2024-08-09-latif24a.md

title	booktitle	year	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Automatic Scoring of Students’ Science Writing Using Hybrid Neural Network	Proceedings of the 2024 AAAI Conference on Artificial Intelligence	2024	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, ANN, 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, ANN, 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 terms of 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 automatically score students’ science writing.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	latif24a	0	Automatic Scoring of Students’ Science Writing Using Hybrid Neural Network	97	106	97-106	97	false	Latif, Ehsan and Zhai, Xiaoming	given family Ehsan Latif given family Xiaoming Zhai	2024-08-09		Proceedings of the 2024 AAAI Conference on Artificial Intelligence	257	inproceedings	date-parts 2024 8 9	https://raw.githubusercontent.com/mlresearch/v257/main/assets/latif24a/latif24a.pdf