4.6 Article

An automatic short-answer grading model for semi-open-ended questions

Journal

INTERACTIVE LEARNING ENVIRONMENTS
Volume 30, Issue 1, Pages 177-190

Publisher

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/10494820.2019.1648300

Keywords

Machine learning; short-answer grading; text analysis; classification; auto-grading

Funding

  1. National Natural Science Foundation of China [61807004]
  2. Philosophy and Social Sciences Research of the Chinese Ministry of Education [16JZD043]

Ask authors/readers for more resources

Automatic short-answer grading has been studied for more than a decade. This technique has been used for auto assessment and intelligent tutoring systems. Most early works grade based on similarity to the reference answer, but struggle with semi-open-ended questions. This paper proposes a model that integrates domain-general and domain-specific information, using a recurrent neural network to consider word sequence information, and demonstrates its advantage in experiments.
Automatic short-answer grading has been studied for more than a decade. The technique has been used for implementing auto assessment as well as building the assessor module for intelligent tutoring systems. Many early works automatically grade mainly based on the similarity between a student answer and the reference answer to the question. This method performs well for closed-ended questions that have single or very limited numbers of correct answers. However, some short-answer questions ask students to express their own thoughts based on various facts; hence, they have no reference answers. Such questions are called semi-open-ended short-answer questions. Questions of this type often appear in reading comprehension assessments. In this paper, we developed an automatic semi-open-ended short-answer grading model that integrates both domain-general and domain-specific information. The model also utilizes a long-short-term-memory recurrent neural network to learn the representation in the classifier so that word sequence information is considered. In experiments on 7 reading comprehension questions and over 16,000 short-answer samples, our proposed automatic grading model demonstrates its advantage over existing models.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available