Automated Short-Answer Grading using Semantic Similarity based on Word Embedding
| Autor: | Fetty Fitriyanti Lubis, Mutaqin, Atina Putri, Dana Waskita, Tri Sulistyaningtyas, Arry Akhmad Arman, Yusep Rosmansyah |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | International Journal of Technology, Vol 12, Iss 3, Pp 571-581 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2086-9614 2087-2100 |
| DOI: | 10.14716/ijtech.v12i3.4651 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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