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DOI: 10.14569/IJACSA.2018.091048
PDF

Automatic Short Answer Scoring based on Paragraph Embeddings

Author 1: Sarah Hassan
Author 2: Aly A. Fahmy
Author 3: Mohammad El-Ramly

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 10, 2018.

  • Abstract and Keywords
  • How to Cite this Article
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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}
}



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.

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