Hybrid Approach to Automated Essay Scoring: Integrating Deep Learning Embeddings with Handcrafted Linguistic Features for Improved Accuracy

Muhammad Faseeh, Abdul Jaleel, Naeem Iqbal, Anwar Ghani, Akmalbek Abdusalomov, Asif Mehmood () and Young-Im Cho ()
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Muhammad Faseeh: Department of Electronic Engineering, Jeju National University, Jeju-si 63243, Republic of Korea
Abdul Jaleel: Department of Information Technology, Asia Pacific International College, Parramatta, Sydney 2150, Australia
Naeem Iqbal: Centre for Secure Information Technologies (CSIT), Momentum One Zero (M1.0), School of Electronics Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT3 9DT, UK
Anwar Ghani: Department of Computer Science, International Islamic University, Islamabad 44000, Pakistan
Akmalbek Abdusalomov: Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent 100066, Uzbekistan
Asif Mehmood: Department of Biomedical Engineering, College of IT Convergence, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea
Young-Im Cho: Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea

Mathematics, 2024, vol. 12, issue 21, 1-29

Abstract: Automated Essay Scoring (AES) systems face persistent challenges in delivering accuracy and efficiency in evaluations. This study introduces an approach that combines embeddings generated using RoBERTa with handcrafted linguistic features, leveraging Lightweight XGBoost (LwXGBoost) for enhanced scoring precision. The embeddings capture the contextual and semantic aspects of essay content, while handcrafted features incorporate domain-specific attributes such as grammar errors, readability, and sentence length. This hybrid feature set allows LwXGBoost to handle high-dimensional data and model intricate feature interactions effectively. Our experiments on a diverse AES dataset, consisting of essays from students across various educational levels, yielded a QWK score of 0.941. This result demonstrates the superior scoring accuracy and the model’s robustness against noisy and sparse data. The research underscores the potential for integrating embeddings with traditional handcrafted features to improve automated assessment systems.

Keywords: automated essay scoring; RoBERTa embeddings; handcrafted features; LwXGBoost; RoBERTa (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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