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Research Article | Open Access |

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) · Vol. 9, No. 10 · Published 2018 · Cited by 31

DOI: https://doi.org/10.14569/IJACSA.2018.091048

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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

How to Cite this Article

Hassan, S., Fahmy, A. A., & El-Ramly, M. (2018). Automatic Short Answer Scoring based on Paragraph Embeddings. International Journal of Advanced Computer Science and Applications, 9(10). https://doi.org/10.14569/IJACSA.2018.091048

Hassan, Sarah, et al.. "Automatic Short Answer Scoring based on Paragraph Embeddings." International Journal of Advanced Computer Science and Applications, vol. 9, no. 10, 2018, https://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},
  volume    = {9},
  number    = {10},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Sarah Hassan and Aly A. Fahmy and Mohammad El-Ramly},
  doi       = {10.14569/IJACSA.2018.091048},
  url       = {https://doi.org/10.14569/IJACSA.2018.091048}
}

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