Abstract
Automatic scoring systems for students’ short answers can eliminate from instructors the burden of grading large number of test questions and facilitate performing even more assessments during lectures especially when number of students is large. This paper presents a supervised learning approach for short answer automatic scoring based on paragraph embeddings. We review significant deep learning based models for generating paragraph embeddings and present a detailed empirical study of how the choice of paragraph embedding model influences accuracy in the task of automatic scoring.
Highlights
Improving the quality of education is always a desired goal in educational institutions
Short answer and essay answer questions require more complicated work related to text processing and analysis; while automatic scoring of other types can be easy and direct task
The objective of this study is to apply a comprehensive evaluation of multiple state-of-the-art paragraph embedding models by applying them to the task of short answer automatic scoring
Summary
Improving the quality of education is always a desired goal in educational institutions. Many courses are given in large classrooms where number of attending students is large. Such large learning environments present special challenges on instructors and one of these challenges is students’ assessments. Automatic short answer scoring is the task of “assessing short natural language responses to objective questions using computational methods” [1]. This eliminates from instructors the burden of grading large number of test questions and facilitates performing even more assessments during lectures. An answer is considered short answer if its length approximately ranges from one phrase to one paragraph [1]
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