# The College English Teacher Behavior and Teaching Effect Based on Big Data Technology

Authors: Jiang Ling; Liu Jianhua; Wang Haijie; Zhang Li
Journal: Mobile Information Systems
 (2022)

---

## Abstract

In order to explore the correlation between college English teacher behavior and teaching effects, this paper applies data mining technology to related research. Moreover, this paper uses Bloom Filter to represent the efficiency of insert queries and space usage of dynamic data sets and designs a multichannel balanced matrix Bloom Filter. In addition, this paper uses the longest prefix matching filling algorithm when selecting the element insertion position in multiple candidate Bloom Filters, and uses this algorithm to process the college teacher behavior and teaching effect data. Finally, this paper combines investigation and experiment to conduct research. Through the investigation, it can be seen that the teaching behavior of college English teachers will have a certain impact on students, and the positive behavior of teachers can effectively improve the teaching effect, which can be used as a reference for the formulation of subsequent teaching plans.


---

## Body

## 1. Introduction

With the continuous deepening of the reform of the new curriculum standard, the reform of China’s college English curriculum education and teaching has become more and more important. The new educational philosophy requires “health as the core.” As one of the school’s basic education courses, the English and health curriculum focuses on the multidirectional development of students’ English ability. Through the comprehensive teaching of sports knowledge and skills, the realization of the various goals of English and health courses can help students better master English skills and improve English learning, which can further promote the deepening reform of college English education and teaching. Under a new type of talent training model aimed at improving students’ ideological and moral character, scientific and cultural knowledge, and comprehensive development of physical and mental health, the new curriculum standard puts forward higher and higher requirements and goals for the personal comprehensive ability of English teachers [1]. English teachers are the implementers of English curriculum standards, as well as the guides of classroom teaching, and their comprehensive ability level is mainly manifested through the teachers’ own leadership behavior [2]. Teacher leadership behavior is a research branch of behavioral science and has a certain behavior pattern. Moreover, it is an external behavior manifested in the psychological process of teachers’ physical and mental quality, and a meaningful leadership activity that governs the internal spiritual motivation of teachers [3].In the implementation of the new curriculum, teaching behavior research has become one of the effective methods of classroom teaching research, and it has received widespread attention and application in the education field. The development of teacher professionalization has become the trend of international teacher education reform, and it has been valued by many countries. It is also a subject of great theoretical significance put forward by the current education reform practice. Compared with other industries, the turnover rate of teachers in this industry is relatively low. Many teachers have been engaged in education for life, serving as teachers for 30–40 years, which is relatively rare in other industries. In the case of a long-term education environment, teachers need to constantly adjust their educational concepts and values, enrich professional knowledge and professional skills, and meet their own needs at different levels, so as to show teacher role behaviors that are table for a specific stage of professional development.Previous studies have suggested that teachers’ teaching concepts will affect teaching behavior, but there has been no thorough and detailed research on how to influence them. This research explores the relationship between the concepts of teaching, teacher, student, and learning concepts and behaviors in teaching design, teaching environment, classroom teaching, and teaching reflection by refining teaching concepts and behaviors, and derives different concepts The degree of influence on different aspects of teaching behavior is different, so as to provide theoretical support for the update of teaching concepts and the transformation to teaching behaviors, enrich and improve the research on the relationship between teaching concepts and teaching behaviors, and enrich the relevant theoretical basis for teachers’ professional growth. The renewal of teachers’ teaching concepts and the transformation of teaching behaviors are part of the research content in the field of teacher professional growth. The ultimate goal of the renewal of teachers’ teaching concepts and efficient transformation of teaching behaviors is to help teachers’ professional growth. Therefore, this research on the status quo and the relationship between teaching concepts and teaching behaviors can enrich the connotation of teachers’ professional growth and supplement the theoretical foundation of teachers’ professional growth.This article combines big data technology to study the behavior and teaching effect of college English teachers, conducts research based on actual cases, and builds an intelligent model through big data technology to provide theoretical references for subsequent teaching reform research.


## 2. Related Work

Regarding the relationship between teaching philosophy and behavior, early foreign scholars also agreed that teaching philosophy and teaching behavior are consistent. The literature [4] proposed that teachers’ ideas determine their teaching behavior to students. The literature [5] pointed out that the ideas held by teachers profoundly affect their behaviors, and based on their ideas, we can more accurately predict the behaviors they will take. Literature [6] believes through research that “concepts are the tendency of action and the decision of behavior,” and concepts guide behavior. Concepts are the internal basis of behaviors, behaviors are the external manifestations of concepts, and teachers’ teaching concepts strongly influence their teaching behaviors. However, with the deepening of research, many scholars have proposed that the relationship between teacher’s teaching philosophy and teaching behavior is not a simple one-to-the-other relationship. The two are related to each other and influence each other. There are both consistency and difference. The literature [7] used a self-compiled questionnaire to investigate the low-level teachers in elementary school, and tried to explore the relationship between their ideas and behaviors. Its research shows that teachers with developmental awareness and new teaching concepts tend to be student-centered, and tend to have a free and open teaching concept. However, teachers, who do not have developmental awareness and stick to the old teaching concepts, are mostly self-centered, and their teaching concepts also remain conservative and closed.The literature [8] found that the influence of teacher’s concept and teaching behavior is interactive: teacher’s concept affects their teaching behavior, and the teacher’s day-to-day teaching behavior affects the change of teacher’s teaching concept to a certain extent. Literature [9] used questionnaires, interviews, and video analysis to study the teaching concepts and teaching behaviors of inquiry teaching on 8 new science teachers who had received inquiry teaching training. It was found that even if new teachers believed in student-centered teaching concepts, they could not be implemented in practice, there was a gap between the concepts and behaviors of new teachers. These studies have revealed the complexity of the relationship between teachers’ individual teaching concepts and teaching behaviors.Literature [10] developed a “checklist for important factors of teacher’s character” after a lot of research, which provides specific tools for frontline teachers to improve their teaching behavior. Literature [11] believed that teachers should have the same professional knowledge as doctors and lawyers, and first proposed the concept of pedagogical content knowledge (PCK), which is an important condition for teachers to practice teaching.The interaction between teachers and students is a necessary condition for teaching and learning. Scholars have studied the interaction between teachers and students from different angles, and the research focuses on the impact of the interaction between teachers and students on student behavior. Literature [12] studied the influence of different leadership styles on personal behavior and obtained the relationship between authoritative, leadership, and laissez-faire teacher leadership styles and student behavior. Literature [13] started from the perspective of the status of teachers, students, and knowledge, and studied the behavior of teachers in the classroom. They divide teacher-student interaction into three ways: teacher-centered, student-centered, and knowledge-centered. The central teacher-student relationship is a relationship of control and obedience, the student-centered teacher-student relationship is a dual subject, and the knowledge-centered teacher-student relationship is a partnership with a common goal.


## 3. Teaching Data Mining Algorithm

The basic principle of Bloom Filter is to represent the element setS=x1,x2,⋯,xn through a bit vector of length m. Among them, the set S contains n elements, and the initial state of each bit in the vector is 0. For each element in the set, k hash functions are used to map it to the vector, and the corresponding position of the hash value is set to 1, and the other bits remain О unchanged. When we are asked to query whether an element belongs to a certain set, we use k hash functions to calculate the element and check whether the corresponding position of each hash value in the vector is 1. As long as the position of a certain hash value in the vector is 0, then it can be determined that the element does not belong to the set, otherwise the element is considered to belong to the set with a certain misjudgment rate. The algorithm structure of Bloom Filter is shown in Figure 1, [14].Figure 1
Bloom filter algorithm structure.Then, the probability that the positions of thek hash function mappings of the element are all 0 is given as follows:(1)1−1mk.The probability that the positions ofk hash function mappings of n elements in the set are all 0 is given as follows:(2)1−1mkn.Then the probability that a certain hash function of an element is mapped to 1 is given as follows:(3)1−1−1mkn.Due to its simplicity and efficiency, Bloom Filter has recently received widespread attention in the network and storage fields. When using Bloom Filter, performance parameters such as misjudgment rate, memory size, and the number of sets to be managed are mainly considered [15].Therefore, the false positive rate of Bloom Filter is given as follows:(4)p=1−1−1mknk≈1−ekn/mk.Although Bloom Filter is an efficient retrieval data structure, it still cannot solve problems such as the dynamic growth and change of collection elements. With the increase of elements in the set, the false positive rate of a single Bloom Filter will increase, which will eventually lead to the failure of the Bloom Filter. In recent years, some substantial improvements have been made to the Bloom Filter for the problem of dynamic collections, resulting in the split Bloom Filter algorithm and the dynamic Bloom Filter algorithm as well as improved algorithms based on these two algorithms.The basic idea of split the Bloom Filter is to user bit vectors to represent the data set. When the number of elements in the set increases to a certain extent and affects the initial design of the misjudgment rate index, a r × m two-dimensional bit vector is regenerated. If the added element does not reach the preset maximum number of sets, a bit vector is randomly selected to represent the element. If the mapped hash function is evenly distributed, the number of elements that can be inserted by each Bloom Filter in the split Bloom Filter is the same. That is, the number of elements that each Bloom Filter can hold is n/r. The structure of the split Bloom Filter is shown in Figure 2.Figure 2
Split bloom filter algorithm structure.For split Bloom Filter, the probability that one of the Bloom Filters will be misjudged is given as follows [16]:(5)pBF′=1−1−1mknk≈1−ekn/mk.Then, the probability that Bloom Filter does not make a misjudgment is given as follows:(6)pBF=1−1−1−1mknk≈1−1−ekn/mk.The probability thatr Bloom Filters does not make a misjudgment is given as follows:(7)pSBF′=1−1−1−1mknk≈1−1−ekn/mk.Then, the probability that split Bloom Filter makes a misjudgment is given as follows:(8)pSBF′=1−1−1−1−1mknkr≈1−1−1−ekn/mkr.Dynamic Bloom Filter dynamically increases Bloom Filter according to the growth of elements. The number of elements that the predesigned Bloom Filter can store isn, the number of initialized Bloom Filters is 1, and the current Bloom Filter that can be inserted is the active Bloom Filter. When we need to insert an element, we first query whether the element exists in the collection. If it does not exist, we determine whether the currently active Bloom Filter is full. If it is not full, it will be inserted into the currently active Bloom Filter. If it is full, it will dynamically grow a Bloom Filter and insert the element. It should be noted that only the last Bloom Filter of the dynamic Bloom Filter is the currently active Bloom Filter, and the r − 1 Bloom Filters on it are the inactive Bloom Filters, which only allow query operations and cannot insert elements. The structure of the dynamic Bloom Filter is shown in Figure 3. It can be seen from the structure diagram of the dynamic Bloom Filter that when the mapping hash function distribution is sufficiently uniform, the number of elements that can be accommodated by the previous r − 1 inactive Bloom Filters is the same, which are all n, as long as the elements stored in the currently active Bloom Filter are less than n0.Figure 3
Dynamic bloom filter algorithm structure.When the dynamic Bloom Filter looks for an inactive Bloom Filter, the probability of no misjudgment is given as follows:(9)pr−1=1−1−1−1mkn0kr−1≈1−1−ekn0/mkr−1.When searching for the currently active Bloom Filter, the probability of no misjudgment is given as follows:(10)pr−1=1−1−1−1mkn0k≈1−1−ekn0/mk.The probability of no misjudgment of the entire dynamic Bloom Filter is given as follows:(11)pDBF′=1−1−ekn0/mkr−11−1−ekn−n0r−1/mk.Then, the probability of misjudgment of dynamic Bloom Filter is given as follows:(12)pDBF=1−1−1−ekn0/mkr−11−1−ekn−n0r−1/mk.Although splitting Bloom Filter and dynamic Bloom Filter solves the problem of dynamic growth of elements, they do not effectively solve the problem of space utilization of a single Bloom Filter. At the same time, these algorithms have brought new problems. In terms of element query performance, they run counter to the constant time complexity of Bloom Filter. The time complexity of the standard Bloom Filter isO (k). However, split Bloom Filter and dynamic Bloom Filter need to search for each Bloom Filter sequentially when inserting and querying data, so its time complexity is related to the number of Bloom Filters, namely, O (kr), where r is the number of Bloom Filters. When the number of Bloom Filters is large, the query performance is poor. Matrix Bloom Filter is improved based on the query performance of split Bloom Filter. It quickly locates a Bloom Filter through a special hash function, and searches or inserts elements in this Bloom Filter. This algorithm no longer uses the inserted element count as the full condition of the Bloom Filter, but introduces the concept of saturation, and uses the saturation as the full condition of the Bloom Filter. In addition, it dynamically grows the Bloom Filter by means of a vector group, and selects the Bloom Filter that uses the least space in the vector group as the element insertion position to achieve the effect of inserting more elements. Although 2BDBF can represent more elements, its query performance has not been improved [17].The false positive rate of the Bloom Filter isp=1−1−1/mknk≈1−ekn/mk, where m is the bit length of Bloom Filter, n is the number of inserted elements, and k is the number of hash functions.Theorem 1.
The false positive rate function of the Bloom Filter is a monotonically increasing function.Proof.
Under the premise that the number of hash functionsk and the vector length m of Bloom Filter are determined, in order to find the minimum misjudgment rate of Bloom Filter, p is derived from n:(13)dpdn=k1−ekn/mk⋅−ekn/m⋅−mn.
Since the exponential functionex increases monotonically at x≥0, and kn/m>0, so ekn/m>e0=1and 0<ekn/m<1. At this point, we can get 1−ekn/m>0. Since k > 0, dp/dn>0. From the abovementioned derivation, it can be concluded that the false positive rate of Bloom Filter is a monotonically increasing function. Therefore, when dp/dn=0, the misjudgment rate is the smallest.
The false positive rate is kept to a minimum, and the Bloom Filter bit space usage rate when the number of inserted elements reaches the maximum is 50% [18].Proof.
According to Theorem1, when the misjudgment rate is the smallest, the maximum number n of set elements that can be represented in the vector satisfies n = In2. If it is assumed that the hash function obeys a uniform distribution, when inserting an element, the probability that a certain hash function of Bloom Filter maps to a certain bit is 1. Then, when n elements are inserted, the probability of any bit being 0 is given as follows:(14)p′=−1mkn.
When formulan=k/mln2 is inserted into formula (14), we can get(15)p′=1−1mk+m/k+ln2≈e−1/m+mln2=12.
It can be seen that the probability that any bit in the Bloom Filter isО is, at this time, it can be considered that the number of bits 1 accounts for 50% of the total length of the vector. That is, when the maximum number of inserted elements is reached, the bit space usage rate of the Bloom Filter is 50%.
As mentioned above, the work of this paper mainly uses Bloom Filter to express the efficiency of insert queries and space usage of dynamic data sets. This text has designed a kind of multichannel balanced matrix Bloom Filter (M-BMBF), its algorithm design structure is shown in Figure4.
In order to solve the problem of the efficiency of inserting and querying dynamic data sets, M-BMBF introduces multiple positioning hash function concepts. The basic design idea of M-BMBF is to express the dynamic set as a bit matrix ofr × m. The behavior r of the matrix indicates that there are r Bloom Filters, and the column of the matrix is m, which indicates that the vector length of each Bloom Filter is m bits. Here, the line r is a constant, which must be predetermined according to the maximum estimate of the size of the collection. In order to improve the search performance of elements, this paper designs s location hash functions h0,…, hs − 1. These s hash functions are specially defined and different from other k-independent hash functions. In order to reduce the probability of collision of s hash functions, r Bloom Filters are divided into s groups, where each group has r/s Bloom Filters, and each positioning hash function maps a set of Bloom Filters. Before adding an element, you need to determine which Bloom Filter the element should be added to. At this time, we can use these s hash functions to perform positioning calculations, and select the Bloom Filter corresponding to the element in each group as the candidate insertion position [19].
We can convert the maximum number of inserted elements of the Bloom Filter under the lowest misjudgment rate into its bit space usage. Based on this, we use the longest prefix matching filling algorithm to improve the space utilization efficiency of Bloom Filter. The main design idea of the longest prefix matching filling algorithm is to map the hash function value of the element to the position of 1 in the Bloom Filter vector as much as possible when inserting the element. Then, it is possible to insert more elements thann, while keeping p unchanged.
Specifically, when inserting, we useé hash functions to calculate the k hash function values of the element x, and compare them with the corresponding address positions of the candidate Bloom Filter. If it is found that the addresses of the k hash functions corresponding to one of the Bloom Filters are all 1, it is considered that the element already exists and does not need to be inserted. Otherwise, the position with the corresponding address value of 1 in the s candidate Bloom Filters that overlaps the k hash addresses of x the most is selected as the insertion position. In M-BMBF, the hash range of each location hash function is 0,1,2,....r/s, and the range of other k hash functions is 0,1,...,m−1. Therefore, before the construction of M-BMBF, the number of hash functions is not the number of days actually used in the algorithm. In fact, k + s hash functions will be used. When we assume that the location hash function is perfectly random, the number of elements that can be contained in each group should be the same, which is n/r.Figure 4
M-BMBF algorithm structure.


### 3.1. Time Complexity

When we want to query whether an element belongs to a certain set, split Bloom Filter and 2BDBF are to query each Bloom Filter in order until it is found. The query time for each Bloom Filter isO (k), where k is the number of hash functions, then the worst-case average query time for splitting Bloom Filter and 2BDBF is O (kr). Its time complexity is O (s). Then, this paper performs the mapping of k hash functions in these s Bloom Filters, and the time complexity is O (k). Therefore, the total query time complexity of the algorithm in this paper is O (ks). Since s is generally much smaller than r, the algorithm constructed in this paper can effectively solve the problem of query efficiency [20].


### 3.2. Analysis of Misjudgment Rate

As far as Bloom Filter is concerned, when querying an element, if the element does not belong to a dynamic collection but returns true during the query process, a misjudgment will occur. From the analysis of the misjudgment rate of split Bloom Filter, it can be seen that the misjudgment rate of split Bloom Filter is given as follows:(16)F=1−1−1−ekn/mkr.For the algorithm proposed in this paper, when0≤d≤s−1, if we assume that the probability of misjudgment of the Bloom Filter corresponding to the d-th location hash function is fd, then the probability of no misjudgment in all location rows of the algorithm is 1−fd. Therefore, the probability of misjudgment of at least one Bloom Filter is 1−1−fd. The misjudgment rate of M-BMBF can be obtained as follows:(17)FM−BMBF=1−1−1−ekn/mrks.Compared with the split Bloom Filter, the misjudgment rate of M-BMBF is related to the number of positioning hash functionss. In the case of the same inserted elements, because of s≤r, FM−BMBF≤F.


### 3.3. Space Performance

Bloom Filter itself is a data structure with efficient spatial performance. It uses bit arrays to express a collection very concisely. Compared with other data structures related to data sizes, such as B-trees and hash tables, the storage space is very small, so it can be resident in memory. This paper uses the longest prefix matching filling algorithm when selecting the element insertion position in multiple candidate Bloom Filters. Before the bit space usage rate of Bloom Filter reaches 50%, more elements can be inserted to save the storage space of Bloom Filter. At the same time, from the analysis of formulas (16) and (17), it can be seen that when the variable parameters m, k and r are the same, if the misjudgment rate is also controlled to be equal, because of s≤r, M-BMBF inserts more elements than split Bloom Filter, which can save space.


## 3.1. Time Complexity

When we want to query whether an element belongs to a certain set, split Bloom Filter and 2BDBF are to query each Bloom Filter in order until it is found. The query time for each Bloom Filter isO (k), where k is the number of hash functions, then the worst-case average query time for splitting Bloom Filter and 2BDBF is O (kr). Its time complexity is O (s). Then, this paper performs the mapping of k hash functions in these s Bloom Filters, and the time complexity is O (k). Therefore, the total query time complexity of the algorithm in this paper is O (ks). Since s is generally much smaller than r, the algorithm constructed in this paper can effectively solve the problem of query efficiency [20].


## 3.2. Analysis of Misjudgment Rate

As far as Bloom Filter is concerned, when querying an element, if the element does not belong to a dynamic collection but returns true during the query process, a misjudgment will occur. From the analysis of the misjudgment rate of split Bloom Filter, it can be seen that the misjudgment rate of split Bloom Filter is given as follows:(16)F=1−1−1−ekn/mkr.For the algorithm proposed in this paper, when0≤d≤s−1, if we assume that the probability of misjudgment of the Bloom Filter corresponding to the d-th location hash function is fd, then the probability of no misjudgment in all location rows of the algorithm is 1−fd. Therefore, the probability of misjudgment of at least one Bloom Filter is 1−1−fd. The misjudgment rate of M-BMBF can be obtained as follows:(17)FM−BMBF=1−1−1−ekn/mrks.Compared with the split Bloom Filter, the misjudgment rate of M-BMBF is related to the number of positioning hash functionss. In the case of the same inserted elements, because of s≤r, FM−BMBF≤F.


## 3.3. Space Performance

Bloom Filter itself is a data structure with efficient spatial performance. It uses bit arrays to express a collection very concisely. Compared with other data structures related to data sizes, such as B-trees and hash tables, the storage space is very small, so it can be resident in memory. This paper uses the longest prefix matching filling algorithm when selecting the element insertion position in multiple candidate Bloom Filters. Before the bit space usage rate of Bloom Filter reaches 50%, more elements can be inserted to save the storage space of Bloom Filter. At the same time, from the analysis of formulas (16) and (17), it can be seen that when the variable parameters m, k and r are the same, if the misjudgment rate is also controlled to be equal, because of s≤r, M-BMBF inserts more elements than split Bloom Filter, which can save space.


## 4. Research on College English Teacher Behavior and Teaching Effect Based on Big Data Technology

Teacher’s teaching behavior refers to all the activities and performance of teachers in order to complete teaching tasks, achieve teaching goals, and promote students’ learning in classroom teaching. This behavior includes both implicit reactions (such as feelings and emotions) and explicit reactions, but both can be observed and recorded, and both can be perceived and understood by students. Students learn mainly under the guidance of teachers’ teaching behavior, which is a direct influence factor on teaching quality.The research objects are mainly English teachers in ordinary colleges and universities. The data of this research are representative.This study made two predictions before the formal investigation. The first prediction was carried out in March 2020. This article mainly uses random sampling methods to consciously collect and summarize the relevant questions reflected by the participants in the process of answering the questionnaire. After the investigation, the data obtained will be entered and analyzed using SPSS 21.0 for Windows. After that, the research team summarizes the problems that the subjects reported during the survey process, such as an excessive number of questions, duplication of some items, or lack of utility. Moreover, this paper invites expert teachers with many years of rich educational research experience to provide guidance to discuss the necessity of the questions in the survey and the rest of the questionnaire, and the rationality of the textual expression. After many meetings and discussions, this paper combines the results of data analysis to finally, delete some items and streamline the questionnaire.The second prediction was conducted in June 2021. A total of 60 English teachers in colleges and universities are surveyed, 0 questionnaires are distributed, 60 questionnaires are returned, and the recovery rate is 100%.This experiment mainly counts teachers’ classroom behavior and teaching effect. Because there are many teacher behaviors, this paper analyzes the correlation between teacher observation behavior, time control behavior, entertainment teaching behavior, summary behavior, and teaching effect. Moreover, this paper combines big data technology to carry out data processing, and counts the test results, as shown in Table1–Table 4 and Figure 5–Figure 8.Table 1
Evaluation results of college English teacher’s observation behavior.
NumberScores169.28274.33372.74478.44577.48686.58773.80883.01980.801076.131169.371269.611380.091483.311577.121672.631779.881874.451983.762084.012173.902285.902371.722470.162581.512686.952786.092874.192969.323081.163173.943284.553377.793483.253572.543669.513770.703870.673978.504076.474177.084282.394375.624480.344574.104669.374770.064869.534977.825083.375178.815279.405385.155487.725584.335670.475776.575869.845971.026083.50Table 2
Evaluation results of time control behavior of college English teacher.
NumberScores150.93260.78351.34453.25555.17656.05751.87856.40957.721054.751152.401256.911350.781455.831550.131654.641752.411851.741956.552060.752155.622250.552356.872451.442550.502657.292760.442860.152956.203056.213150.393253.893351.773454.923560.163649.163754.713853.353957.494056.224149.594252.414350.154454.264553.864654.204751.744852.954957.935054.825157.725254.715357.585459.495550.165658.525750.765854.075951.756055.41Table 3
Evaluation results of college entertainment behavior of English teacher.
NumberScores169.61273.08372.19464.31569.59683.59777.07874.50978.191071.221167.241269.681365.661468.421564.851672.891779.481866.561973.062064.812172.712271.392365.462467.632571.552682.322765.162872.892964.193077.283169.543273.723378.623483.993578.663668.893772.473877.353973.984082.914176.134276.304370.184467.974569.724671.634777.234872.944968.525080.805166.855264.705364.525464.555582.265670.325767.955877.155980.566076.33Table 4
Evaluation results of teaching summary behavior of college English teacher.
NumberScores172.98270.58378.06476.24567.78670.25777.39869.41967.661078.511179.991269.351373.541477.591576.491672.831768.431867.821971.002073.352179.012277.732367.632479.822579.132676.722771.662873.632972.303076.343171.563274.233367.463476.313567.433673.183767.123877.033979.724071.524172.914271.844379.374475.804576.374668.724773.874873.924972.265072.875175.555270.545377.345474.455567.655672.935771.965879.825971.046068.80Figure 5
Statistical diagram of the correlation between college observation behavior of English teacher and teaching effect.Figure 6
Statistical diagram of the correlation between time control behavior of college English teacher and teaching effect.Figure 7
Statistical diagram of the correlation between entertainment behavior of college English teacher and teaching effect.Figure 8
Statistical diagram of the correlation between teaching summary behavior of college English teacher and teaching effect.From the abovementioned research, it can be seen that the teaching behavior of college English teachers will have a certain impact on students, and the positive behavior of teachers can effectively improve the teaching effect, which can be used as a reference for the formulation of subsequent teaching plans.


## 5. Conclusion

Teachers are the implementers and organizers of courses, and teaching activities are the main manifestations of courses. Since the launch of the new round of basic education curriculum reform, the concept, structure, content, implementation, evaluation, and management of the curriculum have made major breakthroughs and innovations compared to the past. Teachers’ educational philosophy and whether they can understand the new curriculum, student, teaching, and evaluation perspectives will directly affect the implementation of the new curriculum and the effectiveness of this round of curriculum reform, and these will be reflected in the teaching behavior of teachers The leadership behavior of English teachers involved in this paper refers to the various behaviors and activities shown in the process of influencing students through their physical or mental activities in the English curriculum teaching of the school, so as to achieve a certain teaching goal.The main purpose of this research is to explore the relationship between college English teachers’ teaching behavior and teaching effect. This article mainly uses the method of questionnaire survey and combines big data technology to carry on the data processing.



---

*Source: 4650250-2022-09-17.xml*