N-Gram Based Approach for Automatic Prediction of Essay Rubric Marks
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Automatic Essay Scoring, applied to the prediction of grades for dimensions of a scoring rubric, can provide automatic detailed feedback on students’ written assignments. We apply a character and word n-gram based technique proposed originally for authorship identification—Common N-Gram (CNG) classifier—to this task. We report promising results for the rubric mark prediction for essays by CNG, and perform analysis of suitability of different types of n-grams for the task.
KeywordsAutomatic Essay Scoring Text classification Character n-grams
The project was supported by the NSERC Engage grant EGP/507291-2016 with industry partner, D2L Corporation. The authors would like to thank D2L members: Brian Cepuran, VP, D2L Labs and Rose Kocher, Director, Grant & Research Programs, for their guidance in the project and the feedback on the paper. The authors would also like to acknowledge a support from Killam Predoctoral Scholarship.
- 1.Shermis, M.D., Burstein, J.: Handbook of Automated Essay Evaluation: Current Applications and New Directions. Routledge, New York (2013)Google Scholar
- 2.Phandi, P., Chai, K.M.A., Ng, H.T.: Flexible domain adaptation for automated essay scoring using correlated linear regression. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 431–439 (2015)Google Scholar
- 3.Kešelj, V., Peng, F., Cercone, N., Thomas, C.: N-gram-based author profiles for authorship attribution. In: Proceedings of the Conference Pacific Association for Computational Linguistics, PACLING 2003, Dalhousie University, Halifax, Nova Scotia, Canada, pp. 255–264, August 2003Google Scholar
- 4.Attali, Y., Burstein, J.: Automated essay scoring with e-rater® v. 2.0. ETS Res. Rep. Ser. 2004(2) (2004)Google Scholar
- 5.Singh, A., Karayev, S., Gutowski, K., Abbeel, P.: Gradescope: a fast, flexible, and fair system for scalable assessment of handwritten work. In: Proceedings of the Fourth 2017 ACM Conference on Learning@ Scale, pp. 81–88. ACM (2017)Google Scholar
- 6.Foltz, P.W., Laham, D., Landauer, T.K.: Automated essay scoring: applications to educational technology. In: EdMedia: World Conference on Educational Media and Technology, pp. 939–944. Association for the Advancement of Computing in Education (AACE) (1999)Google Scholar
- 8.Jankowska, M., Milios, E., Kešelj, V.: Author verification using common n-gram profiles of text documents. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland, pp. 387–397. Dublin City University and Association for Computational Linguistics, August 2014Google Scholar
- 9.Doyle, J.: Automatic evaluation of student essays using n-gram analysis techniques. Master’s thesis, Dalhousie University (2007)Google Scholar
- 10.Stamatatos, E.: Author identification using imbalanced and limited training texts. In: Proceeding of the 18th International Workshop on Database and Expert Systems Applications, DEXA 2007, Regensburg, Germany, pp. 237–241, September 2007Google Scholar
- 11.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar