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EAGER: Discussion Tracker: Development of Human Language Technologies to Improve the Teaching of Collaborative Argumentation in High School English Classrooms: 1842334

Principal Investigator: Amanda Godley
CoPrincipal Investigator(s): Diane Litman
Organization: University of Pittsburgh

Collaborative argumentation – the building of evidence-based, reasoned knowledge and solutions through dialogue – is essential to individual learning as well as group problem-solving in STEM and other disciplines and a defining characteristic of 21st century workplaces. However, teaching collaborative argumentation is an advanced skill that many high school teachers struggle to develop. This EArly Grant for Exploratory Research explores the feasibility of improving high school teachers’ understanding of collaborative argumentation and instruction through a computer-based system called Discussion Tracker. The prototype system draws upon recent advances in human language technologies (HLT) to provide teachers with automatically generated data about the characteristics and quality of their students’ discussions. The system also provides individualized guidance for teachers on how to improve the facilitation of such discussions. The project has the potential to prepare a new generation of teachers and students for the productive collaborative argumentation they will need for future educational and workplace settings. Findings and products resulting from this study also contribute to research and technological innovations aimed at improving teacher learning and student discussions in high school English classrooms and other content areas, grade levels, and learning platforms. Additionally, the project’s multidisciplinary training of graduate and undergraduate students is focused on increasing diversity in cyberlearning research and development.

Three stages of investigation are planned, First, a schema of the features of high-quality collaborative argumentation in high school English language arts (ELA) discussions is created and tested in order to identify the most critical discourse features. Second, prototypes of the system’s interface is developed that provide varied amounts and types of data visualizations and explicit guidance for teacher learning. Third, in order to analyze data on teacher learning, a series of experiments are conducted to study how teachers interact with various prototypes of the system. In order to test optimal teacher learning, teachers will be assigned to one of two conditions: (1) Use of the system with a brief reflection guide, designed to document teachers’ observations and prompt teachers’ instructional changes, (2) Use of the system with an expert instructional coach. Teacher learning will be studied through reflective, qualitative think aloud protocols, pre-post questionnaires, and observations of instruction. Outcomes of the two experimental conditions will be compared using standard statistical methods.

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.

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