Creating Text-based Automated Assistants for Laboratory and Writing Assignments in the Teaching of General Chemistry

在普通化学教学中为实验室和写作作业创建基于文本的自动化助手

• 批准号: 2235600
• 负责人: Thomas Holme
• 金额: $ 29.08万
• 依托单位: Iowa State University
• 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: 2023-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235600&HistoricalAwards=false
• 关键词: Creating; Text; based; Automated; Assistants

This project aims to serve the national interest by developing automated assistants (chatbots) that enhance the ability of chemistry students to make connections between the concepts taught in general chemistry and potential solutions to problems faced by society. Chemistry has often been called “The Central Science” because chemical ideas are used by many other STEM fields. Introductory students benefit in their learning of chemistry when they are able to see the connections between chemistry and other courses they are taking. In addition to science content, students in introductory courses are also learning ways to locate reliable information that is relevant to their learning and to their interests. Automated assistants to be developed in this project will assist students in this form of learning in several ways. These chatbots will use maps of the science content that include extensive connections that are developed by content experts as “decision trees” so that when students use the chatbots, the information they find has been assessed for quality. The nature of the mapped connections can prompt students to explore areas of the content they are studying to broader concerns of society, enhancing their understanding of their future roles in solving problems that society faces. Finally, the software that powers chatbots includes machine learning, so analysis of how the software learns to respond to students and guide their engagement with contributions of chemistry to society will provide data that can inform improved communication strategies, both those automatically generated and those carried out by teachers.A key communication challenge in introductory college science courses is the number of participants involved. With 100,000s of students enrolled in these courses every year, the ability of instructional staff to engage with students often presents a daunting task. Using the expert knowledge of instructors to construct content networks with extensive interconnections as decision trees for automated assistants will allow students, even those in large introductory courses, to engage dialogically with material. Recent advances in machine learning have made such dialogs seem natural in many areas of human endeavor, but academic conversations about science content within the context of societal concerns has yet to be studied and advanced. First-year college chemistry represents an ideal venue to conduct research about building automated assistants capable of directing student engagement. This goal includes not only traditional course content, but also direct connections of chemistry to other fields and larger societal issues. Because the interactions students have with chatbots generates data in the form of logs that can subsequently be mined for key patterns, this project will provide information about inherent student habits of engagement, and ways that automated conversations help students to explore new aspects of chemistry, including how different fields of science in collaboration provide pathways to broad societal improvements. The NSF IUSE:EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the 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.

该项目旨在通过开发自动化助理（聊天机器人）来满足国家利益，从而增强了化学学生在通用化学中教授的概念与社会面临的问题的潜在解决方案之间建立联系的能力。化学通常被称为“中央科学”，因为许多其他STEM领域都使用了化学思想。当他们能够看到化学和正在参加的其他课程之间的联系时，入门学生在化学学习方面受益。除了科学内容外，引言课程中的学生还学习了找到与他们的学习和兴趣相关的可靠信息的方法。在该项目中开发的自动助理将以几种方式帮助学生以这种学习形式。这些聊天机器人将使用科学内容的地图，其中包括由内容专家作为“决策树”开发的广泛连接，以便当学生使用聊天机器人时，他们发现的信息已被评估为质量。映射的联系的性质可以促使学生探索他们正在研究的内容的领域，以扩大社会的更广泛关注，从而增强他们对解决社会面临的问题的未来作用的理解。最后，为聊天机器人提供动力的软件包括机器学习，因此分析软件如何学习对学生的反应并指导他们对社会化学的贡献的参与，将提供可以为改进的沟通策略提供信息的数据，包括自动生成的沟通策略和教师进行的研究。简介中的一项关键交流挑战是参与者参与参与者的参与者。每年有100,000名学生参加这些课程，教学人员与学生互动的能力通常会付出艰巨的任务。利用教师的专家知识来构建具有广泛互连的内容网络作为自动助手的决策树，将允许学生，即使是大型介绍课程的学生，也可以与材料进行对话。机器学习的最新进展使这种对话在人类努力的许多领域似乎很自然，但是在社会关注的背景下，关于科学内容的学术对话尚未研究和高级。一年级的大学化学代表了开展有关能够指导学生参与的自动化助手的研究的理想场所。该目标不仅包括传统课程内容，还包括化学与其他领域和更大社会问题的直接联系。由于学生与聊天机器人进行的互动会以日志的形式生成数据，以便随后开采以获取关键模式，因此该项目将提供有关继承学生参与习惯的信息，以及自动化对话的方式帮助学生探索化学的新方面，包括不同的科学领域的不同科学领域为社会改进提供了途径。 NSF IUSE：EHR计划支持研发项目，以提高所有学生STEM教育的有效性。通过其参与的学生学习轨道，该计划支持了诺言实践和工具的创建，探索和实施。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来通过评估来诚实地表示支持。