A Hybrid Automated Essay Scoring Using NLP and Random Forest Regression
- DOI
- 10.2991/978-94-6463-094-7_35How to use a DOI?
- Keywords
- Automated Essay Scoring; Linear Regression; Random Forest; Deep Learning; Natural Language Processing
- Abstract
Assessing the performance of students through subjective assessments namely essays is critical in measuring their achievement during the learning process in an educational system. The essay test will evaluate the student’s ability to remember and express their ideas or opinions toward certain topics. A teaching staff is usually required to assess and grade the students’ essays. This paper presents a hybrid Automated Scoring System based on Natural Language Processing (NLP) and Random Forest Regression. The model focused on regression task where the predicted score is in a continuous value. Natural Language Processing (NLP) has also been applied in this work to extract features from essays. Finally, all the proposed model is compared to Linear Regression and Deep Learning and are then evaluated to compare the performance of the models by using the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
- Copyright
- © 2022 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Muhammad Zaim Azri Bin Azahar AU - Khairil Imran Bin Ghauth PY - 2022 DA - 2022/12/27 TI - A Hybrid Automated Essay Scoring Using NLP and Random Forest Regression BT - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022) PB - Atlantis Press SP - 448 EP - 457 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-094-7_35 DO - 10.2991/978-94-6463-094-7_35 ID - Azahar2022 ER -