SBIR Phase I: An online learning and assessment platform for sophisticated and secure exams

SBIR 第一阶段：用于复杂且安全考试的在线学习和评估平台

• 批准号: 2304241
• 负责人: Nathan Walters
• 金额: $ 27.5万
• 依托单位: PRAIRIELEARN, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304241&HistoricalAwards=false
• 关键词: SBIR; Phase; online; learning; assessment

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to provide a robust and sophisticated assessment tool to a wider range of STEM (Science, Technology, Engineering and Mathematics) educators to improve student learning, make teaching more efficient, and reduce the incidences of cheating. The core technology of this technology is an online platform for creating and delivering high-quality assessments that are auto-graded by artificial intelligence (AI) algorithms, providing immediate feedback to students. The technology provides students with the opportunity to practice questions in a personalized environment until mastery is achieved. The auto-grading features reduce grading effort, allowing instructors to focus on course design, incorporate more frequent and second-chance testing, and have more time to directly help students. The platform can automatically generate and grade personalized assessments for each student, which helps to minimize cheating and enables repeated practice by students. This learning experience is suited to help minorities, first-generation college students, and students of low socioeconomic status, who have traditionally had less access to the highest quality human instructors. Making STEM education more effective will facilitate the creation and continuing support of a highly educated STEM workforce and is important for national competitiveness in related fields.This Phase I project aims to develop a no-code, graphical authoring environment that will allow instructors without prior programming experience to create AI-based auto-graded content. Instructors will be enabled to create sophisticated, auto-graded assessments by combining the existing core AI technology of this project with the following innovations: a) a no-code, graphical authoring using block-based language and data-flow visualizations; (2) new AI auto-graders for structured data, such as student data analyses within spreadsheets, by using verification algorithms to specify and check constraints on student answers; and (3) a graphical interface to use the new AI auto-graders for structured data, including associated data-flow visualizations. All three of these new capabilities will be evaluated via user-focused studies with a small group of instructors from a variety of backgrounds and programming skill levels, ranging from novice to expert. These semi-structured qualitative studies will follow a grounded theory approach, addressing metrics specific to each objective.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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛/商业影响是为更广泛而复杂的评估工具提供为更广泛的STEM（科学，技术，工程和数学）教育工作者提供的，以提高学生学习，使教学效率更高，并降低作弊的事件。该技术的核心技术是一个在线平台，用于创建和提供由人工智能（AI）算法自动分级的高质量评估，从而为学生提供了即时的反馈。该技术为学生提供了在个性化环境中练习问题的机会，直到获得掌握为止。自动分级功能减少了分级工作，使讲师可以专注于课程设计，纳入更频繁和第二次回合的测试，并有更多时间直接帮助学生。该平台可以自动为每个学生生成和评级个性化评估，这有助于最大程度地减少作弊并实现学生的重复练习。这种学习经验适合帮助少数民族，第一代大学生和社会经济地位低下的学生，他们传统上无法获得最高质量的人类教练的机会。使STEM教育更加有效将有助于创建和继续支持高等教育的STEM劳动力，并且对于相关领域的国家竞争力很重要。本I阶段项目旨在开发无代码的图形创作环境，该环境将允许教师在没有编程经验的情况下创建基于AI基于AI的自动分级内容。通过将该项目的现有核心AI技术与以下创新相结合，可以使讲师能够创建复杂的自动分级评估：a）使用基于块的语言和数据流可视化的无代码，图形创作； （2）通过使用验证算法来指定和检查学生答案的约束，用于结构化数据的新的AI自动学者，例如电子表格中的学生数据分析； （3）图形接口使用新的AI自动学生进行结构化数据，包括关联的数据流可视化。这三个新功能将通过以用户为中心的研究来评估来自各种背景和编程技能水平的一小组讲师，从新手到专家。这些半结构化的定性研究将遵循一种基础理论方法，解决了每个目标的指标。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。