的AISES持份者传播项目成果。该项目还将利用AISES作为国家AI-AN STEM组织的独特地位，为AISES在STEM社区中发挥新的作用，并开发数据收集协议和数据收集工具，该组织可以在未来继续用于研究。本研究的结果将增加对ai - a个体如何在获得STEM成功的过程中，以一种包含土著文化（本土科学）和西方科学之间一致性的方式利用个人和文化资产的理解。这些信息将有助于文献分析ai - a教育和STEM中代表性不足的问题，并为未来的研究指明方向。最重要的是，这项研究可能为增加AI-AN科学家的比例奠定基础。有助于AI-AN在STEM中取得成功和成就的因素往往未被认识、低估或理解不足。通过更好地概念化AI-AN学生的优势和需求，将提高干预措施的一致性，这将为在大学内开发最佳实践和模型驱动的编程提供路线图，改善STEM领域的AI-AN教育成果，进而有助于改善AI-AN个人和社区福祉。