Automatic Essay Grading System Using Deep Neural Network
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- Vikkurty Sireesha 6 ,
- Nagaratna P. Hegde 6 ,
- Sriperambuduri Vinay Kumar 6 ,
- Alekhya Naravajhula 7 &
- Dulugunti Sai Haritha 8
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Essays are important for testing students’ academic scores, creativity, and being able to remember what they studied, but grading them manually is really expensive and time-consuming for a large number of essays. This project aims to implement and train neural networks to assess and grade essays automatically. The human grades given to the essays should be matched with grades generated from our automatic essay grading system consistently with minimum error. Automated essay grading can be used for evaluating essays written according to specific prompts or specific topics. It is the process of automating scoring system of essays without any human intervention and using computer programs. This system is most beneficial for educators since it helps reducing manual work and saves a lot of time. It not only saves a lot of time but also speeds up the process of learning feedback. We used deep neural networks in our system instead of traditional machine learning models.
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Acknowledgements
We thank Vasavi College of Engineering (Autonomous), Hyderabad for the support extended toward this work.
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Vikkurty Sireesha, Nagaratna P. Hegde & Sriperambuduri Vinay Kumar
Accolite Digital, Hyderabad, India
Alekhya Naravajhula
Providence, Hyderabad, India
Dulugunti Sai Haritha
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Correspondence to Vikkurty Sireesha .
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Sireesha, V., Hegde, N.P., Kumar, S.V., Naravajhula, A., Haritha, D.S. (2023). Automatic Essay Grading System Using Deep Neural Network. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. ICCIC 2022. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-2746-3_53
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Published : 02 October 2023
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TDNN : A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring
Cancan Jin , Ben He , Kai Hui , Le Sun
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[TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring](https://aclanthology.org/P18-1100) (Jin et al., ACL 2018)
- TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring (Jin et al., ACL 2018)
- Cancan Jin, Ben He, Kai Hui, and Le Sun. 2018. TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 1088–1097, Melbourne, Australia. Association for Computational Linguistics.
Deep-neural Automated Essay Scoring: A Review
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This project is an attempt to use different neural network architectures to build an accurate automated essay grading system to solve this problem. 1 Introduction Attempts to build an automated essay grading system dated back to 1966 when Ellis B. Page proved on The Phi Delta Kappan that a computer could do as well as a single human judge [1].
Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by humans. Although traditional AES models typically rely on manually designed features, deep neural network (DNN)-based AES models that obviate the need for feature engineering have recently attracted increased attention. Various DNN-AES models with different characteristics have ...
Neural Networks for Automated Essay Grading. H. Nguyen. Published 2016. Computer Science. TLDR. This project is an attempt to use different neural network architectures to build an accurate automated essay grading system to solve the problem of large cost and effort required for scoring. Expand.
Proposed Neural Networks for Automated Essay Grading. In this method, a single layer bi-directional LSTM accepting word vector as input. Glove vectors used in this method resulted in an accuracy of 90%. Ruseti et al. proposed a recurrent neural network that is capable of memorizing the text and generate a summary of an essay. The Bi-GRU network ...
It is observed that the simple feed-forward neural network delivers the best Kappa score after reviewing the results of our implementation. Both of our models, on the other hand, were able to detect and score the essay set’s score ranges. With a quadratic weighted Kappa score of 0.7758, we were able to meet our goal.
Automated essay scoring (AES) has emerged as a secondary or as a sole marker for many high-stakes educational assessments, in native and non-native testing, owing to remarkable advances in feature engineering using natural language processing, machine learning, and deep-neural algorithms.
Catulay J Magsael M Ancheta D Costales J (2021) Neural-Network Architecture Approach: An Automated Essay Scoring Using Bayesian Linear Ridge Regression Algorithm 2021 8th International Conference on Soft Computing & Machine Intelligence (ISCMI) 10.1109/ISCMI53840.2021.9654801 (196-200) Online publication date: 26-Nov-2021
Cancan Jin, Ben He, Kai Hui, and Le Sun. 2018. TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1088–1097, Melbourne, Australia. Association for Computational Linguistics. Cite ...
In this study, we extend the idea of two-stage model and propose a new approach for cross-prompt automated essay scoring as follows: instead of relying on a set of shallow features to identify a set of pseudo rated samples, we contrast the source and target prompts to construct a model that incorporates all potential transferable knowledge and use it to create pseudo ratings for the target prompt.
Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to manual scoring. Traditional AES models typically rely on handcrafted features, whereas deep neural networks (DNNs) -based AES models use automatic feature selection from the original texts. Furthermore, DNNs -AES models have recently achieved fantastic results and attracted increased ...