@inproceedings{339546ae8991499c9fa0993629feb21a,
title = "Can short answers to open response questions be auto-graded without a grading rubric?",
abstract = "Auto-grading short-answers seems to be sufficiently resolved. However, most auto-graders require comprehensive scoring rubrics, which were not always available. This paper used modern machine learning techniques to build auto-graders without expressly defining the rubrics. The result shows that the best auto-grading model is able to achieve a good inter-rater agreement (kappa = 0.625) with expert grading. The agreement can be further improved (kappa = 0.726) if the auto-grading model gave up scoring some of the answers.",
keywords = "Auto-grading, LSTM, Short-answer, SVM",
author = "Xi Yang and Lishan Zhang and Shengquan Yu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 18th International Conference on Artificial Intelligence in Education, AIED 2017 ; Conference date: 28-06-2017 Through 01-07-2017",
year = "2017",
doi = "10.1007/978-3-319-61425-0\_72",
language = "English",
isbn = "9783319614243",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "594--597",
editor = "Elisabeth Andre and Xiangen Hu and Rodrigo, \{Ma. Mercedes T.\} and \{du Boulay\}, Benedict and Ryan Baker",
booktitle = "Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings",
address = "Germany",
}