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- Title
Automated Short-Answer Grading using Semantic Similarity based on Word Embedding.
- Authors
Lubis, Fetty Fitriyanti; Mutaqin; Putri, Atina; Waskita, Dana; Sulistyaningtyas, Tri; Arman, Arry Akhmad; Rosmansyah, Yusep
- Abstract
Automatic short-answer grading (ASAG) is a system that aims to help speed up the assessment process without an instructor's intervention. Previous research had successfully built an ASAG system whose performance had a correlation of 0.66 and mean absolute error (MAE) starting from 0.94 with a conventionally graded set. However, this study had a weakness in the need for more than one reference answer for each question. It used a string-based equation method and keyword matching process to measure the sentences' similarity in order to produce an assessment rubric. Thus, our study aimed to build a more concise short-answer automatic scoring system using a single reference answer. The mechanism used a semantic similarity measurement approach through word embedding techniques and syntactic analysis to assess the learner's accuracy. Based on the experiment results, the semantic similarity approach showed a correlation value of 0.70 and an MAE of 0.70 when compared with the grading reference.
- Subjects
SCORING rubrics; VOCABULARY
- Publication
International Journal of Technology, 2021, Vol 12, Issue 3, p571
- ISSN
2086-9614
- Publication type
Article
- DOI
10.14716/ijtech.v12i3.4651