Integrating Data Science into Undergraduate Psychology Education: A Capacity-Building Workshop for Undergraduate Psychology Students

将数据科学融入本科心理学教育：本科心理学学生能力建设研讨会

• 批准号: 2229829
• 负责人: Darshon Reed
• 金额: $ 5万
• 依托单位: University of Central Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229829&HistoricalAwards=false
• 关键词: Integrating; Data; Science; into; Undergraduate

This project aims to serve the national interest by providing training in data science to undergraduate attendees at the Southwestern Psychological Association (SWPA) Conference. Attendees will come from two- and four-year institutions of higher education, and up to twenty five students will receive funding to take part. The purpose of the project is twofold: 1) to provide a data science education undergraduate-level workshop at the SWPA annual meeting, and 2) to increase diversity in data science by equipping diverse psychology students with data science skills. The primary focus of the undergraduate educational workshop will be to build participants’ knowledge of data science and increase their ability to employ data science methods in research. Skills that will be reviewed in the training include acquiring, visualizing, and managing data and performing specialized analyses. Using readily accessible, social media data (e.g., Twitter or Facebook) with broad applicability across interests, textual nature, and amenability to network analysis, workshop instructors will guide attendees through a high-level look at the process of developing a research proposal that utilizes big data. Additionally, attendees will learn how the study of psychology and data science can be mutually beneficial, and also gain exposure to career paths and options from a panel discussion involving data scientists. Enhancing psychology undergraduate training via data science education would provide an opportunity to examine and address validity and fairness issues in coding of natural language and algorithm training/validation. Natural language processing (NLP) is one of the fastest growing areas of machine learning research. Current linguistic machine learning models do an adequate job on language-understanding tasks, but the patterns learned in the data have been shown to produce algorithms that often express stereotypes and social biases. Stereotypes, prejudice, and implicit bias are a major focus of social psychological curriculum and training. If psychology education not only included theories and concepts surrounding these topics but also data science methodology, psychology students could conduct research that would assist in the optimization of machine learning models to reduce implicit biases. Thus, the proposed conference workshop will demonstrate the relationship between STEM learning in formal and informal settings by providing a unique informal setting (conference workshop) where student learning takes place but has not been assessed or compared to learning that happens in a typical classroom setting. Through the informal learning environment of the conference this workshop will (a) provide context and purpose to formal learning, (b) provide students opportunity and access to data science professionals, and (c) extend STEM content learning environments and student engagement. Participants knowledge development and attitude formation will be evaluated through direct measures of knowledge as well as qualitative methods. The findings from the assessment of this workshop will be developed into a mini-workbook and external-facing resource portal that will be shared broadly through the SWPA website and the American Psychological Association's educational directorates. The mini-workbook will also be shared with other psychological regional organizations and APA leadership to develop similar workshops and training for annual meetings and national conferences. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students.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.

该项目旨在通过为参加西南心理学协会(SWPA)会议的本科生提供数据科学培训来服务于国家利益。参与者将来自两年制和四年制高等教育机构，最多25名学生将获得参与资金。该项目的目的有两个：1)在SWPA年会上提供数据科学教育本科生水平的研讨会，2)通过让不同的心理学学生掌握数据科学技能来增加数据科学的多样性。本科生教育工作坊的主要重点将是培养学员的数据科学知识，并提高他们在研究中使用数据科学方法的能力。将在培训中复习的技能包括获取、可视化、管理数据和执行专门分析。使用易于访问的社交媒体数据(例如，Twitter或Facebook)，这些数据广泛适用于各种兴趣、文本性质和网络分析，研讨会讲师将指导与会者从高层次上了解利用大数据制定研究提案的过程。此外，与会者还将了解心理学和数据科学的研究如何互惠互利，并通过数据科学家参与的小组讨论了解职业道路和选择。通过数据科学教育加强心理学本科生培训将提供一个机会，以审查和解决自然语言编码和算法培训/验证中的有效性和公平性问题。自然语言处理(NLP)是机器学习研究中发展最快的领域之一。目前的语言机器学习模型在语言理解任务上做得很好，但从数据中学习的模式已被证明产生了经常表达刻板印象和社会偏见的算法。刻板印象、偏见和隐性偏见是社会心理学课程和培训的主要焦点。如果心理学教育不仅包括围绕这些主题的理论和概念，还包括数据科学方法，心理学专业的学生就可以进行有助于优化机器学习模型的研究，以减少内隐偏差。因此，拟议的会议讲习班将通过提供一个独特的非正式环境(会议讲习班)来展示STEM在正式和非正式环境中的学习之间的关系，学生在这里进行学习，但没有经过评估或与在典型课堂环境中进行的学习进行比较。通过会议的非正式学习环境，本讲习班将(A)为正式学习提供背景和目的，(B)为学生提供接触数据科学专业人员的机会和机会，以及(C)扩大STEM内容学习环境和学生参与。将通过直接的知识测量和定性方法对参与者的知识发展和态度形成进行评估。这次研讨会的评估结果将被开发成一个迷你练习册和面向外部的资源门户，将通过SWPA网站和美国心理协会的教育主管广泛分享。该迷你工作手册还将与其他心理区域组织和《行动纲领》领导层共享，以便为年度会议和国家会议举办类似的讲习班和培训。NSF IUSE：EHR计划支持研究和开发项目，以提高STEM教育对所有学生的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。