Automated Essay Scoring (AES); A Semantic Analysis Inspired Machine Learning Approach
| Autor: | Ahsan Ikram, Billy Castle |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
T1
Artificial neural network Computer science business.industry Semantic analysis (machine learning) 05 social sciences Lexical diversity Cohesion (computer science) 02 engineering and technology L1 Automated essay scoring Python (programming language) Machine learning computer.software_genre 050105 experimental psychology QA76 Focus (linguistics) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business computer computer.programming_language |
| Zdroj: | 2020 12th International Conference on Education Technology and Computers. |
| Popis: | With the advancements in Artificial Intelligence (AI), ‘Automated Essay Scoring’ (AES) systems have become more and more prevalent in recent years. This research proposes an extension to the Coh-Metrix algorithm AES, with a focus on feature lists. Technical features, such as, referential cohesion, lexical diversity, and syntactic complexity are evaluated. Furthermore, it proposes the use of four novel semantic measures, including estimating the topic overlap between an essay and its brief. A prototype implementation, using neural networks, is used to test the individual and comparative performance of the newly proposed AES system. The results show a considerable improvement on the results obtained in the existing research for the original Coh-Metrix algorithm; from an adjacent accuracy of 91%, to an adjacent accuracy of 97.5% (and a QWK of 0.822). This suggests that the new features and the proposed system have the potential to improve essay grading and would be a good area for further research. |
| Databáze: | OpenAIRE |
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