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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响是为更广泛的STEM（科学，技术，工程和数学）教育工作者提供一个强大而复杂的评估工具，以改善学生的学习，提高教学效率，并减少作弊的发生率。该技术的核心技术是一个在线平台，用于创建和提供高质量的评估，这些评估由人工智能（AI）算法自动评分，为学生提供即时反馈。该技术为学生提供了在个性化环境中练习问题的机会，直到掌握为止。自动评分功能减少了评分工作，使教师能够专注于课程设计，纳入更频繁和第二次机会的测试，并有更多的时间直接帮助学生。该平台可以为每个学生自动生成和评分个性化的评估，这有助于减少作弊，并使学生能够重复练习。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的学生，他们传统上很少有机会获得最高质量的人类教师。提高STEM教育的有效性将有助于培养和持续支持一支受过高等教育的STEM人才队伍，对相关领域的国家竞争力至关重要。该项目的第一阶段旨在开发一个无代码的图形化创作环境，使没有编程经验的教师能够创建基于AI的自动评分内容。教师将能够通过将该项目现有的核心人工智能技术与以下创新相结合来创建复杂的自动评分评估：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对学生答案的约束，用于结构化数据的新人工智能自动评分器，例如电子表格中的学生数据分析;以及（3）图形界面，用于使用新的AI自动分级器进行结构化数据，包括相关的数据流可视化。所有这三个新的能力将通过以用户为中心的研究与一小群教师从各种背景和编程技能水平，从新手到专家进行评估。这些半结构化的定性研究将遵循扎根理论的方法，解决每个目标的具体指标。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。