Automated Essay Scoring: Deep Reinforcement Learning and BigBird-BiLSTM Model Evaluation

Authors

  • Dandinker Suryakant King Abdul-Aziz University, Jeddah, KSA
  • Liza N. Bordios King Abdul-Aziz University, Jeddah, KSA

DOI:

https://doi.org/10.53469/jerp.2026.08(03).10

Keywords:

Automated essay grading, deep reinforcement learning, BigBird-BiLSTM, semantic features, evaluation metrics

Abstract

This paper evaluates the potential of Deep Reinforcement Learning (DRL) and BigBird-BiLSTM models in enhancing Automated Essay Grading (AEG) systems. Leveraging the Hewlett dataset, the study examines how these models handle semantic features and scalability challenges compared to existing frameworks. Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) highlight the strengths and limitations of each model.

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Published

2026-03-30

How to Cite

Suryakant, D., & Bordios, L. N. (2026). Automated Essay Scoring: Deep Reinforcement Learning and BigBird-BiLSTM Model Evaluation. Journal of Educational Research and Policies, 8(3), 50–54. https://doi.org/10.53469/jerp.2026.08(03).10

Issue

Vol. 8 No. 3 (2026)

Section

Articles

License

Copyright (c) 2026 Dandinker Suryakant, Liza N. Bordios

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


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