# Automatic Scoring of Students' Science Writing Using Hybrid Neural Network

arXiv: https://arxiv.org/abs/2312.03752 · HTML: https://mlsanity.com/paper/2312.03752/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Applying Large Language Models and Chain-of-Thought for Automatic Scoring](https://mlsanity.com/paper/2312.03748/) (2023)
- [Fine-tuning ChatGPT for Automatic Scoring](https://mlsanity.com/paper/2310.10072/) (2023)
- [Context Matters: A Strategy to Pre-train Language Model for Science Education](https://mlsanity.com/paper/2301.12031/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education](https://mlsanity.com/paper/2410.21287/) (2024)
- [G-SciEdBERT: A Contextualized LLM for Science Assessment Tasks in German](https://mlsanity.com/paper/2402.06584/) (2024)
- [Generative AI in Training and Coaching: Redefining the Design Process of Learning Materials](https://mlsanity.com/paper/2508.11662/) (2025)

## Abstract (arXiv)

This study explores the efficacy of a multi-perspective hybrid neural network (HNN) for scoring student responses in science education with an analytic rubric. We compared the accuracy of the HNN model with four ML approaches (BERT, AACR, Naive Bayes, and Logistic Regression). The results have shown that HHN achieved 8%, 3%, 1%, and 0.12% higher accuracy than Naive Bayes, Logistic Regression, AACR, and BERT, respectively, for five scoring aspects (p<0.001). The overall HNN's perceived accuracy (M = 96.23%, SD = 1.45%) is comparable to the (training and inference) expensive BERT model's accuracy (M = 96.12%, SD = 1.52%). We also have observed that HNN is x2 more efficient in training and inferencing than BERT and has comparable efficiency to the lightweight but less accurate Naive Bayes model. Our study confirmed the accuracy and efficiency of using HNN to score students' science writing automatically.