Abstract
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.
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Notes
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A correction for multiple hypothesis testing was not applied; it would be valuable if outperforming performance of any classifier was to be established.
References
Shermis, M.D., Burstein, J.: Handbook of Automated Essay Evaluation: Current Applications and New Directions. Routledge, New York (2013)
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)
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 2003
Attali, Y., Burstein, J.: Automated essay scoring with e-rater® v. 2.0. ETS Res. Rep. Ser. 2004(2) (2004)
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)
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)
Juola, P.: Authorship attribution. Found. Trends Inf. Retriev. 1(3), 233–334 (2008)
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 2014
Doyle, J.: Automatic evaluation of student essays using n-gram analysis techniques. Master’s thesis, Dalhousie University (2007)
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 2007
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)
Bird, S., Loper, E., Klein, E.: Natural Language Processing with Python. O’Reilly Media Inc., Sebastopol (2009)
Lemaître, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a Python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(17), 1–5 (2017)
Acknowledgement
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.
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Jankowska, M., Conrad, C., Harris, J., Kešelj, V. (2018). N-Gram Based Approach for Automatic Prediction of Essay Rubric Marks. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_30
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DOI: https://doi.org/10.1007/978-3-319-89656-4_30
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