期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2019
卷号：17
期号：2
页码：763-770
DOI：10.12928/telkomnika.v17i2.11785
出版社：Universitas Ahmad Dahlan
摘要：During the past decades, researches about automatic grading have become an interesting issue.
These studies focuses on how to make machines are able to help human on assessing students’ learning
outcomes. Automatic grading enables teachers to assess student's answers with more objective,
consistent, and faster. Especially for essay model, it has two different types, i.e. long essay and short
answer. Almost of the previous researches merely developed automatic essay grading (AEG) instead of
automatic short answer grading (ASAG). This study aims to assess the sentence similarity of short answer
to the questions and answers in Indonesian without any language semantic's tool. This research uses
pre-processing steps consisting of case folding, tokenization, stemming, and stopword removal. The
proposed approach is a scoring rubric obtained by measuring the similarity of sentences using the stringbased
similarity methods and the keyword matching process. The dataset used in this study consists of
7 questions, 34 alternative reference answers and 224 student’s answers. The experiment results show
that the proposed approach is able to achieve a correlation value between 0.65419 up to 0.66383 at
Pearson's correlation, with Mean Absolute Error (𝑀𝐴𝐸) value about 0.94994 until 1.24295. The proposed
approach also leverages the correlation value and decreases the error value in each method.
其他摘要：During the past decades, researches about automatic grading have become an interesting issue. These studies focuses on how to make machines are able to help human on assessing students’ learning outcomes. Automatic grading enables teachers to assess student's answers with more objective, consistent, and faster. Especially for essay model, it has two different types, i.e. long essay and short answer. Almost of the previous researches merely developed automatic essay grading (AEG) instead of automatic short answer grading (ASAG). This study aims to assess the sentence similarity of short answer to the questions and answers in Indonesian without any language semantic's tool. This research uses pre-processing steps consisting of case folding, tokenization, stemming, and stopword removal. The proposed approach is a scoring rubric obtained by measuring the similarity of sentences using the string-based similarity methods and the keyword matching process. The dataset used in this study consists of 7 questions, 34 alternative reference answers and 224 student’s answers. The experiment results show that the proposed approach is able to achieve a correlation value between 0.65419 up to 0.66383 at Pearson's correlation, with Mean Absolute Error (𝑀𝐴𝐸) value about 0.94994 until 1.24295. The proposed approach also leverages the correlation value and decreases the error value in each method.
