# Construction of Intelligent Evaluation Platform Based on Random Matrix iWrite College English Writing

Authors: Lei Gao; Jiatong Xu; Xiaoda Fu; Juan Li
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The advantages of iWrite English writing teaching and marking system 3.0 and the case study of English writing in higher education based on this system have a positive effect on forming a new model of English writing teaching in higher education under the background of Internet + and improving and solving the main problems of English writing teaching in higher education. Although the current intelligent marking systems have made a lot of achievements, they have not fundamentally solved the problem of the rationality of intelligent marking of subjective questions. In order to better perceive the sense of speech in English essays in depth and improve the rationality problem of intelligent marking, this study proposes a quantification of N-element sense of speech value based on correlation analysis and a scoring fitting algorithm for English essays based on rationality enhancement. The quantification of perceptual value is done by obtaining the N components of the composition and calculating their support in the corpus. In the daily teaching work, teachers should actively cooperate with the marking system to improve their own teaching level and explore ways and methods of students’ writing training, so as to cultivate more English talents. In addition, word features, sentence features, and chapter structure features were extracted from the essays to fit the English essay scores. Since not all students were able to complete the essays according to the requirements of the questions, a streaming scoring model was used to separate the normal essays from the low-scoring essays. The low-scoring essays were scored using the k-nearest neighbor algorithm, while the normal essays were fitted to the test takers’ scores using the support vector regression algorithm. 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 study addresses this problem by using a rationality enhancement method, which gives a corresponding penalty factor according to the distribution of the data set.


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## Body

## 1. Introduction

With the rapid development of science and technology, computer information technology is widely used in all aspects of life, such as education, finance, medical care, traffic, transportation, and other fields. In this context, the education industry has been affected as never before. Many experts and scholars in the academic field have organically combined computer information technology with traditional education and carried out a lot of explorations, trials, and researches on teaching based on computer technology, expecting that computer information technology can better assist teaching and serve teaching, so that the majority of educators can be freed from the heavy teaching work and assignment correction tasks, thus reducing teachers’ work intensity, increasing teaching efficiency, and improving students’ learning [1]. The iWrite English Writing Teaching and Review System 3.0 (hereinafter referred to as iWrite 3.0) is an intelligent English composition review system developed by the Foreign Language Teaching and Research Press in cooperation with experts and scholars from the China Foreign Language Education Research Center of Beijing Foreign Studies University [2]. The system is in line with the writing standards of major exams at home and abroad and is able to review students’ essays based on four dimensions: language, content, chapter structure, and technical specifications, helping students to improve the relevance and coherence of their essays on the basis of their knowledge of English grammar and relieving English teachers of the burden of reviewing essays.In order to be able to count students’ objective part of the test, students need to submit their answers after taking their own test, which is usually done by filling in the answer key and then reading it through a cursor reader [3]. Although this mode can accomplish the purpose of the teacher’s statistics such as error rate, students also need a certain amount of time in filling out the answer card and in the need to revise the answer; if they do not wipe clean the incorrectly filled out options, it will lead to a machine misjudgment situation [4]. The disadvantage of the cursor reader is that it cannot achieve complete correctness for the objective part of the marking. This is only for the objective part of the questions, and for the subjective part of the cursor reader, it is completely helpless. Teacher has no time to correct the composition questions, and students cannot correct grammar and other errors in time. In the long run, students will have more and more serious problems with writing, and even the mistakes that occurred last time will still occur next time. Although the writing part of English does not take up as much weight as the language part, the mistakes in the English composition part, if ignored, will lead to a serious bifurcation of students’ performance in composition. If problems in writing and essay scores are not taken seriously in practice exams, then a portion of the score will be lost in the official college entrance exam, which runs counter to the original intent of the problem-based approach. Teachers do occasionally review students’ essays, only in the more formal exams. They also correct essays in a single line, basically looking for the main errors and marking them on the paper, without marking the reasons for them. This pattern of correction can lead to some students who are not so basic to know how to correct. Students may mistakenly think that what they have written is correct for the errors not marked by the teacher. Furthermore, since essays are subjective topics, the marks marked by different teachers can vary. Even the same teacher may give different scores to the same essay depending on their mood. In order to solve these problems, various online examination systems have been established [5]. For students who need to correct their answers to objective questions instead of wiping their answer cards, they only need to click on the corrected answer and the answer that needs to be corrected is corrected. This approach not only avoids the time wasted by candidates filling in their answer cards but also greatly improves the correctness and efficiency of marking. Systems for automatic marking of English essays have also been developed, each of which has its own characteristics, but the results are not yet ideal for intelligent marking [6–8].This study focuses on the construction of an English online mock examination system platform and the intelligent reading of essays. Since I am a master’s student in software engineering, I first built an online examination platform, which can realize students’ online examination function and automatically calculate objective scores, etc. In the context of the Internet + era, English teachers in higher education must keep up with the times, strengthen their learning, and actively cultivate personal information literacy in order to comply with the ever-changing trend of the times. As the times change, teachers’ teaching philosophy, teaching methods, and teaching approaches cannot remain unchanged. Teachers of English in higher education must actively accept new things from the Internet, dare to experiment, study teaching contents, improve teaching methods, make full use of the resources of the Internet platform, reflect on the problems in writing teaching and improve them, and constantly improve their teaching ability. In this study, we analyze in detail the advantages and shortcomings of the iWrite 3.0 system and its significance for supporting college English writing teaching in the form of a case study. The system can also be used free of charge for teaching research using the text data readily available in the system. In conclusion, nothing is perfect, and there are still some areas for improvement in the current iWrite 3.0 system, but I believe that a combination of manual and machine review is the most effective solution to the current shortcomings of the iWrite 3.0 system. iWrite 3.0 has many advantages and features for English teachers to try and discover.


## 2. Related Work

In the 1960s, when the development of computer hardware and software was relatively backward, a team at Duke University had already developed the PEG (Project Essay Grade) essay grading system [9]. At this time, natural language processing technology was still in its infancy and was based on working life experience with natural language, so the PEG system was a rule-based approach that focused on the form to grade essays. The system uses word length to determine students’ mastery of vocabulary, sentence length to predict students’ mastery of sentence structure, and so on. It extracts these simple and easily quantifiable text characteristics and then performs multiple linear regression analyses on these variables to produce a linear regression equation. When the essay is to be graded, each feature variable is entered to determine the score of the essay. This kind of extraction of simple features, but not the content features of the essay, makes it easy for candidates to find loopholes, which is why the PEG system was not widely used at that time. IEA (Intelligent Essay Assessor) is a system that scores essays based on semantic statistics of words. In contrast to PEG, IEA is a content-based scoring system [10]. Its development team claims that IEA can measure both semantics and essay content.This is due to the fact that IEA uses latent semantic analysis (LSA), which is a model for information retrieval that can be used to filter words in the text, which is equivalent to mining the keywords in the text [11]. In this way, LSA is used on the training and test sets to represent keywords as spatial vectors, and then the semantic similarity between them is compared to measure the semantic “readability” of the text. E-rater is a hybrid scoring system developed by the U.S. Educational Testing Service, which is used in many large-scale exams, including the TOEFL exam, where essays are graded [12]. TOEFL essay marking generally requires two scorers, with E-rater acting as one scorer and only introducing another scorer for manual marking if it differs significantly from the manually marked score. This shows that the E-rater scoring system is relatively well done, because it uses a variety of techniques that incorporate not only the advantages of the PEG system for evaluating language quality but also the IEA system for extracting features for text content quality. E-rater scores these features using the same linear regression analysis used by PEG.In addition to these three representative studies, there are also those that detect semantic errors by measuring the consistency of semantic spatial changes and thus score them [13] and those that score by extracting articulation and word features using hierarchical classification methods, etc. [14]. With the continuous development of mobile Internet, big data, artificial intelligence, etc., modern lifestyle gradually tends to be intelligent and automated, and achieving accurate and fast matching of semantic similarity between two texts has a profound impact on applications such as intelligent search, intelligent reading, and intelligent translation. Text matching is a core problem in the field of natural language process (NLP) [15], and the research of text matching has been applied to many fields, such as machine translation [16], information retrieval [17], paraphrase identification (PI) [18], automatic question and answer [19], and other tasks. Information retrieval can be regarded as relevance matching between query text and candidate text, machine translation can be regarded as matching different linguistic expressions of the same semantics, and automatic Q&A can be regarded as matching between the given questions and answers [20]. For different tasks, it is necessary to build corresponding text matching models according to the characteristics of the tasks, and how to use a better model to solve the text matching problem has become the focus and challenge of the research.


## 3. iWrite English Writing and Reviewing System Based on Random Matrix

### 3.1. Random Matrix Text Matching Features

In the evaluation process, the computer only needs to compare the strings of standard answers and students’ answers to give scores directly for objective questions, while for subjective questions, the computer needs to compare students’ answers with standard answers and give scores according to the scores of students’ answers. In order to solve the problem of intelligent assessment of subjective questions, a deep text matching model is introduced to determine the semantic similarity between students’ answers and standard answers and to give scores, and word separation and text vectorization operations are required before inputting the deep text matching model. The technical route of the intelligent review function is shown in Figure1.Figure 1
The technical route of the intelligent review function.In the field of education, the marking of scripts occupies an extremely important position in teachers’ work tasks, and as the number of educated students in society continues to rise, the workload of teachers marking scripts is becoming increasingly heavy, with the workload of teachers marking scripts accounting for about thirty percent of the total workload. At present, teachers use a combination of automatic marking of objective questions and manual marking of subjective questions, which is easy to achieve for objective questions such as incorrect or incorrect, but difficult to achieve for subjective questions, which are much more difficult than objective questions. There is a way to make the computer automatically evaluate subjective questions by using matching strings of score points on the Internet, but its accuracy is not accurate and there are large loopholes. If the students’ answers only give the strings of score points that need to be matched or give strings with the same semantics as the strings that need to be matched, this way will cause a large degree of misjudgment and misjudgment.In the actual marking process, there is a many-to-many relationship between students and questions, where a student can answer multiple questions and a question can be used for multiple students. Given the number of reference students in an examn, the number of questions in this exam is m, and the scores of student answers in this exam can be expressed as a two-dimensional matrix, as shown in the following equation:(1)Fmt=∑i=1mqiRm,∀i∈R,where Fm denotes the score of student n’s response to question m.The problem of intelligent marking of subjective questions can be abstracted as a matching problem between the standard answers based on the questions and the responses of each student in the marking system, with the following objective function defined:(2)Q=YYHn=∑i=1nyiyiHn,where yi is the score of the jth student’s answer to question i, Q denotes the standard answer of question i, Y denotes the answer given by the jth student to question i, F denotes the matching operation between the standard answer and the student’s answer, and ki denotes the full score coefficient of question i. When teachers evaluate subjective questions, they need to integrate syntax and other factors to give scores. Without considering the bias caused by human tendency, F(Q, Ai) in the formula can be understood as the compound of multiple functions, its matching process can be approximated by polynomial, and its mathematical expression is as follows:(3)FTx=∑i=1N1/λiN,where N denotes the total number of influencing factors, w1,w2,w3,…,wn correspond to the coefficient of each influencing factor, and Y(c, z) denotes the matching score under the nth influencing factor. A complete marking system needs not only to implement the marking function but also to count the students’ answers. The total score of students is the accumulation of the scores of each question in an examination, and its mathematical expression is as follows:(4)mYYHz≅1−c−z−zmYYHz−1,where zm is the total score of student j in an exam and m is the number of questions in an exam.In the process of analyzing test scores, the average student scores for each question need to be counted. By looking at the average scores for this question, teachers can help analyze the students’ mastery of the question with the following formula:(5)ft=12π1−t21/2,t∈−1,1.Z is the average score of all students on question i in a test, and n is the number of reference students in a test.


### 3.2. Platform Design and Implementation

In order to apply the research results of this study to the marking process of education, an online examination platform with B/S architecture is designed and developed. The online examination platform can realize the intelligent marking function of subjective questions, provide intelligent services for teachers, reduce teachers’ working time, and improve teachers’ teaching quality.Requirements analysis is an important part of the software development process. The content of the requirements analysis is to provide complete, clear, and specific requirements for the software system to be developed and determine what the software system must achieve. This section introduces part of the requirements analysis for implementing the intelligent marking function, in which the user roles involved in the intelligent marking function are marking teachers and students, and the use case diagram for students and marking teachers is shown in Figure2.Figure 2
Student and reviewer use case diagram.Introduction and tables that are not relevant to the research content of this study are not described here:(1)
Student Table. The table name of student table is Tb_Student, which mainly stores the information of student users. The fields in the student table mainly include student number (primary key), class, major, college, and school (foreign key), to which the student belongs and basic information of the student, and detailed information is shown in Figure 3.(2)
Teachers Table. The teachers table mainly stores the information of teachers. The fields in the teachers table mainly include the teacher number (primary key), the major, college, and school to which the teacher belongs (foreign key) and the basic information of the teacher, and the details are shown in Figure 4. For subjective questions, the computer should not simply match the strings but should focus on the semantic differences between the student’s answers and the standard answers. In order to semantically match students’ answers with standard answers, artificial intelligence and natural language processing techniques are introduced to model the intelligent marking function of subjective questions, and the similarity score is given by calculating the similarity of statements between students’ answers and standard answers, and then the maximum score is given to students.Figure 3
Student information summary.Figure 4
Teacher information summary.


### 3.3. Intelligent Review Function

In order to test the performance and effectiveness of the intelligent review function of subjective questions, this study designs intelligent review experiments for 20 pairs of texts to give their matching results and analyze their computational efficiency. The hyperparameters of the model training are set as follows: convolutional kernel sizew = 3, maximum training epoch = 50, word vector dimension d = 300, and learning rate lr = 0.065. The model with the highest evaluation index is selected for the intelligent review function after the training is completed. As shown in Figure 5, this study gives the algorithm time consumption data of the model calculating 1, 10, and 20 subjective questions, respectively, and its specific time consumption is divided into model start-up time, model preoperation time, text processing time, and model operation time, where the preoperation time is determined by the scale of the model.Figure 5
Time consumption data of subjective questions intelligent review.In order to more accurately describe the students’ responses, their scores were divided into intervals based on the size of 1f. When 1f is below 0.4, students’ responses are poor and have a large semantic bias; when 1f is between 0.4 and 0.6, students’ responses are average and have a small semantic bias; when 1f is between 0.6 and 0.8, students’ responses are good and have some semantic similarity; when 1f is between 0.8 and 1, students’ responses are excellent and have a high semantic similarity. According to the results of the author’s classroom survey, about 90% of the students think that English writing is important as a basic skill for English learning, but more than 85% of them do not know how to write and have more grammar problems; about 78% of the students do not like English writing very much; in the process of writing, students have various problems, such as not remembering words solidly and making more spelling mistakes; in writing, they use simple vocabulary expressions and do not have the “right” word.In the process of writing, students have a variety of problems, such as not remembering words well and making a lot of spelling mistakes, using simple vocabulary in writing and not using “flash” words, using more simple sentences in writing, using unreasonable articulation words, even without proper articulation, making more grammatical mistakes, not having enough content in writing, and having a shorter length. The current situation of teaching and learning of college English writing prompts teachers to actively think and change their teaching methods in order to improve the teaching effect of college English writing. In recent years, intelligent teaching platforms have been introduced into college English teaching, which has improved the modernization of English teaching and achieved certain results. In teaching practice, the use of intelligent teaching platforms has also played a positive role in promoting the teaching of college English writing. From the above analysis, it can be found that the impact of question size on the total evaluation time is very small and is suitable for large-scale questions. The impact of question size on the total review time is very small and is suitable for large-scale review.


## 3.1. Random Matrix Text Matching Features

In the evaluation process, the computer only needs to compare the strings of standard answers and students’ answers to give scores directly for objective questions, while for subjective questions, the computer needs to compare students’ answers with standard answers and give scores according to the scores of students’ answers. In order to solve the problem of intelligent assessment of subjective questions, a deep text matching model is introduced to determine the semantic similarity between students’ answers and standard answers and to give scores, and word separation and text vectorization operations are required before inputting the deep text matching model. The technical route of the intelligent review function is shown in Figure1.Figure 1
The technical route of the intelligent review function.In the field of education, the marking of scripts occupies an extremely important position in teachers’ work tasks, and as the number of educated students in society continues to rise, the workload of teachers marking scripts is becoming increasingly heavy, with the workload of teachers marking scripts accounting for about thirty percent of the total workload. At present, teachers use a combination of automatic marking of objective questions and manual marking of subjective questions, which is easy to achieve for objective questions such as incorrect or incorrect, but difficult to achieve for subjective questions, which are much more difficult than objective questions. There is a way to make the computer automatically evaluate subjective questions by using matching strings of score points on the Internet, but its accuracy is not accurate and there are large loopholes. If the students’ answers only give the strings of score points that need to be matched or give strings with the same semantics as the strings that need to be matched, this way will cause a large degree of misjudgment and misjudgment.In the actual marking process, there is a many-to-many relationship between students and questions, where a student can answer multiple questions and a question can be used for multiple students. Given the number of reference students in an examn, the number of questions in this exam is m, and the scores of student answers in this exam can be expressed as a two-dimensional matrix, as shown in the following equation:(1)Fmt=∑i=1mqiRm,∀i∈R,where Fm denotes the score of student n’s response to question m.The problem of intelligent marking of subjective questions can be abstracted as a matching problem between the standard answers based on the questions and the responses of each student in the marking system, with the following objective function defined:(2)Q=YYHn=∑i=1nyiyiHn,where yi is the score of the jth student’s answer to question i, Q denotes the standard answer of question i, Y denotes the answer given by the jth student to question i, F denotes the matching operation between the standard answer and the student’s answer, and ki denotes the full score coefficient of question i. When teachers evaluate subjective questions, they need to integrate syntax and other factors to give scores. Without considering the bias caused by human tendency, F(Q, Ai) in the formula can be understood as the compound of multiple functions, its matching process can be approximated by polynomial, and its mathematical expression is as follows:(3)FTx=∑i=1N1/λiN,where N denotes the total number of influencing factors, w1,w2,w3,…,wn correspond to the coefficient of each influencing factor, and Y(c, z) denotes the matching score under the nth influencing factor. A complete marking system needs not only to implement the marking function but also to count the students’ answers. The total score of students is the accumulation of the scores of each question in an examination, and its mathematical expression is as follows:(4)mYYHz≅1−c−z−zmYYHz−1,where zm is the total score of student j in an exam and m is the number of questions in an exam.In the process of analyzing test scores, the average student scores for each question need to be counted. By looking at the average scores for this question, teachers can help analyze the students’ mastery of the question with the following formula:(5)ft=12π1−t21/2,t∈−1,1.Z is the average score of all students on question i in a test, and n is the number of reference students in a test.


## 3.2. Platform Design and Implementation

In order to apply the research results of this study to the marking process of education, an online examination platform with B/S architecture is designed and developed. The online examination platform can realize the intelligent marking function of subjective questions, provide intelligent services for teachers, reduce teachers’ working time, and improve teachers’ teaching quality.Requirements analysis is an important part of the software development process. The content of the requirements analysis is to provide complete, clear, and specific requirements for the software system to be developed and determine what the software system must achieve. This section introduces part of the requirements analysis for implementing the intelligent marking function, in which the user roles involved in the intelligent marking function are marking teachers and students, and the use case diagram for students and marking teachers is shown in Figure2.Figure 2
Student and reviewer use case diagram.Introduction and tables that are not relevant to the research content of this study are not described here:(1)
Student Table. The table name of student table is Tb_Student, which mainly stores the information of student users. The fields in the student table mainly include student number (primary key), class, major, college, and school (foreign key), to which the student belongs and basic information of the student, and detailed information is shown in Figure 3.(2)
Teachers Table. The teachers table mainly stores the information of teachers. The fields in the teachers table mainly include the teacher number (primary key), the major, college, and school to which the teacher belongs (foreign key) and the basic information of the teacher, and the details are shown in Figure 4. For subjective questions, the computer should not simply match the strings but should focus on the semantic differences between the student’s answers and the standard answers. In order to semantically match students’ answers with standard answers, artificial intelligence and natural language processing techniques are introduced to model the intelligent marking function of subjective questions, and the similarity score is given by calculating the similarity of statements between students’ answers and standard answers, and then the maximum score is given to students.Figure 3
Student information summary.Figure 4
Teacher information summary.


## 3.3. Intelligent Review Function

In order to test the performance and effectiveness of the intelligent review function of subjective questions, this study designs intelligent review experiments for 20 pairs of texts to give their matching results and analyze their computational efficiency. The hyperparameters of the model training are set as follows: convolutional kernel sizew = 3, maximum training epoch = 50, word vector dimension d = 300, and learning rate lr = 0.065. The model with the highest evaluation index is selected for the intelligent review function after the training is completed. As shown in Figure 5, this study gives the algorithm time consumption data of the model calculating 1, 10, and 20 subjective questions, respectively, and its specific time consumption is divided into model start-up time, model preoperation time, text processing time, and model operation time, where the preoperation time is determined by the scale of the model.Figure 5
Time consumption data of subjective questions intelligent review.In order to more accurately describe the students’ responses, their scores were divided into intervals based on the size of 1f. When 1f is below 0.4, students’ responses are poor and have a large semantic bias; when 1f is between 0.4 and 0.6, students’ responses are average and have a small semantic bias; when 1f is between 0.6 and 0.8, students’ responses are good and have some semantic similarity; when 1f is between 0.8 and 1, students’ responses are excellent and have a high semantic similarity. According to the results of the author’s classroom survey, about 90% of the students think that English writing is important as a basic skill for English learning, but more than 85% of them do not know how to write and have more grammar problems; about 78% of the students do not like English writing very much; in the process of writing, students have various problems, such as not remembering words solidly and making more spelling mistakes; in writing, they use simple vocabulary expressions and do not have the “right” word.In the process of writing, students have a variety of problems, such as not remembering words well and making a lot of spelling mistakes, using simple vocabulary in writing and not using “flash” words, using more simple sentences in writing, using unreasonable articulation words, even without proper articulation, making more grammatical mistakes, not having enough content in writing, and having a shorter length. The current situation of teaching and learning of college English writing prompts teachers to actively think and change their teaching methods in order to improve the teaching effect of college English writing. In recent years, intelligent teaching platforms have been introduced into college English teaching, which has improved the modernization of English teaching and achieved certain results. In teaching practice, the use of intelligent teaching platforms has also played a positive role in promoting the teaching of college English writing. From the above analysis, it can be found that the impact of question size on the total evaluation time is very small and is suitable for large-scale questions. The impact of question size on the total review time is very small and is suitable for large-scale review.


## 4. Numerical Experiments and Results Analysis

In this study, we conduct experiments on the platform and build the model using Python 3.5.2 and Tensorflow 13.1.1 framework. To verify the performance of the RNP-MP model, experiments are conducted for the PI problem in text matching research. The dataset required for the experiments is the MSRP (Microsoft Research Paraphrase) dataset provided by Microsoft, which is a recognized authoritative dataset. The MatchPyramid model and RNP-MP model are compared under the same dataset, and the maximum number of model iterations is set to 20 under the premise that the training of the model can converge to the optimum, as shown in Figure6. The MSRP dataset is a classical public corpus for text interpretation task, which contains 5801 pairs of texts, each pair contains a label, and a label of 1 means the two texts match successfully, while a label of 0 means the two texts do not match. About 4076 pairs of texts are used for training in the MSRP dataset. The MSRP dataset contains 4076 pairs of texts, including 2753 pairs of successful matches and 1323 pairs of mismatches. The test set contains 1725 pairs of texts, including 1147 pairs of successful matches and 578 pairs of mismatches.Figure 6
Relevant data information of the dataset.The accuracy of matching (accuracy) and F1 are commonly used to measure the performance of a model on the MSRP dataset, where the F1 score is often used as a measure of the accuracy (precision,P) of a binary model, the meaning of which is expressed as a weighted average of P and recall (R). The mathematical expressions for P, R, and the F1 score are as follows:(6)P=TPTP+FP,R=TPTP+FN,F1=2PRP+R.In the formula, TP in the MSRP dataset means the number of times the two texts are predicted to match successfully and in fact the two texts are matched, FP in the MSRP dataset means the number of times the two texts are predicted to match successfully but in fact the two texts do not match, and FN in the MSRP dataset means the number of times the two texts are predicted to match unsuccessfully but in fact the two texts can match successfully. The F1 score of the model is obtained by substituting the values ofP and R. The main functions that students participate in the marking session are taking exams, submitting answers to questions, and viewing exam results, etc. The main functions that teachers participate in the marking session are adding questions, adding papers, adding exams, and marking results.The performance of the two models with different sizes of convolution kernels is compared. The experimental results on the training set show that both MatchPyramid model and RNP-MP model achieve more than 90% matching accuracy after training, which indicates that both models can match the trained data with high accuracy after training, and the best matching effect is achieved at the convolutional kernel size w3. Comparing the MatchPyramid model and RNP-MP model on the training set for each set of convolutional kernel size, we can see that the matching accuracy of RNP-MP model on the training set is better than that of the original MatchPyramid model, indicating that the RNP-MP model is better than the MatchPyramid model in learning to match between texts with the same data set. This indicates that the RNP-MP model is better than the MatchPyramid model in learning matches between texts with the same dataset. Comparing the MatchPyramid model with the RNP-MP model on the test set for each set of convolutional kernel sizes, we can see that the matching accuracy of the RNP-MP model on the test set is lower than that of the MatchPyramid model, which indicates that the RNP-MP model has the good matching ability for trained datasets but the poor matching ability for unknown, untrained datasets.The MSRP dataset is small and suitable for text matching tasks with simple models, while complex models are prone to overfitting. The word sequences S1 and S2 are transformed into vectors one by one according to the order of arrangement and stitched together to obtain the word vector matrix, and the distance between the two vectors is calculated to obtain the matching matrix between the text T1 and T2 under the word granularity, as shown in Figure7. The darker color in the figure indicates the higher similarity between the word vectors, and the lighter color indicates the lower similarity between the word vectors. Assuming a convolutional sliding window size of 3, the vector expressions of T1 and T2 phrase sequences of the text can be obtained, and the higher-granularity text expressions can be obtained similarly. The matching ability is poor. According to the above analysis, the RNP-MP model has the better learning ability for the text matching task, but the model structure is more complex and prone to overfitting problems when the dataset is small. Therefore, the RNP-MP model is more suitable for large-scale text matching datasets, while MatchPyramid is suitable for small-scale text matching datasets.Figure 7
Interaction matrix between texts T1 and T2 at word granularity.To verify the performance of the MG-CMF model, experiments and analyses are conducted for the PI task as well as the AS task in the text matching study. In order to verify the effect of multiple granularities of the model on the algorithm, four granularities are chosen for the experiments, namely, word granularity (WG), short phrase granularity (SPG), long phrase granularity (LPG), and sentence granularity (SG). According to the granularity levels used in the MG-CMF model, the MG-CMF model using SPG is MG-CMF@1, the MG-CMF model using LPG is MG-CMF@2, the MG-CMF model using SG is MG-CMF@3, the MG-CMF model using both WG and SPG is MG-CMF@4, and the MG-CMF model using both WG and SPG is MG-CMF@4. The MG-CMF model with three levels of granularity, WG, SPG, and LPG, is MG-CMF@5; the MG-CMF model with four levels of granularity, WG, SPG, LPG, and SG, is MG-CMF@6. The MG-CMF model will be experimented with three sets of convolution kernels of different sizes,w = 2, 3, and 4, respectively. Different granularity levels are used for different sizes of convolution kernels. The number of experimental iterations is 20 for each group, and the curve changes of the experimental evaluation metrics are recorded during the 20 iterations, with different evaluation metrics for different data sets. The experimental data of MatchPyramid model and RNP-MP model are shown in Figure 8, and the experimental results of MatcgPyramid model and RNP-MP model on MSRP training set and MSRP test set are recorded respectively.Figure 8
Experimental results data on the MSRP dataset.The evaluation metrics to measure the AS task are MAP (mean average precision) and MRR (mean reciprocal rank). MAP is a single-value metric that reflects the performance of the system on all relevant documents, and the higher the relevant documents retrieved by the system, the higher the MAP is likely to be, while the accuracy rate defaults to 0 if the system does not return relevant documents. The first result matching score of the evaluation search algorithm is 1, the second matching score is 0.5, and the nth matching score is 1 / n. If there is no matching sentence with a score of 0, the final score is the sum of all the scores, as shown in Figure9.Figure 9
Experimental results at the convolution kernel size.The matching ability of the six MG-CMF models was analyzed, and the first three variations belonged to the single-granularity level, while the last three variations belonged to the multigranularity level. From the analysis of the above experimental results and the change curve of evaluation indexes, we can get that the MG-CMF model with multiple granularity levels has significantly improved the matching accuracy of two text segments, and the matching ability of the RNP-MP model is not as good as that of the MG-CMF model, but it has superior stability for the text matching task. It can be concluded that the performance on the WikiQA dataset for the AS task is ranked as follows: MG-CMF > RNP-MP > MatchPyramid model. Unlike traditional English writing, the iWrite English writing and marking system is an online platform, and both students’ English writing and teachers’ marking are done online. The first advantage of this model is that it is instantaneous, as teachers can assign writing tasks online and students can complete them online. Students can submit their writing assignments to the teacher for review immediately, reducing the traditional offline workload of sending and receiving assignments. In addition, all writing and marking can be done in class. With the popularity of smart devices and the Internet, students can do English writing training anytime and anywhere through smart devices and the Internet. This online operation and training are very different from traditional teaching, and as the system becomes fully popular, other teaching contents can be learned by students through the online platform.


## 5. Conclusion

English is a basic course for college students, and learning English well can help students in their future work and career development. In the actual teaching process, we find that students are not interested in learning English, especially in the learning and training of English writing, and they lack energy and interest. English writing is a comprehensive reflection of students’ English application ability, and it takes up a large part in the daily study and English level exams in universities. In the process of applying iWrite English writing and grading system, teachers should constantly find problems and give feedback to the platform management, so as to promote the platform to continuously optimize the system settings and introduce new services and functions that meet students’ learning characteristics. At the same time, teachers should also actively adjust their teaching ideas and teaching methods to adapt to the increasingly popular and improved information-based teaching methods and continuously improve their teaching level and teaching effectiveness. iWrite English Writing and Reviewing System is the result of long-term research by the industry research team, and its widespread use in colleges and universities is of great significance to English writing teaching. The change curve of evaluation indexes shows that there is no significant difference between the matching ability of MG-CMF model at single granularity level and MatchPyramid model, and the matching ability of MG-CMF model at multiple granularity levels is significantly better than that of single granularity level. As we can see from the teaching practice of English writing, simply training and teaching writing through iWrite English Writing and Reviewing System can also cause students to burn out and be in a negative writing state. Therefore, in the actual teaching process, teachers should not rely too much on the system platform but should realize the combination of traditional writing training and online writing training in an orderly way. By constantly changing learning methods, students can always keep their interest in writing training. In addition, English writing is done on paper, whether it is a school test or another level test. Long-term training on the online platform is also not conducive to students’ access to the test.



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*Source: 6402418-2022-08-29.xml*