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Vol.12, No.1, February 2023. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Automatic Short Answer Grading onHigh School’s E-Learning Using Semantic Similarity Methods
Daniel Wilianto, Abba Suganda Girsang
© 2023 Daniel Wilianto, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 12, Issue 1, Pages 297-302, ISSN 2217-8309, DOI: 10.18421/TEM121-37, February 2023.
Received: 11 June 2022. Revised: 16 November 2022.
Abstract:
Grading students’ answers has always been a daunting task which takes a lot of teachers’ time. The aim of this study is to grade students’ answers automatically in a high school’s e-learning system. The grading process must be fast, and the result must be as close as possible to the teacher assigned grades. We collected a total of 840 answers from 40 students for this study, each already graded by their teachers. We used Python library sentence-transformers and three of its latest pre-trained machine learning models (all-mpnet-base-v2, all-distilroberta-v1, all-MiniLM-L6-v2) for sentence embeddings. Computer grades were calculated using Cosine Similarity. These grades were then compared with teacher assigned grades using both Mean Absolute Error and Root Mean Square Error. Our results showed that all-MiniLM-L6-v2 gave the most similar grades to teacher assigned grades and had the fastest processing time. Further study may include testing these models on more answers from more students, also fine tune these models using more school materials.
Keywords –automated grading, sentence-transformers, machine learning, cosine similarity, mean absolute error, root mean square error. |
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