Summary of Hey Ai Can You Grade My Essay?: Automatic Essay Grading, by Maisha Maliha and Vishal Pramanik
Hey AI Can You Grade My Essay?: Automatic Essay Grading
by Maisha Maliha, Vishal Pramanik
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to automatic essay grading (AEG) that leverages collaborative and transfer learning to improve grading accuracy. The proposed model consists of three networks: one for assessing grammatical and structural features, another for evaluating overall idea, and a third for combining the two scores. This multi-network architecture allows for more effective feature extraction and learning, outperforming state-of-the-art models in AEG with an accuracy of 85.50%. The model’s design is motivated by the limitations of single-network AEG approaches, which may struggle to capture all aspects of human-written essays. By transferring knowledge between networks, the proposed model can adapt to different essay styles and structures, leading to more accurate grading. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to grade essays using artificial intelligence. The current methods use one network to do everything, but this might not be good enough because it’s hard for one network to understand all aspects of an essay. This research introduces a new model that uses three networks working together. One network checks the grammar and sentence structure, another evaluates the main idea, and the third combines these scores to give a final grade. This approach does better than previous models, with an accuracy rate of 85.50%. The goal is to make grading more accurate and efficient. |
Keywords
» Artificial intelligence » Feature extraction » Transfer learning
