Leveraging natural language processing to support automated assessment and feedback for student open responses in mathematics
Abstract
Teachers often rely on the use of open‐ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close‐ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time‐consuming, to evaluate and provide directed feedback. It is a well‐studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher‐authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 13, 2023
- Source ID
- 10.1111/jcal.12793
Entities
People
- Anthony Botelho
- John A. Erickson
- Neil T. Heffernan
- Priyanka Benachamardi
- Sami Baral
Organizations
- Institute of Education Sciences
- National Science Foundation
- Office of Naval Research
- United States Department of Education
- University of Florida
- Western Kentucky University
- Worcester Polytechnic Institute