Diane Litman
Associated IES Content
Grant
Enhancing Undergraduate STEM Education by Integrating Mobile Learning Technologies with Natural Language Processing
In this project, the researchers will refine an existing mobile application, CourseMIRROR, for use in postsecondary STEM lecture courses. This application aims to improve deep learning by encouraging students to reflect on course content and receive immediate feedback on their reflections. Often, in large lecture courses, students' ability to reflect on course content and get feedback on these reflections is limited by class size and instructor availability. At the same time, instructors oft...
Federal funding program:
Award number:
R305A180477
Grant
Response-to-Text Tasks to Assess Students' Use of Evidence and Organization in Writing: Using Natural Language Processing for Scoring Writing and Providing Feedback At-Scale
Researchers for this project will develop and validate an automated assessment of students' analytic writing skills in response to reading text. During prior work the researchers studied an assessment of students' analytic writing to understand progress toward outcomes in the English Language Arts Common Core State Standards, and to understand effective writing instruction by teachers. The researchers focused on response-to-text assessment because: it is an essential skill for secondary and ...
Federal funding program:
Award number:
R305A160245
Grant
Intelligent Scaffolding for Peer Reviews of Writing
The purpose of this project is to improve upon an existing software technology-Scaffolded Writing and Rewriting in the Disciplines (SWoRD)-that facilitates the writing and revision of essays and compositions, and handles the logistics of peer review (e.g., distribution of essays to reviewers, collection and distribution of anonymous reviewer comments to original authors). Researchers will attempt to improve upon the current software technology by adding new features that leverage advances in...
Federal funding program:
Award number:
R305A120370
Grant
Improving a Natural-Language Tutoring System that Engages Students in Deep Reasoning Dialogues about Physics
Recent studies show that U.S. students lag behind students in other developed countries in math and science. Because one-on-one tutoring has been shown to be a highly effective form of instruction, many educators and education policy makers have looked to intelligent tutoring systems (ITSs) as a means of providing cost-effective, individualized instruction to students that can improve their conceptual understanding of and problem-solving skills in math and science. However, even though many ...
Federal funding program:
Award number:
R305A100163