Discussion Tracker: Development of Human Language Technologies to Improve the Teaching of Collaborative Argumentation in High School

合作辩论--或通过对话建立以证据为基础的、合理的知识和解决办法--对个人学习和集体解决问题至关重要。以学生为中心的讨论和协作性辩论期间延长的学生谈话是跨学科健康学习的指标。参与协作解决问题的能力也是21世纪工作场所和公民参与的一个决定性特征。然而，合作研讨教学是许多教师努力发展的一项高级技能。该项目的目标是开发一种名为讨论跟踪器的创新技术，这是一个基于计算机的高中英语教师系统，利用人类语言技术(HLT)的最新进展，为教师提供关于学生在课堂上协作辩论质量的自动生成数据和教学指导。讨论跟踪器将为教师提供学生协作谈话的重要特征的可视化表示，以及用于教学反思和未来规划的工具。该项目将改进高中协作性辩论的教与学，以便学生为未来教育、工作场所和公民环境中的协作性问题解决做好准备。该项目利用人类语言技术(HLT)、数据可视化/分析和教师学习方面的最新进展来推进提供课堂谈话自动反馈的技术，以提高教学效率和学生成绩。它将开发新的HLT方法来检测学生协作谈话的三个重要特征：论点移动(声明、证据、推理)、特殊性和协作(例如，建立、探索或挑战他人的想法)。在第一年和第二年，将进行一系列实验，以测试讨论跟踪界面选项，探索教师针对不同类型的教学指导的学习，并改进系统的功能。同时，利用学生话语语料库(包括现有的和从上述实验中收集的)，将关系编码、论点挖掘的进展、手工特征和神经网络模型与多任务训练相结合，对学生话语的三个特征进行计算建模。然后，这些模型将被纳入讨论跟踪系统，以促进教师快速、数据驱动的反思实践。在第三年，将进行一项大型课堂实验，以确定全自动讨论跟踪器在不同时间和学校背景下对教师学习和教学的影响。将在项目的每个阶段确定概括的见解，以促进向未来技术的可转换性，旨在检测学生谈话的特征，并在类似的内容领域、年级水平和基于语言的学习平台上进行教师学习。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。

Collaborative argumentation - or the building of evidence-based, reasoned knowledge and solutions through dialogue - is essential to individual learning as well as group problem-solving. Student-centered discussions and extended student talk during collaborative argumentation are indicators of robust learning across disciplines. The ability to engage in collaborative problem-solving is also a defining characteristic of 21st century workplaces and civic engagement. However, teaching collaborative argumentation is an advanced skill that many teachers struggle to develop. The goal of this project is to develop an innovative technology called Discussion Tracker, a computer-based system for high school English teachers that uses recent advances in human language technologies (HLT) to provide teachers with automatically generated data and instructional guidance on the quality of students' collaborative argumentation in their classrooms. Discussion Tracker will provide teachers with visual representations of the significant features of their students' collaborative talk and tools for instructional reflection and future planning. The project will improve the teaching and learning of collaborative argumentation in high schools so that students will be prepared for collaborative problem-solving in future educational, workplace, and civic settings.This project leverages recent advances in human language technologies (HLT), data visualization/ analytics, and teacher learning to advance technology that provides automated feedback on classroom talk with the goal of improving teaching effectiveness and student achievement. It will develop novel HLT methods for detecting three significant features of students' collaborative talk: argument moves (claim, evidence, reasoning), specificity, and collaboration (e.g., building on, probing or challenging others' ideas). During years 1 and 2, a series of experiments will be conducted to test Discussion Tracker interface options, to explore teacher learning in response to different types of instructional guidance, and to improve the functionality of the system. Simultaneously, student talk corpora (both existing and collected from the experiments above) will be used to computationally model the three features of student talk by combining relation coding, advances in argument mining, handcrafted features and neural network models with multi-task training. These models will then be incorporated into the Discussion Tracker system to promote teachers' rapid, data-driven reflective practice. In year 3, a large classroom experiment will be conducted to determine the effects of the fully automated Discussion Tracker on teacher learning and instruction across time and school contexts. Generalizable insights will be identified at every stage of the project to promote transferability to future technologies aimed at detecting features of student talk and to teacher learning in similar content areas, grade levels, and language-based learning platforms.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.