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 Bobomirzaevich Abdusalomov, Asif Mehmood, Young Im Cho

Mathematics 2024 article cited by 39

Source Open-access PDF More in Education & Everyday

importance 4/10 ai assisted Educational Technology and Assessment education technology stage: research

Problem

Automated essay scoring systems face persistent challenges in delivering accurate and efficient evaluations.

Why it matters

Accurate automated grading could enable reliable essay assessment at scale for education.

What the AI did

RoBERTa-generated embeddings were combined with handcrafted linguistic features (grammar errors, readability, sentence length) and fed into a Lightweight XGBoost model for scoring.

Results

QWK score of 0.941 on a diverse AES dataset of essays from students across educational levels

Impact

The stated QWK of 0.941 supports improved scoring accuracy on the tested dataset.

Record

Venue	Mathematics
Published	2024
Work type	article
Authors	Muhammad Faseeh, Abdul Jaleel, Naeem Iqbal, Anwar Ghani, Akmalbek Bobomirzaevich Abdusalomov, Asif Mehmood, Young Im Cho
Citations	39 (OpenAlex, at import time)
References	48
Model or method	RoBERTa embeddings + Lightweight XGBoost (LwXGBoost)
Organization	not stated in source
Primary source	https://doi.org/10.3390/math12213416
Open access	gold
Keywords	automated essay scoring, RoBERTa, XGBoost, QWK, hybrid features, deep learning embeddings

Importance rating

4 / 10

real but narrower advance

Better automated essay scoring could aid assessment at scale, but abstract gives no scale or deployment evidence; a modest accuracy improvement on one dataset.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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