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

讨论跟踪器：人类语言技术的发展以改善高中协作论证的教学

基本信息

批准号：
1917673
负责人：
Amanda Godley
金额：
$ 74.95万
依托单位：
University of Pittsburgh
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917673&HistoricalAwards=false
关键词：
Discussion Tracker Development Human Language

项目摘要

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.

协作论证-或通过对话建立基于证据的、经过推理的知识和解决办法-对于个人学习和小组解决问题至关重要。以学生为中心的讨论和扩展的学生谈话在协作论证是跨学科的强大学习的指标。参与协作解决问题的能力也是21世纪世纪工作场所和公民参与的一个定义性特征。然而，教学合作论证是一个先进的技能，许多教师努力发展。该项目的目标是开发一种名为Discussion Tracker的创新技术，这是一种基于计算机的高中英语教师系统，该系统使用人类语言技术（HLT）的最新进展，为教师提供自动生成的数据和教学指导，以提高学生在课堂上的协作论证质量。讨论追踪器将为教师提供学生合作谈话的重要特征的视觉表示，以及用于教学反思和未来规划的工具。该项目将改善高中协作论证的教学，使学生为未来的教育，工作场所和公民环境中的协作解决问题做好准备。该项目利用人类语言技术（HLT），数据可视化/分析，和教师学习先进的技术，提供自动反馈的课堂谈话的目标，提高教学效率和学生成就它将开发新的HLT方法来检测学生合作谈话的三个重要特征：论点移动（主张，证据，推理），特异性和合作（例如，建立、探索或挑战他人的想法）。在第1年和第2年，将进行一系列的实验，以测试讨论跟踪界面选项，探讨教师学习，以应对不同类型的教学指导，并提高系统的功能。同时，学生谈话语料库（现有的和从上述实验中收集的）将被用来计算模型的三个功能的学生谈话相结合的关系编码，参数挖掘，手工制作的功能和神经网络模型与多任务训练。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，将进行一个大型的课堂实验，以确定全自动讨论跟踪器对教师学习和教学的影响，跨越时间和学校环境。在项目的每一个阶段都将确定可推广的见解，以促进可转移到旨在检测学生谈话特征的未来技术，以及在类似内容领域，年级和基于语言的学习平台中的教师学习。该奖项反映了NSF的法定使命，并被认为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来支持。