Collaborative Research: BPE Track 2: Disability DCL - Capturing Narratives that Characterize Neurodivergent Strengths and Weaknesses

神经发散型（如多动症、自闭症、双相情感障碍）的学生为工程学带来了人们所期望的特征，如对细节的关注、创造力和独特的空间技能，但由于不同的障碍和挑战，他们更有可能从大学退学。通过理解所面临的优势和挑战，赋予神经发散性参与工程的能力，将极大地有利于解决复杂的全球和社会问题所需的创造性和创新性解决方案的产生。犹他州立大学和明尼苏达州立大学的国家科学基金会扩大参与工程合作项目将通过与社交媒体（例如Instagram， TikTok）上的神经分化社区接触，了解他们的优势和挑战，从而接近工程中的神经多样性。社交媒体是一个巨大的信息来源，社区在这里分享他们的生活经历，建立社区，更具体地说，在COVID-19隔离后，网上的神经发散性存在扩大了。神经分化的人分享他们的生活经历，彼此互动，产生共同的意义，从而产生新的解放语言，描述他们的经历，并将自己从基于缺陷的框架和刻板印象中解放出来。这样的语言包含了神经发散者所面临的优势和挑战，可以在工程教育中加以利用，以支持神经发散的学生。通过参与神经分化群体，可以在研究人员和神经分化人群之间建立信任，共同开发更好的学习环境和资源。此外，这种语言可以为高等教育提供信息，例如，撰写政策和开发学习便利，代表并潜在地增加神经分化学生的参与和学位完成。在工程方面，神经发散学生将分享他们在工程方面的故事，分享他们成功的生活经验，并提供应对机制来管理挑战。这些故事将被公开分享，以鼓励其他神经分化的学生在他们的研究中，并告知同行和教师关于工程中的神经分化。这个混合方法的合作项目将识别出描述工程学中神经发散学生的优势和挑战的解放性语言（神经发散指的是思维、功能和行为明显不同于社会“规范”的人，例如多动症、自闭症、精神分裂症）。该项目将首先实施社会媒体分析，以参与和识别公共神经分化社区中的解放语言。社交媒体平台（如TikTok、Instagram、Twitter）将被提取神经发散性相关内容，并使用自然语言处理技术（潜在狄利克雷分配和情感分析）进行分析。这种方法将揭示与神经发散性优势和挑战相关的共同语言。调查结果将用于制定下一阶段的优势和挑战面试方案。下一阶段将包括两项叙述性调查。第一个调查将是纵向的，犹他州立大学的24名神经发散性工程专业的学生将在一个学期内接受采访，以形成一个学期的长篇叙述。第二项调查将利用社交媒体在全国范围内招募神经分化工程专业的学生，以发展各种叙事。预期的结果将包括在神经分化和研究社区之间建立信任，开发词汇来描述和概念化工程中的神经分化，展示工程教育研究中的社会媒体分析和自然语言处理方法，并为神经分化工程专业的学生和工程教师开发资源和叙述库。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Neurodivergent (e.g., ADHD, autistic, bipolar) students bring desired characteristics such as attention to detail, creativity, and unique spatial skills to engineering, yet they are more likely to drop out of college due to different barriers and challenges. Empowering neurodivergent participation in engineering by understanding both the strengths and challenges faced will significantly benefit the generation of creative and innovative solutions needed to solve complex global and societal problems. This National Science Foundation’s Broadening Participation in Engineering collaborative project at Utah State University and Minnesota State University will approach neurodiversity in engineering by engaging with the neurodivergent community on social media (e.g., Instagram, TikTok) to understand their strengths and challenges. Social media is a vast information source where communities share their lived experiences and build community, and more specifically, the neurodivergent presence online expanded after the COVID-19 quarantine. Neurodivergent people share their lived experiences and interact with one another to generate shared meaning resulting in new emancipatory language that describe their experiences and emancipate themselves from deficit-based framing and stereotypes. Such language embraces both strengths and challenges faced by neurodivergent people which can be leveraged in engineering education to support neurodivergent students. By engaging with neurodivergent communities, trust can be built between researchers and neurodivergent people to work together to develop better learning accommodations and resources. Further, this language can be informative to higher education, for example, to write policy and develop learning accommodations that represent and potentially increase participation and degree completion of neurodivergent students. In engineering, neurodivergent students will share their stories in engineering to share their lived experiences such as successes and offer coping mechanisms to manage challenges. These stories will be shared publicly to encourage other neurodivergent students in their studies and inform peers and faculty about neurodivergence in engineering. This mixed-methods, collaborative project will identify emancipatory language that describe the strengths and challenges of neurodivergent students in engineering (neurodivergent refers to people who think, function, and behave significantly different from societal “norms,” e.g., ADHD, autism, schizophrenia). This project will first implement social media analytics to engage with and identify emancipatory language in the public neurodivergent community. Social media platforms (e.g., TikTok, Instagram, Twitter) will be scraped for neurodivergent related content and analyzed using natural language processing techniques (latent Dirichlet allocation and sentiment analysis). This method will reveal common language associated with neurodivergent strengths and challenges. Findings will then be used to develop a strengths and challenges interview protocol for the next phase. The next phase will consist of two narrative inquiries. The first inquiry will be longitudinal where 24 neurodivergent engineering students at Utah State University will be interviewed over a semester to develop semester long narratives. The second inquiry will leverage social media to recruit neurodivergent engineering students across the nation to develop a variety of narratives. Expected outcomes will include building trust between neurodivergent and research communities, developing a lexicon of words to describe and conceptualize neurodivergence in engineering, demonstrating social media analytics and natural language processing methods in engineering education research, and developing a resource and narrative depository for neurodivergent engineering students and engineering faculty.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.