Abstract
In this paper, we analyze the intelligent subdesign of the simulated marking system through an in-depth study of it. This paper proposes a correlation analysis-based quantification of N-element sense values and a rationality enhancement-based scoring fitting algorithm for English essays. This paper also extracts word features, sentence features, and chapter structure features in essays to fit English composition scores. Since not all students can complete the essays according to the topic requirements, a triage scoring model is used to separate the normal essays from the low-scoring essays. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. The results show that the English essay scoring fitting algorithm proposed in this paper can well improve the prediction accuracy of some data and solve the problem of skewed data where the scores show a normal distribution. This paper designs and implements an online mock examination system that incorporates an intelligent scoring system for essays, enabling it to meet the needs of teachers and students for online examinations and intelligent scoring.
Highlights
With the continuous evolution of IT technologies, especially the wave of smart education represented by artificial intelligence, cloud computing, big data, and the Internet of ings, these technologies have triggered a new round of educational revolution, which is a complete overturning of the traditional educational evaluation field, showing a magnificent blueprint of educational assessment and evaluation for education experts and scholars, and the majority of front-line teachers [1]
The algorithms used for automatic machine marking of objective questions in online examination systems are relatively simple and easy to implement and only need to compare candidates’ answers with standard answers to determine the correctness and give the examination score in real-time; it is obvious that the application of automatic marking techniques for objective question types represented by multiple-choice, judgment, matching, and fill-in-the-blank questions has greatly improved the markers’ performance, making the probability of misclassification or omission close to zero [4]
For the essay scoring algorithm, the support vector regression algorithm was used for normal essays that meet the requirements of the questions
Summary
With the continuous evolution of IT technologies, especially the wave of smart education represented by artificial intelligence, cloud computing, big data, and the Internet of ings, these technologies have triggered a new round of educational revolution, which is a complete overturning of the traditional educational evaluation field, showing a magnificent blueprint of educational assessment and evaluation for education experts and scholars, and the majority of front-line teachers [1]. The algorithms used for automatic machine marking of objective questions in online examination systems are relatively simple and easy to implement and only need to compare candidates’ answers with standard answers to determine the correctness and give the examination score in real-time; it is obvious that the application of automatic marking techniques for objective question types represented by multiple-choice, judgment, matching, and fill-in-the-blank questions has greatly improved the markers’ performance, making the probability of misclassification or omission close to zero [4]. In the field of automatic machine marking of subjective questions, especially in the field of Chinese subjective marking [5], the intelligent marking technology has become more than a word-by-word comparison and verification process. Given the different levels of understanding of each candidate and the diversity of Chinese language expressions, even if students can answer accurately, it is difficult to fully harmonize with the narrative and logic of standard answers [6], which undoubtedly poses a significant problem for the “less intelligent” computer assessment
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