Sourcewrite: real-time, biometric, intention-informed scaffolding of source-based writing processes

The National Science Foundation (NSF), through its Computer and Information Science and Engineering (CISE) Program (CFDA 47.070), awarded a $150,000 Project Grant to Iowa State University of Science and Technology (Iowa State University). This 3-year grant supports the development of an intelligent tutoring system called SourceWrite that will automatically track students' writing processes, infer their intentions, and provide real-time, individualized advice and assistance during source-based writing assignments.

The project will combine biometric data, such as keystroke timings and eye movements, with natural language processing to predict how students' writing processes will impact the quality of their final written compositions, even before the text is fully produced. Iowa State University is collaborating with Technische Universiteit Eindhoven (TU/e), which is serving as a sub-grantee to develop the user interface and user experience for the SourceWrite system, ensure the project is grounded in current learning analytics and machine learning research, and provide specialized training for an Iowa State graduate student.

Abstract

Writing is a complex task which comprises several component processes: reading source materials, setting goals, planning content, translating ideas into language, reading already-written text, copyediting, and so forth. Which processes a student uses, and in what sequence, affects the quality of their written composition. By the time students reach college, most will have developed their own individual mixture of writing processes. These will vary in effectiveness. When faced with demanding disciplinary writing tasks, especially those that require synthesizing multiple sources, students' established writing processes often turn out to be suboptimal. This is a particular concern for students studying for science, technology, engineering, and mathematics (stem) degrees. Required college-level composition classes are designed to help students improve their writing skills. However, in these classes, students usually receive feedback only about the texts they have already written, not about the processes they use when they write. This is because writing instructors do not have access to the moment-by-moment actions by which students' texts are produced. In this project, the researchers will develop an intelligent tutoring system called sourcewrite that will automatically track what the student is doing during the composition process, infer why they are doing IT, and then provide individualized advice and assistance, all in real time while the student is still in the process of composing their text. Specifically, the researchers will develop methods for automatic writing-process analysis that will combine biometric data (keystroke timings and eye movements) with natural language processing to infer the student's intentions during composition. These methods will permit automatic, real-time predictions about writing-process patterns and how these will affect the ultimate quality of the text. This will be achieved in real time, during text composition, before the text has been fully produced. To achieve this end, this project will bring together research in (data-driven) writing analytics with (theory-driven) psycholinguistics of text production, two directions that have traditionally been followed separately. The learning and teaching innovation will be in designing, implementing, and evaluating a novel educational intervention that will provide intelligent support to students as they engage with their sources and produce academic text, in the context of a college composition course. Through a series of design-based research iterations followed by a randomized, controlled evaluation, this project will establish design principles for this new pedagogy and determine its effectiveness for developing college students' writing ability. 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.- subawards are planned for this award.