Collaborative Research: Developing Online Laboratories for Computer Science Students to Learn How to Build Accessible Software and to Use Artificial Intelligence/Machine Learning

该项目旨在通过开发灵活的在线实验室来服务于国家利益，帮助计算机科学本科生学习重要技能，包括设计可访问的软件和使用人工智能。具体地说，该项目旨在创建、评估和传播自给自足的计算机科学实验室，了解如何为认知障碍、听力损失、视力障碍和其他残疾人士开发可访问的软件。它还旨在创建类似的在线实验室，帮助计算机科学专业的学生学习人工智能/机器学习。在在线实验室的设计中，该项目旨在开发一种模块化的形式，可以在多个主题中确定对学生和教师都有好处的功能。这些实验将基于真实世界的场景，将使用体验式学习模式，并将从多个角度探讨基础主题。将这些日益重要的主题纳入课程可能具有挑战性，特别是对资源有限的机构而言。该项目的在线实验室方法可以通过提供独立且易于采用的学习资源来提供帮助。事实上，采用这些实验只需要一个网络浏览器，该项目将为教师提供辅助材料，如课程幻灯片和教学视频，以帮助他们成功教授该主题。该项目的研究工作将侧重于在线实验室的设计和传播，以更好地了解如何促进其他机构的采用。这项研究还将检查(在可访问性实验室中)包含建立同理心的内容和(在人工智能实验室中)包含伦理内容对学生用户的影响。这个由罗切斯特理工学院和白求恩-库克曼大学合作的项目将为计算机科学本科生开发十个独立的实验室：五个研究人工智能/机器学习，五个研究可访问软件的开发。除了这些实验室的发展，主要项目目标包括：(1)开发一个可应用于计算机科学其他领域的独立实验室的统一框架；(2)评估具体的实验室和更广泛的框架，同时考虑到学生用户和教师采用者；以及(3)广泛传播实验室和共享框架，包括强大的数字培训机会。该项目的混合方法研究和评估计划将从学生焦点小组和调查、教师访谈和学生学习评估中收集数据，这些数据将与参与课程的学生的结果进行比较，这些课程使用不同的既定机制教授类似的内容。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。该项目是一个参与式的学生学习项目，IUSE计划通过该项目支持有前景的实践和工具的创建、探索和实施。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。

This project aims to serve the national interest by developing flexible online laboratories to help undergraduate computer science students learn important skills, including design of accessible software and use of artificial intelligence. Specifically, the project aims to create, evaluate, and disseminate self-contained computer science laboratories about how to develop accessible software for individuals with cognitive impairments, hearing loss, vision impairment, and other disabilities. It also aims to create similar online laboratories to help computer science students learn about artificial intelligence/machine learning. In design of the online labs, the project intends to develop a modular format that can identify beneficial features for both students and faculty across multiple topics. The labs will be based on real-world scenarios, will use an experiential learning model, and will address foundational topics from multiple angles. Including these increasingly essential topics in curricula can be challenging, especially for institutions with resource constraints. The project’s online lab approach can help by providing learning resources that are self-contained and easily adoptable. In fact, adopting the labs will require only a web browser, and the project will provide instructors with supplemental materials such as lecture slides and instructional videos to help them successfully teach the topic. The project’s research efforts will focus on the design and dissemination of the online labs to better understand how to facilitate adoption at other institutions. The research effort will also examine how inclusion of empathy-building content (in the accessibility labs) and ethics content (in the artificial intelligence labs) impact student users. This collaborative project from Rochester Institute of Technology and Bethune-Cookman University will develop ten self-contained labs for undergraduate computer science students: five addressing artificial intelligence/machine learning and five addressing the development of accessible software. Beyond the development of these labs, major project goals include: (1) the development of a unified framework for self-contained labs that can be applied to other areas of computer science; (2) evaluation of the specific labs and the broader framework, considering both student users and faculty adopters; and (3) broad dissemination of the labs and the shared framework, including robust digital training opportunities. The project’s mixed-methods research and evaluation plans will collect data from student focus groups and surveys and instructor interviews and assessments of student learning that will be compared to outcomes of students engaged in courses that use different, established mechanisms to teach similar content. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is an Engaged Student Learning project through which the IUSE program supports the creation, exploration, and implementation of promising practices and tools.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.