Novel Framework for Improving the Correctness of Reference Answers to Enhance Results of ASAG Systems

Abstract

Usage of online learning platforms increases day by day and henceforth, there emerges the need for automated grading systems to assess the learner’s performance. Evaluating these answers demands for a well-grounded reference answer which aids a strong foundation for better grading. Since reference answers impacts the exactness of grading answers of learners, its correctness remains a great concern. A framework that addresses the reference answer exactness in Automated Short Answer Grading (ASAG) systems was developed. This framework includes material content acquisition, clustering collective content, expert answer as key components which was later fed to a zero-shot classifier for a strong reference answer generation. Then, the computed reference answers along with student answers and questions from Mohler dataset were fed to an ensemble of transformers to produce relevant grades. The aforementioned models’ RMSE and correlation values were compared against the past values of the dataset. Based on the observations made, this model outperforms the previous approaches.