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

该项目旨在通过开发自动化助手（聊天机器人）来服务于国家利益，提高化学学生在普通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。化学经常被称为“中心科学”，因为化学思想被许多其他STEM领域所使用。当他们能够看到化学和他们正在学习的其他课程之间的联系时，入门学生在化学学习中受益。除了科学内容，学生在入门课程也学习如何找到可靠的信息，是相关的学习和他们的兴趣。在这个项目中开发的自动化助手将以多种方式帮助学生进行这种形式的学习。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开发的广泛连接作为“决策树”，以便当学生使用聊天机器人时，他们找到的信息已经过质量评估。映射连接的性质可以促使学生探索他们正在学习的内容领域，以更广泛的社会关注，提高他们对解决社会面临的问题的未来角色的理解。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会回应学生并引导他们参与化学对社会的贡献，将提供数据，可以为改进的沟通策略提供信息，包括自动生成的策略和教师执行的策略。大学科学入门课程的一个关键沟通挑战是参与者的数量。每年有10万名学生报名参加这些课程，教学人员与学生互动的能力往往是一项艰巨的任务。使用教师的专业知识来构建具有广泛互连的内容网络，作为自动化助理的决策树，将允许学生，即使是大型入门课程的学生，与材料进行对话。机器学习的最新进展使这种对话在人类奋进的许多领域中显得很自然，但在社会关注的背景下，关于科学内容的学术对话还有待研究和推进。大学一年级的化学是一个理想的场所，可以进行关于建立能够指导学生参与的自动化助理的研究。这一目标不仅包括传统的课程内容，还包括化学与其他领域和更大的社会问题的直接联系。由于学生与聊天机器人的互动会以日志的形式生成数据，随后可以挖掘关键模式，因此该项目将提供有关学生参与的固有习惯的信息，以及自动对话帮助学生探索化学新方面的方式，包括不同科学领域如何合作为广泛的社会改善提供途径。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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 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.