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，以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。化学通常被称为“中心科学”，因为化学思想被许多其他STEM领域所使用。当入门学生能够看到化学与他们正在学习的其他课程之间的联系时，他们就会在化学学习中受益。除了科学内容，入门课程的学生也在学习找到与他们的学习和兴趣相关的可靠信息的方法。在这个项目中将开发的自动化助手将以几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地图，其中包括由内容专家开发的广泛联系，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。映射关系的性质可以促使学生探索他们正在学习的内容的领域，以更广泛地关注社会，增强他们对未来在解决社会面临的问题方面的角色的理解。最后，支持聊天机器人的软件包括机器学习，因此分析该软件如何学习回应学生，并引导他们参与到化学对社会的贡献中，将提供数据，为改进的沟通策略提供信息，无论是自动生成的沟通策略，还是教师执行的沟通策略。大学科学入门课程中的一个关键沟通挑战是参与者的数量。由于每年有10万名学生注册这些课程，教学人员与学生互动的能力往往是一项艰巨的任务。利用教师的专业知识构建具有广泛互连的内容网络，作为自动化助理的决策树，将使学生，即使是大型入门课程的学生，也可以与材料进行对话。机器学习的最新进展使这样的对话在人类努力的许多领域似乎是自然的，但在社会关切的背景下关于科学内容的学术对话尚未得到研究和推进。一年级的大学化学课程是进行研究的理想场所，目的是培养能够指导学生参与的自动化助手。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产生了日志形式的数据，随后可以挖掘这些数据来寻找关键模式，因此该项目将提供有关学生固有的参与习惯的信息，以及自动对话帮助学生探索化学新方面的方法，包括不同科学领域的协作如何提供广泛的社会进步的途径。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。通过其参与的学生学习跟踪，该计划支持有前途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 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.