A novel automated essay scoring approach for reliable higher educational assessments
| Autor: | Hasan Rashaideh, Majdi Beseiso, Omar A. Alzubi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Higher education Computer science business.industry Deep learning 05 social sciences Educational technology 050301 education 050801 communication & media studies Automated essay scoring computer.software_genre Data science Education 0508 media and communications Educational assessment Language model Artificial intelligence business 0503 education computer Transformer (machine learning model) |
| Zdroj: | Journal of Computing in Higher Education. 33:727-746 |
| ISSN: | 1867-1233 1042-1726 |
| DOI: | 10.1007/s12528-021-09283-1 |
| Popis: | E-learning is gradually gaining prominence in higher education, with universities enlarging provision and more students getting enrolled. The effectiveness of automated essay scoring (AES) is thus holding a strong appeal to universities for managing an increasing learning interest and reducing costs associated with human raters. The growth in e-learning systems in the higher education system and the demand for consistent writing assessments has spurred research interest in improving the accuracy of AES systems. This paper presents a transformer-based neural network model for improved AES performance using Bi-LSTM and RoBERTa language model based on Kaggle’s ASAP dataset. The proposed model uses Bi-LSTM model over pre-trained RoBERTa language model to address the coherency issue in essays that is ignored by traditional essay scoring methods, including traditional NLP pipelines, deep learning-based methods, a mixture of both. The comparison of the experimental results on essay scoring with human raters concludes that the proposed model outperforms the existing methods in essay scoring in terms of QWK score. The comparative analysis of results demonstrates the applicability of the proposed model in automated essay scoring at higher education level. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink