Proceedings of the 2nd Workshop on Natural Language Processing Techniques for Educational Applications 2015
DOI: 10.18653/v1/w15-4409
|View full text |Cite
|
Sign up to set email alerts
|

An Automated Scoring Tool for Korean Supply-type Items Based on Semi-Supervised Learning

Abstract: Scoring short-answer questions has disadvantages that may take long time to grade and may be an issue on consistency in scoring. To alleviate the disadvantages, automated scoring systems are widely used in America or Europe, but, in Korea, there has been researches regarding the automated scoring. In this paper, we propose an automated scoring tool for Korean short-answer questions using a semisupervised learning method. The answers of students are analyzed and processed through natural language processing and… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2021
2021
2021
2021

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(2 citation statements)
references
References 4 publications
0
2
0
Order By: Relevance
“…Next, the essays are processed to retain significant features in the Feature Selection process. Typical features in MLF were words (Cheon et al, 2015), syntactic and dependency features. Feature selection is an important step in many machine…”
Section: Machine Learning Frameworkmentioning
confidence: 99%
See 1 more Smart Citation
“…Next, the essays are processed to retain significant features in the Feature Selection process. Typical features in MLF were words (Cheon et al, 2015), syntactic and dependency features. Feature selection is an important step in many machine…”
Section: Machine Learning Frameworkmentioning
confidence: 99%
“…In interpreting any kappa value, K can be considered as poor when lower than 0.4, fair to good when K between 0.4 and 0.75, and excellent when K is greater than 0.75. To interpret Pearson's correlation, r can be considered as very small when r lower than 0.2, small when r between 0.2 and 0.4, medium when r between 0.4 and 0.6, large when r between 0.6 and 0.8, and very large when r greater than 0.8 (Cheon et al, 2015). Theses evaluation methods are suitable for evaluating the multiclass classification model since it calculates a confusion matrix between the predicted and actual values.…”
Section: Pearson's Correlation Coefficient Cohen's Kappa Coefficient and Quadratic Weighted Kappa (Qwk)mentioning
confidence: 99%