Context-based Automated Scoring of Complex Mathematical Responses
Aoife Cahill | James H Fife | Brian Riordan | Avijit Vajpayee | Dmytro Galochkin
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications
The tasks of automatically scoring either textual or algebraic responses to mathematical questions have both been well-studied, albeit separately. In this paper we propose a method for automatically scoring responses that contain both text and algebraic expressions. Our method not only achieves high agreement with human raters, but also links explicitly to the scoring rubric – essentially providing explainable models and a way to potentially provide feedback to students in the future.