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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Automatic Short Answer Scoring based on Paragraph Embeddings
Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.091048
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 10, 2018.
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.
Keywords: Automatic scoring; short answer; Pearson correlation coefficient; RMSE; deep learning
Sarah Hassan, Aly A. Fahmy and Mohammad El-Ramly, “Automatic Short Answer Scoring based on Paragraph Embeddings” International Journal of Advanced Computer Science and Applications(IJACSA), 9(10), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091048
@article{Hassan2018,
title = {Automatic Short Answer Scoring based on Paragraph Embeddings},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091048},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091048},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {10},
author = {Sarah Hassan and Aly A. Fahmy and Mohammad El-Ramly}
}
