Automated essay scoring (AES) of constructed responses in nursing examinations: An evaluation

Stephen, Tracey C.; Gierl, Mark J.; King, Serina

doi:10.1016/j.nepr.2021.103085

Cited by 14 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research carried out by [24] and [25] compared the agreement levels between human graders and an AES system. Remarkably, the results revealed a strong similarity in scoring, with AES systems requiring significantly less time.…”

Section: The Imperative For Automated Essay Scoring Systems and Their...mentioning

confidence: 99%

Evaluation of Transformer-Based Neural Language Models for Writing Feedback and Automated Essay Scoring

Abraha,

Nazir

2024

Preprint

View full text Add to dashboard Cite

Writing remains a challenging skill for many students due to inadequate feedback tools. There is a need to develop more effective tools for supporting students’ writing skill development. This study aims to evaluate the effectiveness of transformer-based neural language models for assessing and automatically scoring argumentative essays written by 8th-12th grade English Language Learners (ELLs). The students’ English essays were assessed and scored based on six criteria, including cohesion, syntax, vocabulary, phraseology, grammar, and conventions. The models were trained on real teacher feedback from 2700 scored essays. We also compared various transformer-based neural language models to find the most effective model. Several metrics were used for evaluation, with the root mean square error (RMSE) as the primary measure. The results show that a specific model, DeBERTa-v3-large, outperforms others in most categories. In conclusion, this study suggests that transformer-based neural language models, especially when using the DeBERTa-v3-large model, hold significant promise in improving automated essay scoring and feedback, potentially leading to enhanced writing skills among English language learners.

show abstract

Section: The Imperative For Automated Essay Scoring Systems and Their...mentioning

confidence: 99%

Evaluation of Transformer-Based Neural Language Models for Writing Feedback and Automated Essay Scoring

Abraha,

Nazir

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Revision is very limited in secondary school writing, and the ability to motivate students to spend some time on revision is likely to positively impact writing achievement. Research on students' perception of AES systems and the effect on motivation as well as on learning processes and learning outcomes is needed, particularly given the powerful and ubiquitous nature of generative AI (Stephen et al, 2021). Despite all the doomsday headlines about AI causing the end of writing, we believe that generative AI could, instead, be harnessed as a powerful tool to improve students' writing skills and habits.…”

Section: Limitations and Conclusionmentioning

confidence: 99%

Can AI provide useful holistic essay scoring?

Tate,

Steiss,

Bailey

et al. 2024

Computers and Education: Artificial Intelligence

View full text Add to dashboard Cite

“…The XGBoost algorithm is a common machine learning algorithm. The traditional automatic English essay scoring algorithm will first extract the shallow features and on-topic features of the essay when scoring [7]. The shallow features of the essay are the statistical features that only consider the structural organization without considering the meaning of the text.…”

Section: Machine Learning Based English Essay Scoring Algorithmmentioning

confidence: 99%

Machine Learning Models: A Study of English Essay Text Content Feature Extraction and Automatic Scoring

Shang,

Men,

2023

JICTS

View full text Add to dashboard Cite

Accurate automatic scoring of English essay is beneficial for both teachers and students in English teaching. This paper briefly introduced an XGBoost-based automated scoring algorithm for English essay. To improve the accuracy of the algorithm, a long short-term memory (LSTM) semantic model was introduced to extract semantic scoring features from essays. Finally, the improved XGBoost algorithm was compared with the traditional XGBoost and LSTM algorithms in a simulation experiment using five types of essay prompts. The results indicate that the improved XGBoost algorithm has the best performance for automatic scoring of English essay and also requires the shortest scoring time.

show abstract

Automated essay scoring (AES) of constructed responses in nursing examinations: An evaluation

Cited by 14 publications

References 15 publications

Evaluation of Transformer-Based Neural Language Models for Writing Feedback and Automated Essay Scoring

Evaluation of Transformer-Based Neural Language Models for Writing Feedback and Automated Essay Scoring

Can AI provide useful holistic essay scoring?

Machine Learning Models: A Study of English Essay Text Content Feature Extraction and Automatic Scoring

Contact Info

Product

Resources

About