Research on Automatic Scoring Algorithm of College English Composition Based on Tangency Feature

This study aims to develop an automatic data correction system for correcting the public construction data. The unstructured nature of the construction data presents challenges for its management. The different user habits, time-consuming system operation, and long pretraining time all make the data management system full of data in an inconsistent format or even incorrect data. Processing the construction data into a machine-readable format is not only time-consuming but also labor-intensive. Therefore, this study used Taiwan’s public construction data as an example case to develop a natural language processing (NLP) and machine learning-based text classification system, coined as automatic correction system (ACS). The developed system is designed to automatically correct the public construction data, meanwhile improving the efficiency of manual data correction. The ACS has two main features: data correction that converts unstructured data into structured data; a recommendation function that provides users with a recommendation list for manual data correction. For implementation, the developed system was used to correct the data in the public construction cost estimation system (PCCES) in Taiwan. We expect that the ACS can improve the accuracy of the data in the public construction database to increase the efficiency of the practitioners in executing projects. The results show that the system can correct 18,511 data points with an accuracy of 76%. Additionally, the system was also validated to reduce the system operation time by 51.69%.

With the rapid development of science and technology, big data is gradually being applied to the field of English writing teaching. On this basis, an automatic correction system is developed to automatically evaluate and feedback students’ compositions. This has become the future of feedback research and development Directions and trends. Writing is a cyclical creation process. The correction and feedback of composition is very important in writing teaching. However, in traditional teaching, due to the heavy workload of teachers, it is difficult to give feedback sentence by sentence, and there may also be inefficient feedback. In view of this, it is necessary to conduct an in-depth discussion on English composition based on automatic correction system, and on this basis, explore the automatic correction service system for English composition based on big data, so as to provide inspiration and reference for the evaluation of English writing teaching. The study found that the automatic correction service system came into being, which made up for the shortcomings of composition feedback in traditional teaching. Moreover, the automatic correction service system for English composition based on big data has many advantages compared with teacher correction, and it can accurately provide Students point out errors in writing, which can reduce teachers’ workload and improve teaching quality. In addition, this article also summarizes the shortcomings of the automatic correction service system and puts forward suggestions in order to better apply the automatic correction service system to English writing teaching.

English grammar error correction, as a subtask in the field of natural language processing, can provide second language learners with services such as automatic correction of grammatical errors and article touch-up. Based on the current mainstream neural machine grammar error correction methods, this paper proposes an English grammar error correction model based on the Transformer model incorporating the replication mechanism (C-Transformer). Combining this model with the pinyin detection algorithm and the feedback filtering algorithm, and expanding the training data by creating pseudo-parallel sentence pairs, an automatic English grammar error correction system is successfully designed. Compared with the traditional CAMB grammar error correction model, the accuracy, recall and F 0.5 metrics of this paper’s model are improved by 16.68%, 20.38% and 17.33%, respectively. Moreover, in the English composition correction experiments for Chinese students, the average precision rate, recall rate and F1 value of this paper’s model for various types of grammatical error correction reached 84.70%, 71.85% and 77.75%, respectively, proving the effectiveness and superiority of this paper’s model. In addition, using the designed English grammar automatic error correction system to conduct teaching experiments, students’ knowledge of various grammar questions, especially in writing, was significantly improved, indicating that the designed system has a better application effect, which is of great significance for improving the effectiveness of university English teaching.

For correction system of English pronunciation errors, the level of correction performance and the reliability, practicability, and adaptability of information feedback are the main basis for evaluating its excellent comprehensive performance. In view of the disadvantages of traditional English pronunciation correction systems, such as failure to timely feedback and correct learners’ pronunciation errors, slow improvement of learners’ English proficiency, and even misleading learners, it is imperative to design a scientific and efficient automatic correction system for English pronunciation errors. High‐sensitivity acoustic wave sensors can identify English pronunciation error signal and convert the dimension of collected pronunciation signal according to channel configuration information; acoustic wave sensors can then assist the automatic correction system of English pronunciation errors to filter out interference components in output signal, analyze real‐time spectrum, and evaluate the sensitivity of the acoustic wave sensor. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounds the current research status and significance of the design of automatic correction system for English pronunciation errors, elaborates the development background, current status and future challenges of high‐sensitivity acoustic wave sensor technology, introduces the methods and principles of time‐domain signal amplitude measurement and pronunciation signal preprocessing, carries out the optimization design of pronunciation recognition sensors, performs the improvement design of pronunciation recognition processors, proposes the hardware design of automatic correction system for English pronunciation errors based on the assistance of high‐sensitivity acoustic wave sensors, analyzes the acquisition program design for English pronunciation errors, implements the parameter extraction of English pronunciation error signal, discusses the software design of automatic correction system for English pronunciation errors based on the assistance of high‐sensitivity sound wave sensor, and finally, conducts system test and its result analysis. The study results show that the automatic correction system of English pronunciation errors assisted by the high‐sensitivity acoustic wave sensors can realize the automatic correction of the amplitude linearity, sensitivity, repeatability error, and return error of English pronunciation errors, which has the robust functions of automatic real‐time data collection, processing, saving, query, and retesting. The system can also minimize external interference and improve the accuracy of acoustic wave sensors’ sensitivity calibration, and it provides functions such as reading and saving English pronunciation error signals and visual operation, which effectively improves the ease of use and completeness of the correction system. The study results in this paper provide a reference for the further researches on the automatic correction system design for English pronunciation errors assisted by high‐sensitivity acoustic wave sensors.

In this paper, we evaluate an automatic correction essay system used as an assessment tool on a gamified course. The gamified course uses a question/answer battle as its main strategy to engage and empower students’ learning. As educational methodology, it uses peer review strategy on flipped classrooms. In such context, it was developed an automatic essay correction system, called Milsa, to be used by students out off the classroom. Milsa is used to insert questions and template answers, to automatically correct the questions based on template answers, to show the students the question, the answer and the resulting grade and, finally, to learn from the users’ feedback on the answer’s evaluation. Milsa is used as an assessment tool to measure students’ development at the gamified course. Then, we evaluate the contribution of Milsa to the students’ learning process at the course. We conducted and analyzed tests based on data collected at classes and Milsa: individual flow aligned between the classes, the assessments and an Intrinsic Motivation Inventory (IMI) questionnaire. Finally, we discusses the advantages and disadvantages of the use of Milsa as a social network that helps students with disabilities.

With the reform of China's education industry, more and more universities are using computers to conduct examinations. For the automatic correction of essays as subjective questions, existing automatic English text scoring systems suffer from insufficient extraction of coherence information and low accuracy when analysing text coherence. Therefore, this paper proposes an unsupervised semantic coherence analysis model for English texts based on sentence semantic graphs, taking Chinese students' English compositions as the research context. Guided by the semantic coherence theory, the English text is represented as a sentence semantic graph, and an improved VF2 subgraph matching algorithm is used to mine the frequently occurring subgraph patterns in the sentence semantic graph. After that, the set of frequent subgraphs is generated by filtering the subgraph patterns according to their frequencies, and the subgraph frequency of each frequent subgraph is calculated separately. Finally, the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graphs are extracted to quantify the overall coherence quality of English texts. The experimental results show that the model proposed in this paper has higher accuracy and practical value compared with the current methods of coherence analysis.

In this paper, a system for automatic detection and correction of mispronunciation of native Chinese learners of English by speech recognition technology is designed with the help of radiomagnetic pronunciation recording devices and computer‐aided software. This paper extends the standard pronunciation dictionary by predicting the phoneme confusion rules in the language learner’s pronunciation that may lead to mispronunciation and generates an extended pronunciation dictionary containing the standard pronunciation of each word and the possible mispronunciation variations, and automatic speech recognition uses the extended pronunciation dictionary to detect and diagnose the learner’s mispronunciation of phonemes and provides real‐time feedback. It is generated by systematic crosslinguistic phonological comparative analysis of the differences in phoneme pronunciation with each other, and a data‐driven approach is used to do automatic phoneme recognition of learner speech and analyze the mapping relationship between the resulting mispronunciation and the corresponding standard pronunciation to automatically generate additional phoneme confusion rules. In this paper, we investigate various aspects of several issues related to the automatic correction of English pronunciation errors based on radiomagnetic pronunciation recording devices; design the general block diagram of the system, etc.; and discuss some key techniques and issues, including endpoint detection, feature extraction, and the system’s study of pronunciation standard algorithms, analyzing their respective characteristics. Finally, we design and implement a model of an automatic English pronunciation error correction system based on a radiomagnetic pronunciation recording device. Based on the characteristics of English pronunciation, the correction algorithm implemented in this system uses the similarity and pronunciation duration ratings based on the log posterior probability, which combines the scores of both, and standardizes this system scoring through linear mapping. This system can achieve the purpose of automatic recognition of English mispronunciation correction and, at the same time, improve the user’s spoken English pronunciation to a certain extent.

In order to solve the problems of low accuracy and efficiency of traditional English composition automatic evaluation system, a new English composition automatic evaluation system based on B/S architecture is proposed and designed. This paper analyzes the operation process of the English composition automatic evaluation system. The hardware part designs the English composition image recognition module, the English composition automatic evaluation module and the English composition repetition rate detection module. The software part constructs the English composition text matrix, and uses singular value decomposition method to solve the English composition text matrix, and calculates the relevant indicators of English composition automatic evaluation. The experimental results show that, compared with the traditional English composition automatic evaluation system, the evaluation results of the designed system are more accurate and efficient.

In order to solve the problems of low accuracy and slow correction speed in traditional singing intonation correction system, an automatic singing intonation correction system based on deep learning is proposed. In the hardware, floating-point DSP and TDSP-TF984 chip are selected as the core chips of automatic correction processor of singing intonation. The data input module and parameter calculation module of singing intonation are designed to improve the singing intonation data collector. In the software, the group delay estimation method is used to collect the singing intonation signal, and the deep learning algorithm is used to decompose the false component of the singing intonation signal. The autocorrelation function and characteristic distribution operator of the singing intonation signal are obtained to realise the singing intonation signal correction. The experimental results show that the highest accuracy of the proposed system is about 97.8%, and the shortest correction time is about 1 s.

With the increasing improvement of living standards, healthy long-distance running has become one of the current ways of aerobic physical exercise. Long-distance running is beneficial to many bodily functions such as neck, spine, and heart, but inappropriate long-distance running can easily damage the joints of the human body. In order to make more fitness athletes run with a healthy and reasonable posture, this paper studies the automatic recognition and correction system of running movements based on computer vision technology. Based on the analysis of human motion detection methods and human motion recognition methods, an automatic recognition and correction system for running actions is designed. In order to understand the accuracy of the system in the recognition of wrong actions, this paper compares the designed system with the traditional system. The results show that the system designed in this paper has good accuracy and can control the deviation within a reasonable range.

Part‐of‐speech tagging for English composition is the basis for automatic correction of English composition. The performance of the part‐of‐speech tagging system directly affects the performance of the marking and analysis of the correction system. Therefore, this paper proposes an automatic scoring model for English composition based on article part‐of‐speech tagging. First, use the convolutional neural network to extract the word information from the character level and use this part of the information in the coarse‐grained learning layer. Secondly, the word‐level vector is introduced, and the residual network is used to establish an information path to integrate the coarse‐grained annotation and word vector information. Then, the model relies on the recurrent neural network to extract the overall information of the sequence data to obtain accurate annotation results. Then, the features of the text content are extracted, and the automatic scoring model of English composition is constructed by means of model fusion. Finally, this paper uses the English composition scoring competition data set on the international data mining competition platform Kaggle to verify the effect of the model.

FOR a sample of more than 500 freshmen at California State University, Northridge (CSUN) who had elected to take the general English composition course (English 155) during their first semester as well as for male and female subsamples of comparable size, comparisons were made of the predictive validity of the California State University and Colleges English Placement Test (CSUC-EPT) (Educational Testing Service, 1977a, b), the Test of Standard Written English (TSWE) of the Admissions Testing Program of the College Board (Educational Testing Service, 1974-1978, 1978a, 1978b), and the Verbal and Mathematics portions of the College Board Scholastic Aptitude Test (CEEB—SAT—Verbal and CEEB-SAT—Mathematics) (Educational Testing Service, 1948-1978) with respect to each of four criterion measures: (a) Grades in Fall Semester 1977 English 155—Written Expression, (b) Fall Semester 1977 GPA, (c) Spring Semester 1978 GPA, and (d) 1977-1978 Academic Year (Total CSUN GPA. Statistical findings suggested these conclusions : (1) the TSWE requiring 30 minutes of test-taking time is very nearly as valid as the CSUC-EPT, which involves two hours and forty-five minutes of working time, in prediction of success in the general English composition course; (2) both the TSWE and the CSUC-EPT appear to be more valid than is the CEEB—SAT—Verbal measure in placing correctly freshman students in an English composition course (from the standpoint of an accurate prediction of their achievement); (3) a short multiple-choice examination requiring knowledge of principles about grammar, usage, choice of words and phrases, and syntax such as the TSWE or a subtest of the CSUCEPT reflecting similar competencies provides a more accurate prediction of success in a basic English composition course emphasizing writing skills than does an essay examination requiring the actual demonstration of writing skills; (4) level of achievement in high school is nearly as valid an indicator of overall academic performance during the freshman year as are scores on the CEEB-SAT— Verbal or Mathematics, the TSWE, or the CSUC-EPT—Total Score measures; and (5) an optimally weighted combination of two or more of these measures is typically more predictive of performance in either the English composition course or in overall academic achievement during the freshman year than is any one of these variables alone. In general, validity coefficients for females are higher than those for males irrespective of which criterion measure is being predicted.

The detection of grammatical errors in English composition is an important task in the field of NLP. The main purpose of this task is to check out grammatical errors in English sentences and correct them. Grammatical error detection and correction are important applications in the automatic proofreading of English texts and in the field of English learning aids. With the increasing influence of English on a global scale, a huge breakthrough has been made in the task of detecting English grammatical errors. Based on machine learning, this paper designs a new method for detecting grammatical errors in English composition. First, this paper implements a grammatical error detection model based on Seq2Seq. Second, this paper implements a grammatical error detection and correction scheme based on the Transformer model. The Transformer model performs better than most grammar models. Third, this paper realizes the application of the BERT model in grammar error detection and error correction tasks, and the generalization ability of the model has been significantly enhanced. This solves the problem that the forward and backward cannot be merged when the Transformer trains the language model. Fourth, this paper proposes a method of grammatical error detection and correction in English composition based on a hybrid model. According to specific application scenarios, the corresponding neural network model is used for grammatical error correction. Combine the Seq2Seq structure to encode the input sequence and automate feature engineering. Through the combination of traditional model and deep model, the advantages are complemented to realize grammatical error detection and automatic correction.

Of great importance in the educational process, exams based on single or multiple choise tests, both online and offline, nowadays are becoming more widespread, offering a high efficiency in the process of assessing the knowledge of pupils and students, as well as in the process of correcting tests. In order to meet the growing demands of having an easy-to-use tool for creating and correcting exam tests, the author designed an automatic tests correction system based on optical character recognition and an original response detection algorithm.The presented system offers multiple facilities such as: easy construction of questions sets (quizzes); generating different variants of sets starting from a source set; automatic generation and printing of exam sets and questionnaires; automatic correction of answer sheets; the possibility of manual verification of any corrected test; displaying the results in the application graphical interface, along with the exam statistics.

Scholars have conducted research on the errors made by language learners in writing both theoretically and practically, and they have made considerable breakthroughs in Second Language Acquisition (SLA) with respect of writing. The learners’ use of articles in English writing displays a certain pattern with factors such as influence from a learner’s mother language, personal idiosyncracy or common misunderstanding. Thus it is feasible to judge whether an article is used correctly in a certain context on a rule basis. This paper adopts a rule-based method, which lets the computer learn how to use articles from empirical sources and automatic correct the Definite Article Redundancy Errors (DAREs) committed by students in their English compositions. With the help of both grammatical and contextual information, the rules in our system are deduced from authentic examples and possess authority and accuracy. The system also allows teachers to add or alter these rules flexibly by either examining the actual cases or updating their professional knowledge as they wish. By doing this, we hope the quality and efficiency of marking compositions in Chinese colleges will be improved.