# Modern and Contemporary Literature Courses in Colleges and Universities Using the Teaching Mode of Deep Learning

Authors: Hong Jiang
Journal: Mobile Information Systems
 (2022)

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## Abstract

With the continuous promotion and development of Chinese around the world, increasingly foreign students come to China to learn Chinese. What they have learned is not only language skills but also the culture behind the language, because language and culture are integrated and inseparable. The purpose of this paper is to explore the teaching research of the teaching mode of using deep learning in contemporary literature courses in colleges and universities. This paper firstly introduces the distance and open education, which refers to a new type of education that uses modern teaching information technology to organize classroom teaching and deliver teaching content under the guidance of the concept of openness. The biggest difference between distance and open education and traditional education lies in the location and method of teaching. Then, the teaching of deep learning theory is explained, and the optimization algorithm of teaching and learning and the problem of education optimization are expounded. The algorithm successfully simulates the process of teachers passing professional knowledge to students and students’ self-learning professional knowledge. Finally, the teaching of modern and contemporary literature courses is investigated and analyzed. The results of this experimental survey show that more than 40% of the learners believe that modern and contemporary culture courses help to develop their Chinese communication skills. At the same time, this course will help foreign students to better master Chinese and understand China, which is beneficial and harmless. It can also change the world’s stereotype of China and shape a new image of China.


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## Body

## 1. Introduction

With the development of teaching Chinese as a foreign language around the world, increasingly foreigners have come to know China. To learn more about China, or simply have a love for Chinese and are interested in Chinese culture, foreigners have come to China to learn Chinese, and most of them have entered universities for short-term study or four-year courses like Chinese students. Therefore, colleges and universities also offer various courses for international students, including comprehensive courses, language skill courses (language listening, speaking, reading, writing, etc.), newspaper reading courses, and HSK improvement courses. There are also calligraphy classes, paper-cut classes, and literature classes on Chinese culture. However, the proportion of cultural courses is relatively small, which is of course related to factors such as less class hours for international students and heavy course tasks. On the other hand, we should also see that the content of the culture should not be underestimated. Because language and culture are unified and inseparable, language is the carrier of culture, and culture is displayed through language. In the final analysis, learning language is also a learning culture. Among these cultural courses, the most closely related language and culture should belong to Chinese literature, which can best reflect Chinese thought and culture. Among them, Chinese modern and contemporary literature is more in line with the current language norms than ancient literature. Students can not only learn language but also learn Chinese thought, culture, and cultivate sentiment through language. Therefore, the uniqueness of Chinese, modern, and contemporary literature courses has become more prominent. However, the current situation is that although the course of Chinese, modern, and contemporary literature is one of the courses for international students. For a long time, Chinese comprehensive courses and special skills courses, which are the main courses, have achieved great results, while Chinese literature courses are relatively lacking.This paper investigates and understands the curriculum setting and textbook selection of Chinese modern and contemporary literature. And we go deep into the classroom of Chinese, modern, and contemporary literature; understand the learning situation of international students and the teaching status of teachers; find problems; and put forward corresponding countermeasures and suggestions. The research on the course of Chinese, modern, and contemporary literature for international students provides a certain reference value for the further research of this course in China. The purpose of this paper is to research and discuss the teaching mode of modern and contemporary literature courses in colleges and universities under the background of distance and open education, to make certain contributions to the development of modern and contemporary literature courses.The innovation of this paper is reflected in: (1) the overall elaboration of distance and open education, which refers to an educational form that uses modern teaching information technology to organize classroom teaching and deliver teaching content under the guidance of the concept of openness; (2) a systematic introduction to the teaching and learning optimization algorithm; and (3) finally, the investigation and statistics are carried out on the teaching of modern and contemporary literature courses.


## 2. Related Work

According to the research progress in foreign countries, different researchers have also conducted corresponding cooperative research in the aspect of distance and open education. Ramdass addressed the “ambiguous” and possibly “slippery slope” nature of quality through a critical analysis of concepts such as the development, improvement, and assurance of teaching quality in higher education. The method is a case study approach using qualitative analysis in an Open and Distance Learning (ODL) institution [1]. Mathew and Chung discussed the comparison between diploma and degree student perspectives. The survey results show that most students have positive views on the implementation of ODL, while some students say they should not continue ODL next semester due to issues such as poor Internet connectivity, tight budgets, and time management issues. Mathew and Chung also provided improvement suggestions for better ODL implementation in the near future [2]. Saidi et al. conducted a survey to determine students’ preferences for the convenience of adopting online learning. The findings can inform educators in formulating the most appropriate tools as options for implementing ODL. Choosing the right tools is critical to ensuring that no student is left behind and that the teaching process is successful [3]. Ntombela aimed to initiate a discussion on the teaching and learning support needs of students with disabilities, especially in an open distance e-learning environment, and to propose strategies that can be used to increase the quality of their participation and improve their academic performance [4]. Khanna described an effective governance operating model that will encourage enhanced good governance during the overall work of Open and Distance Learning (ODL) institutions. A governance operating model developed based on the existing literature on structural and institutional governance will help improve the efficiency, effectiveness, and overall performance of the relevant ODL institutions [5]. Pretorius et al. addressed misconceptions about the role of ODEL in the transition to sustainability. Appropriately structured assessments require ODEL students to engage with real-world issues in their local environment and can provide them with valuable sustainability learning experiences [6]. However, these scholars did not research and discuss the teaching mode of modern and contemporary literature courses in colleges and universities under the background of distance and open education, but only discussed its significance unilaterally.


## 3. Methods of the Teaching Mode of Modern and Contemporary Literature Courses in Colleges and Universities under the Background of Distance and Open Education

### 3.1. Distance and Open Education

Distance education is an educational model that relies on advanced information technology to carry out distance teaching. It breaks the limitation of face-to-face teaching and enables students to learn more conveniently and freely without delaying work [7]. As shown in Figure 1, students’ learning cannot be limited by time and space, and the teaching mode has also changed from the traditional teacher’s teaching as the center to the students’ autonomous teaching as the center. This teaching mode conforms to the main theme of the development of this era of promoting human subjectivity [8]. Among them, the distance education organization mode can be divided into individual learning mode and collective learning mode, that is, individual learning and group learning.Figure 1
Distance education.Open education, broadly speaking, means that everyone enjoys the right to lifelong education, which not only means openness to educational objects, but also, more importantly, the openness of educational concepts, educational resources, and educational processes. In a narrow sense, open education is the modern distance education implemented by TVU. Open education first appeared in the Open University established in the UK. Its educational concept of opening schools has led to the emergence of a wave of remote open higher education in various places, which has brought opportunities for some people who have not had access to higher education to go to colleges and universities. Open education adopts a combination of face-to-face tutoring and online teaching, group guidance and individual guidance, individualized autonomous learning, and group collaborative learning to carry out teaching to meet the needs of students. Open education is not just a traditional teaching concept, but also a new teaching mode. It makes full use of modern teaching resources, advanced educational information technology, and modern teaching support services so that everyone in need of learning can be free from time constraints and receive lifelong education, thus promoting the popularization of higher education in China [9]. Open higher education is a talent training model under the conditions of advanced modern information technology. Using its unique teaching methods and management methods, it has cultivated a large number of applied talents that meet the needs of local and grassroots education [10].Therefore, open teaching and distance teaching are two categories that are both different and related. The so-called open teaching is to create opportunities for the learner group to receive higher education without being restricted by anyone. Open education represents the highest state of practice and is a basic theory [11]. Distance continuing teaching refers to the real field of quality education for teachers and students that separates working hours from each other. Therefore, open education is to realize the sharing of ideas for distance educators, while distance continuing teaching is the only a form of open education [12]. However, at the same time, under the impact of modern information technology, distance education has obvious open features in teaching mode and organizational form. The two are gradually integrated, promoting and improving each other, so there is the current “open distance education,” which is usually translated as open distance education in China [13]. It refers to a new form of education that uses modern educational technology to organize teaching activities (face-to-face, correspondence, and self-study) and deliver teaching content under the guidance of an open concept, as shown in Figure 2.Figure 2
Open distance education.


### 3.2. Deep Learning

Due to the accelerating speed of knowledge updating, to better meet the needs of social development, learners need to maintain their competitiveness through lifelong learning, which will lead to the continuous integration and updating of learners’ learning content and learning methods. Among them, shallow learning focuses on the mechanical memory of the learned content and only stays in a superficial understanding and memory state. Shallow learning mainly treats, understands, and memorizes the information encountered in the learning process as independent points and does not pay attention to the association of information. As a result, most of the information will be forgotten over time, and it is difficult for information to be stored in the brain for a long time and in a structured manner. The knowledge system advocated by deep learning has the characteristics of linking old and new knowledge with complex problems as the main line and pays attention to learners’ deep understanding of knowledge. Continuously, we integrate the learned knowledge into the learner’s cognitive structure, and the relevant content of the cognitive knowledge structure forms a beneficial and close connection. And we transfer and apply the acquired knowledge and abilities to other situations, and the main purpose is to solve practical problems. Therefore, the process of deep learning pays more attention to establishing the connection between new knowledge and the original knowledge system and establishes a knowledge cognitive structure and knowledge system by integrating learning content and learning methods.There is a time sequence between shallow learning and deep learning, and a close relationship is formed between them. This close relationship of succession and continuity requires learners to gradually change from the stage of shallow learning to the stage of deep learning, which has further requirements for the learners’ thinking level, and pays more attention to the development of learners’ higher-order thinking abilities. Shallow learning is limited to a lower level of learning and only stays in a low-level state of mind of knowing, comprehending, and applying. Deep learning is limited to a higher level of learning and forms more in-depth higher-order thinking on the basis of lower-order thinking. The main manifestations of higher-order thinking are analysis, evaluation, and creation, focusing on improving learners’ higher-order thinking abilities, and cultivating learners’ creative thinking abilities, problem-solving abilities, and critical thinking abilities.


### 3.3. Overview of Teaching and Learning Optimization Algorithms and Optimization Problems

#### 3.3.1. Basic Teaching and Learning Optimization Algorithm

The teaching and learning optimization algorithm imitates the process of teachers passing knowledge points to students and students’ self-learning knowledge points. Based on the investigation and understanding of the teacher’s teaching process, the basic model of the teaching and research optimization algorithm can be abstracted [14].Suppose that to teach the same course, teachers A and B are assigned to teach the course by two different classes, and here, it is assumed that the average grades of the students in the two classes are the same. Suppose again that after the two teachers have each carried out the teaching task of this course, the grades of the students in the classes led by A and B are shown as curves 1 and 2 in Figure3. Here, it is assumed that the grades of the students in the class obey a Gaussian distribution. As can be seen from Figure 3, if there is a significant difference in the teaching level of the two teachers, B will be higher than A, and the difference in the level of students in the two classes will be reflected in the average of the students’ grades in their respective classes. That is to say, a good teacher can increase the average grade of the whole class even more [15]. Figure 4 illustrates that when a teacher imparts knowledge to students in a class, the average teaching performance of the class increases from MA to MB. With the increase of students’ level, because the previous teachers are no longer competent for the school’s future educational tasks, teachers have changed from A to B. Students will continue to improve themselves under the leadership of B, and student grades will reach their maximum in such an iterative process. This process is abstracted as the teaching process in the TLBO algorithm. In addition to learning from teachers in the classroom, students will also communicate with each other and learn from each other after class, ask for advice from students who are better than themselves, learn from each other’s strengths and weaknesses, and improve their own grades, which forms the learning process in the TLBO algorithm. Through the continuous cycle of these two processes, students’ grades will continue to improve. The solution represented by each student will also be continuously updated and optimized until the algorithm stops when the optimal solution is reached. This process is the basic principle on which the teaching and learning optimization algorithm is based.Figure 3
Teaching effect diagram of different teachers.Figure 4
The distribution of grades in different classes after the completion of the teaching phase.In the most basic TLBO algorithm, students in each class represent a group. Each course mastered by a student represents a decision variable, the student’s grade represents the value of the objective function, and the teacher is the individual with the best objective function value in the class.For optimization problems:mingM|M∈T, T denotes the feasible region, M∈m1,m2,…,mk, k is the number of decision variables (the dimension of the solution space), Mp=mp,1,mp,2,…,mp,kp=1,2,…ER is a point in the search space, and ER represents the number of search points (i.e., the population size). The basic terms of teaching and learning optimization algorithms are explained as follows:Class: the set of all individuals in the search space is called a class.Teacher: the individual with the best grades (the smallest fitness value) in the class is called the teacher, represented byMT.Student: each individualMp=mp,1,mp,2,…,mp,kp=1,2,…,ER in the class is called a student.A class can be represented in the following way:(1)M1M2⋮MER|gM1gM2⋮gMER=m1,1m1,2⋯m1,km2,1m2,2⋯m2,k⋮⋮⋮⋮mER,1mER,2⋯mER,k|gM1gM2⋮gMERAmong them,Mp=mp,1,mp,2,…,mp,k represents class students.(2)MT=argmingMp,p=1,2,…,ER.ER is the number of students, andk is the number of subjects to be studied.(1) Teaching stage. The teaching stage refers to the process of students’ learning from teachers. Through teachers imparting professional knowledge to students, students can improve their academic performance to a certain extent [16].For the objective function G(M),M is a k-dimensional variable, and the pth student can be expressed as follows:(3)Mp=mp1,mp2,…,mpk.Fors students in a class, the average position can be expressed as follows:(4)Mmean=1s∑p=1smp1,∑p=1smp2,…,∑p=1smpk.Each student updates their position according to the formula:(5)Mp,new=Mp,old+rand∗Mt−UGMmean.Among them,Mp,new and Mp,old represent the new and original positions of the pth student, respectively. Mt for teacher, UG=roud1+rand0,12−1, is an integer with a random value of 1 or 2 [17].If the function valuegMp,new corresponding to the new position Mp,new of student p is better than the function value gMp,old corresponding to the original position Mp,old, then we replace the original position Mp,old with the new position Mp,new; otherwise, we keep the original position Mp,old unchanged.(2) Learning stage. The learning stage refers to the process in which both learners use common practice to enhance their strengths and avoid weaknesses and improve their own performance. Each student randomly selects a student from a class and then completes their study according to the difference between their own and the school’s grades [18].For studentMq, we randomly select a student Mh from all students and update its position in the following way:IfMq is better than Mh, which is gMq<gMh, then(6)Mq,new=Mq,old+rand∗Mq−Mh.Otherwise(7)Mq,new=Mq,old+rand∗Mh−Mq.If the function value corresponding to the student’s new positionMq,new is better than the original position Mq,old, the new position is accepted; otherwise, the original position remains unchanged.(3) Algorithm process. According to the above description, the basic TLBO algorithm flow is shown in Figure 5.Figure 5
TLBO algorithm flow.


#### 3.3.2. Constraint Handling Skills

The general intelligent optimization methods use unconstrained search methods, but the discovery of constraints greatly reduces the size of the space-efficient domain. Because once there are formula constraints, the effective range of the full space will be reduced to several curves under unconstrained optimization; thus, the discovery of constraints will also cause considerable difficulties in solving optimization problems [19]. Constraint processing technology is an insurmountable and important step to be able to use intelligent algorithms to solve constrained optimization problems. And choosing the appropriate constraint processing technology will provide great convenience for solving the optimal solution of the optimization problem.The penalty function is a common constraint processing technique.The penalty function method is simple in principle, easy to use, and complete with programs, and has high sensitivity of penalty items and strong operability. Due to the above advantages, it has formed the more commonly used constraint processing technology. The main idea is to add the penalty term k(a) constructed by the user in an algorithm to the objective function to construct the fitness function of the algorithm and thus achieve the penalty for impossible solutions. Therefore, the algorithm can better achieve a balance between achieving the optimal prediction of the objective function and meeting the constraints. In this way, the constrained optimization problem can be transformed into the most common unconstrained optimization problem so that the constrained optimization problem can be solved by the unconstrained search technique [20].When the penalty function is constructed, the more messages contained in the population are used, the better the constructed penalty function will be, but this will cause the penalty function to be too complicated, which is not conducive to the calculation of the program. The composition of the penalty term k(a) is usually based on the degree of individual constraint destruction F(a). Among them,Fa=max0,fa, f(a) is the constraint function for the constrained optimization problem.In the static penalty function method, the penalty factor selects a constant value, which does not change with the evolutionary algebraic structure and population changes. The operation of this method is very simple, but the penalty factor remains unchanged in the whole evolution process and will not use any information of population evolution, and the optimization effect obtained by this method is also very general. Among them, the representation of penalty function and fitness function is as follows:(8)ka=∑n=1ktnFn2a,fitnessa=ja+ka.Among them,tnn=1,2,…,k is the penalty factor, the value of which is given by the user of the algorithm, and k is the number of constraints.The death penalty function method is the most severe constraint processing method. It will not select any infeasible solution in the evolution process, so the population evolution information provided by the infeasible solution cannot be used in the evolution process. In the death penalty function method, the fitness value of the impossible solution is defined as infinite, that is to say, the priority of the infeasible solution is arranged after all feasible solutions. However, when there is no feasible solution in the population, the fitness value of all individuals is infinite, the algorithm will stop and cannot continue to run, and a new population needs to be regenerated until there is a feasible solution, and the algorithm can continue. When the feasible region is a convex set or the feasible region occupies most of the search space, the death penalty function method can be used.The penalty factor in the dynamic penalty function method changes with the change of evolutionary algebra. It can enlarge or reduce the penalty factor according to the evolutionary algebra to achieve better control over the penalty factor of the algorithm. The fitness value function is as follows:(9)fitnessa=ja+Srφ∑n=1kFnσa.Among them,r is the evolutionary algebra; S, ϕ, σ are the algorithm parameters; and k is the number of constraints. Although the dynamic penalty function method achieves a better solution effect than the static penalty function method, it is very difficult to select a suitable set of parameters and generally needs to be adjusted after many experiments.The adaptive penalty function method can automatically adjust the penalty factor by using the information of the population in the search process, so the overall optimization effect is better. The fitness value function and the penalty factor update method are as follows:(10)fitnessa=ja+γr∑n=1kFna.Among them,γr is the penalty factor with the following values:If(r modh = 0)(11)γr+1=γrs,ifkj>25%,γr∗s,ifkj<25%,γr,if15%<kj<25%.Else(12)γr+1=γr.end.Among them,h is a fixed constant, which means that γr is updated every h generation, and kj is the feasible solution ratio.According to whether there is a feasible solution in the population, and distinguishing the feasible solution from the infeasible solution, the following fitness value function is proposed:(13)fitnessa=ja+F^a=j^a,wa,j^a2+wa2+1−kjwa+kjj^a.Among them,jmin is the minimum value of the objective function, and jmax is the maximum value of the objective function. j^a is the normalization of the objective function. Fmax is the maximum constraint violation degree, and kj is the proportion of feasible solutions, that is,(14)jmin=jamina,jmax=jamaxa,j^a=ja−jminjmax−jmin,Fmax=Fnamaxa,wa=1k∑n=1kFnaFmax.Other penalty function methods mainly include random sorting algorithm and adaptive isolation constraint processing evolutionary algorithm. The random sorting algorithm is a relatively classical constraint processing technology so far. This method attempts to balance the objective function value and the constraint violation amount when setting the individual fitness value. It replaces the penalty factor with a parameter ofkj. Parameter kj represents the probability that individuals are compared only by the objective function value in the infeasible region. The core evolution process of the adaptive isolation constraint processing evolution algorithm is mainly composed of the following three parts: adaptive penalty function, constraint-based recombination, and isolation selection operator. The penalty factor of the adaptive penalty function is constructed in the following way:(15)φr+1=φrfact,ifδr>δtarget,φr∗fact,else.Among them, fact andδtarget are user-defined parameters, and fact >1, generally take δtarget=0.5; δr is the proportion of feasible solutions; and r is the evolutionary algebra.


## 3.1. Distance and Open Education

Distance education is an educational model that relies on advanced information technology to carry out distance teaching. It breaks the limitation of face-to-face teaching and enables students to learn more conveniently and freely without delaying work [7]. As shown in Figure 1, students’ learning cannot be limited by time and space, and the teaching mode has also changed from the traditional teacher’s teaching as the center to the students’ autonomous teaching as the center. This teaching mode conforms to the main theme of the development of this era of promoting human subjectivity [8]. Among them, the distance education organization mode can be divided into individual learning mode and collective learning mode, that is, individual learning and group learning.Figure 1
Distance education.Open education, broadly speaking, means that everyone enjoys the right to lifelong education, which not only means openness to educational objects, but also, more importantly, the openness of educational concepts, educational resources, and educational processes. In a narrow sense, open education is the modern distance education implemented by TVU. Open education first appeared in the Open University established in the UK. Its educational concept of opening schools has led to the emergence of a wave of remote open higher education in various places, which has brought opportunities for some people who have not had access to higher education to go to colleges and universities. Open education adopts a combination of face-to-face tutoring and online teaching, group guidance and individual guidance, individualized autonomous learning, and group collaborative learning to carry out teaching to meet the needs of students. Open education is not just a traditional teaching concept, but also a new teaching mode. It makes full use of modern teaching resources, advanced educational information technology, and modern teaching support services so that everyone in need of learning can be free from time constraints and receive lifelong education, thus promoting the popularization of higher education in China [9]. Open higher education is a talent training model under the conditions of advanced modern information technology. Using its unique teaching methods and management methods, it has cultivated a large number of applied talents that meet the needs of local and grassroots education [10].Therefore, open teaching and distance teaching are two categories that are both different and related. The so-called open teaching is to create opportunities for the learner group to receive higher education without being restricted by anyone. Open education represents the highest state of practice and is a basic theory [11]. Distance continuing teaching refers to the real field of quality education for teachers and students that separates working hours from each other. Therefore, open education is to realize the sharing of ideas for distance educators, while distance continuing teaching is the only a form of open education [12]. However, at the same time, under the impact of modern information technology, distance education has obvious open features in teaching mode and organizational form. The two are gradually integrated, promoting and improving each other, so there is the current “open distance education,” which is usually translated as open distance education in China [13]. It refers to a new form of education that uses modern educational technology to organize teaching activities (face-to-face, correspondence, and self-study) and deliver teaching content under the guidance of an open concept, as shown in Figure 2.Figure 2
Open distance education.


## 3.2. Deep Learning

Due to the accelerating speed of knowledge updating, to better meet the needs of social development, learners need to maintain their competitiveness through lifelong learning, which will lead to the continuous integration and updating of learners’ learning content and learning methods. Among them, shallow learning focuses on the mechanical memory of the learned content and only stays in a superficial understanding and memory state. Shallow learning mainly treats, understands, and memorizes the information encountered in the learning process as independent points and does not pay attention to the association of information. As a result, most of the information will be forgotten over time, and it is difficult for information to be stored in the brain for a long time and in a structured manner. The knowledge system advocated by deep learning has the characteristics of linking old and new knowledge with complex problems as the main line and pays attention to learners’ deep understanding of knowledge. Continuously, we integrate the learned knowledge into the learner’s cognitive structure, and the relevant content of the cognitive knowledge structure forms a beneficial and close connection. And we transfer and apply the acquired knowledge and abilities to other situations, and the main purpose is to solve practical problems. Therefore, the process of deep learning pays more attention to establishing the connection between new knowledge and the original knowledge system and establishes a knowledge cognitive structure and knowledge system by integrating learning content and learning methods.There is a time sequence between shallow learning and deep learning, and a close relationship is formed between them. This close relationship of succession and continuity requires learners to gradually change from the stage of shallow learning to the stage of deep learning, which has further requirements for the learners’ thinking level, and pays more attention to the development of learners’ higher-order thinking abilities. Shallow learning is limited to a lower level of learning and only stays in a low-level state of mind of knowing, comprehending, and applying. Deep learning is limited to a higher level of learning and forms more in-depth higher-order thinking on the basis of lower-order thinking. The main manifestations of higher-order thinking are analysis, evaluation, and creation, focusing on improving learners’ higher-order thinking abilities, and cultivating learners’ creative thinking abilities, problem-solving abilities, and critical thinking abilities.


## 3.3. Overview of Teaching and Learning Optimization Algorithms and Optimization Problems

### 3.3.1. Basic Teaching and Learning Optimization Algorithm

The teaching and learning optimization algorithm imitates the process of teachers passing knowledge points to students and students’ self-learning knowledge points. Based on the investigation and understanding of the teacher’s teaching process, the basic model of the teaching and research optimization algorithm can be abstracted [14].Suppose that to teach the same course, teachers A and B are assigned to teach the course by two different classes, and here, it is assumed that the average grades of the students in the two classes are the same. Suppose again that after the two teachers have each carried out the teaching task of this course, the grades of the students in the classes led by A and B are shown as curves 1 and 2 in Figure3. Here, it is assumed that the grades of the students in the class obey a Gaussian distribution. As can be seen from Figure 3, if there is a significant difference in the teaching level of the two teachers, B will be higher than A, and the difference in the level of students in the two classes will be reflected in the average of the students’ grades in their respective classes. That is to say, a good teacher can increase the average grade of the whole class even more [15]. Figure 4 illustrates that when a teacher imparts knowledge to students in a class, the average teaching performance of the class increases from MA to MB. With the increase of students’ level, because the previous teachers are no longer competent for the school’s future educational tasks, teachers have changed from A to B. Students will continue to improve themselves under the leadership of B, and student grades will reach their maximum in such an iterative process. This process is abstracted as the teaching process in the TLBO algorithm. In addition to learning from teachers in the classroom, students will also communicate with each other and learn from each other after class, ask for advice from students who are better than themselves, learn from each other’s strengths and weaknesses, and improve their own grades, which forms the learning process in the TLBO algorithm. Through the continuous cycle of these two processes, students’ grades will continue to improve. The solution represented by each student will also be continuously updated and optimized until the algorithm stops when the optimal solution is reached. This process is the basic principle on which the teaching and learning optimization algorithm is based.Figure 3
Teaching effect diagram of different teachers.Figure 4
The distribution of grades in different classes after the completion of the teaching phase.In the most basic TLBO algorithm, students in each class represent a group. Each course mastered by a student represents a decision variable, the student’s grade represents the value of the objective function, and the teacher is the individual with the best objective function value in the class.For optimization problems:mingM|M∈T, T denotes the feasible region, M∈m1,m2,…,mk, k is the number of decision variables (the dimension of the solution space), Mp=mp,1,mp,2,…,mp,kp=1,2,…ER is a point in the search space, and ER represents the number of search points (i.e., the population size). The basic terms of teaching and learning optimization algorithms are explained as follows:Class: the set of all individuals in the search space is called a class.Teacher: the individual with the best grades (the smallest fitness value) in the class is called the teacher, represented byMT.Student: each individualMp=mp,1,mp,2,…,mp,kp=1,2,…,ER in the class is called a student.A class can be represented in the following way:(1)M1M2⋮MER|gM1gM2⋮gMER=m1,1m1,2⋯m1,km2,1m2,2⋯m2,k⋮⋮⋮⋮mER,1mER,2⋯mER,k|gM1gM2⋮gMERAmong them,Mp=mp,1,mp,2,…,mp,k represents class students.(2)MT=argmingMp,p=1,2,…,ER.ER is the number of students, andk is the number of subjects to be studied.(1) Teaching stage. The teaching stage refers to the process of students’ learning from teachers. Through teachers imparting professional knowledge to students, students can improve their academic performance to a certain extent [16].For the objective function G(M),M is a k-dimensional variable, and the pth student can be expressed as follows:(3)Mp=mp1,mp2,…,mpk.Fors students in a class, the average position can be expressed as follows:(4)Mmean=1s∑p=1smp1,∑p=1smp2,…,∑p=1smpk.Each student updates their position according to the formula:(5)Mp,new=Mp,old+rand∗Mt−UGMmean.Among them,Mp,new and Mp,old represent the new and original positions of the pth student, respectively. Mt for teacher, UG=roud1+rand0,12−1, is an integer with a random value of 1 or 2 [17].If the function valuegMp,new corresponding to the new position Mp,new of student p is better than the function value gMp,old corresponding to the original position Mp,old, then we replace the original position Mp,old with the new position Mp,new; otherwise, we keep the original position Mp,old unchanged.(2) Learning stage. The learning stage refers to the process in which both learners use common practice to enhance their strengths and avoid weaknesses and improve their own performance. Each student randomly selects a student from a class and then completes their study according to the difference between their own and the school’s grades [18].For studentMq, we randomly select a student Mh from all students and update its position in the following way:IfMq is better than Mh, which is gMq<gMh, then(6)Mq,new=Mq,old+rand∗Mq−Mh.Otherwise(7)Mq,new=Mq,old+rand∗Mh−Mq.If the function value corresponding to the student’s new positionMq,new is better than the original position Mq,old, the new position is accepted; otherwise, the original position remains unchanged.(3) Algorithm process. According to the above description, the basic TLBO algorithm flow is shown in Figure 5.Figure 5
TLBO algorithm flow.


### 3.3.2. Constraint Handling Skills

The general intelligent optimization methods use unconstrained search methods, but the discovery of constraints greatly reduces the size of the space-efficient domain. Because once there are formula constraints, the effective range of the full space will be reduced to several curves under unconstrained optimization; thus, the discovery of constraints will also cause considerable difficulties in solving optimization problems [19]. Constraint processing technology is an insurmountable and important step to be able to use intelligent algorithms to solve constrained optimization problems. And choosing the appropriate constraint processing technology will provide great convenience for solving the optimal solution of the optimization problem.The penalty function is a common constraint processing technique.The penalty function method is simple in principle, easy to use, and complete with programs, and has high sensitivity of penalty items and strong operability. Due to the above advantages, it has formed the more commonly used constraint processing technology. The main idea is to add the penalty term k(a) constructed by the user in an algorithm to the objective function to construct the fitness function of the algorithm and thus achieve the penalty for impossible solutions. Therefore, the algorithm can better achieve a balance between achieving the optimal prediction of the objective function and meeting the constraints. In this way, the constrained optimization problem can be transformed into the most common unconstrained optimization problem so that the constrained optimization problem can be solved by the unconstrained search technique [20].When the penalty function is constructed, the more messages contained in the population are used, the better the constructed penalty function will be, but this will cause the penalty function to be too complicated, which is not conducive to the calculation of the program. The composition of the penalty term k(a) is usually based on the degree of individual constraint destruction F(a). Among them,Fa=max0,fa, f(a) is the constraint function for the constrained optimization problem.In the static penalty function method, the penalty factor selects a constant value, which does not change with the evolutionary algebraic structure and population changes. The operation of this method is very simple, but the penalty factor remains unchanged in the whole evolution process and will not use any information of population evolution, and the optimization effect obtained by this method is also very general. Among them, the representation of penalty function and fitness function is as follows:(8)ka=∑n=1ktnFn2a,fitnessa=ja+ka.Among them,tnn=1,2,…,k is the penalty factor, the value of which is given by the user of the algorithm, and k is the number of constraints.The death penalty function method is the most severe constraint processing method. It will not select any infeasible solution in the evolution process, so the population evolution information provided by the infeasible solution cannot be used in the evolution process. In the death penalty function method, the fitness value of the impossible solution is defined as infinite, that is to say, the priority of the infeasible solution is arranged after all feasible solutions. However, when there is no feasible solution in the population, the fitness value of all individuals is infinite, the algorithm will stop and cannot continue to run, and a new population needs to be regenerated until there is a feasible solution, and the algorithm can continue. When the feasible region is a convex set or the feasible region occupies most of the search space, the death penalty function method can be used.The penalty factor in the dynamic penalty function method changes with the change of evolutionary algebra. It can enlarge or reduce the penalty factor according to the evolutionary algebra to achieve better control over the penalty factor of the algorithm. The fitness value function is as follows:(9)fitnessa=ja+Srφ∑n=1kFnσa.Among them,r is the evolutionary algebra; S, ϕ, σ are the algorithm parameters; and k is the number of constraints. Although the dynamic penalty function method achieves a better solution effect than the static penalty function method, it is very difficult to select a suitable set of parameters and generally needs to be adjusted after many experiments.The adaptive penalty function method can automatically adjust the penalty factor by using the information of the population in the search process, so the overall optimization effect is better. The fitness value function and the penalty factor update method are as follows:(10)fitnessa=ja+γr∑n=1kFna.Among them,γr is the penalty factor with the following values:If(r modh = 0)(11)γr+1=γrs,ifkj>25%,γr∗s,ifkj<25%,γr,if15%<kj<25%.Else(12)γr+1=γr.end.Among them,h is a fixed constant, which means that γr is updated every h generation, and kj is the feasible solution ratio.According to whether there is a feasible solution in the population, and distinguishing the feasible solution from the infeasible solution, the following fitness value function is proposed:(13)fitnessa=ja+F^a=j^a,wa,j^a2+wa2+1−kjwa+kjj^a.Among them,jmin is the minimum value of the objective function, and jmax is the maximum value of the objective function. j^a is the normalization of the objective function. Fmax is the maximum constraint violation degree, and kj is the proportion of feasible solutions, that is,(14)jmin=jamina,jmax=jamaxa,j^a=ja−jminjmax−jmin,Fmax=Fnamaxa,wa=1k∑n=1kFnaFmax.Other penalty function methods mainly include random sorting algorithm and adaptive isolation constraint processing evolutionary algorithm. The random sorting algorithm is a relatively classical constraint processing technology so far. This method attempts to balance the objective function value and the constraint violation amount when setting the individual fitness value. It replaces the penalty factor with a parameter ofkj. Parameter kj represents the probability that individuals are compared only by the objective function value in the infeasible region. The core evolution process of the adaptive isolation constraint processing evolution algorithm is mainly composed of the following three parts: adaptive penalty function, constraint-based recombination, and isolation selection operator. The penalty factor of the adaptive penalty function is constructed in the following way:(15)φr+1=φrfact,ifδr>δtarget,φr∗fact,else.Among them, fact andδtarget are user-defined parameters, and fact >1, generally take δtarget=0.5; δr is the proportion of feasible solutions; and r is the evolutionary algebra.


## 3.3.1. Basic Teaching and Learning Optimization Algorithm

The teaching and learning optimization algorithm imitates the process of teachers passing knowledge points to students and students’ self-learning knowledge points. Based on the investigation and understanding of the teacher’s teaching process, the basic model of the teaching and research optimization algorithm can be abstracted [14].Suppose that to teach the same course, teachers A and B are assigned to teach the course by two different classes, and here, it is assumed that the average grades of the students in the two classes are the same. Suppose again that after the two teachers have each carried out the teaching task of this course, the grades of the students in the classes led by A and B are shown as curves 1 and 2 in Figure3. Here, it is assumed that the grades of the students in the class obey a Gaussian distribution. As can be seen from Figure 3, if there is a significant difference in the teaching level of the two teachers, B will be higher than A, and the difference in the level of students in the two classes will be reflected in the average of the students’ grades in their respective classes. That is to say, a good teacher can increase the average grade of the whole class even more [15]. Figure 4 illustrates that when a teacher imparts knowledge to students in a class, the average teaching performance of the class increases from MA to MB. With the increase of students’ level, because the previous teachers are no longer competent for the school’s future educational tasks, teachers have changed from A to B. Students will continue to improve themselves under the leadership of B, and student grades will reach their maximum in such an iterative process. This process is abstracted as the teaching process in the TLBO algorithm. In addition to learning from teachers in the classroom, students will also communicate with each other and learn from each other after class, ask for advice from students who are better than themselves, learn from each other’s strengths and weaknesses, and improve their own grades, which forms the learning process in the TLBO algorithm. Through the continuous cycle of these two processes, students’ grades will continue to improve. The solution represented by each student will also be continuously updated and optimized until the algorithm stops when the optimal solution is reached. This process is the basic principle on which the teaching and learning optimization algorithm is based.Figure 3
Teaching effect diagram of different teachers.Figure 4
The distribution of grades in different classes after the completion of the teaching phase.In the most basic TLBO algorithm, students in each class represent a group. Each course mastered by a student represents a decision variable, the student’s grade represents the value of the objective function, and the teacher is the individual with the best objective function value in the class.For optimization problems:mingM|M∈T, T denotes the feasible region, M∈m1,m2,…,mk, k is the number of decision variables (the dimension of the solution space), Mp=mp,1,mp,2,…,mp,kp=1,2,…ER is a point in the search space, and ER represents the number of search points (i.e., the population size). The basic terms of teaching and learning optimization algorithms are explained as follows:Class: the set of all individuals in the search space is called a class.Teacher: the individual with the best grades (the smallest fitness value) in the class is called the teacher, represented byMT.Student: each individualMp=mp,1,mp,2,…,mp,kp=1,2,…,ER in the class is called a student.A class can be represented in the following way:(1)M1M2⋮MER|gM1gM2⋮gMER=m1,1m1,2⋯m1,km2,1m2,2⋯m2,k⋮⋮⋮⋮mER,1mER,2⋯mER,k|gM1gM2⋮gMERAmong them,Mp=mp,1,mp,2,…,mp,k represents class students.(2)MT=argmingMp,p=1,2,…,ER.ER is the number of students, andk is the number of subjects to be studied.(1) Teaching stage. The teaching stage refers to the process of students’ learning from teachers. Through teachers imparting professional knowledge to students, students can improve their academic performance to a certain extent [16].For the objective function G(M),M is a k-dimensional variable, and the pth student can be expressed as follows:(3)Mp=mp1,mp2,…,mpk.Fors students in a class, the average position can be expressed as follows:(4)Mmean=1s∑p=1smp1,∑p=1smp2,…,∑p=1smpk.Each student updates their position according to the formula:(5)Mp,new=Mp,old+rand∗Mt−UGMmean.Among them,Mp,new and Mp,old represent the new and original positions of the pth student, respectively. Mt for teacher, UG=roud1+rand0,12−1, is an integer with a random value of 1 or 2 [17].If the function valuegMp,new corresponding to the new position Mp,new of student p is better than the function value gMp,old corresponding to the original position Mp,old, then we replace the original position Mp,old with the new position Mp,new; otherwise, we keep the original position Mp,old unchanged.(2) Learning stage. The learning stage refers to the process in which both learners use common practice to enhance their strengths and avoid weaknesses and improve their own performance. Each student randomly selects a student from a class and then completes their study according to the difference between their own and the school’s grades [18].For studentMq, we randomly select a student Mh from all students and update its position in the following way:IfMq is better than Mh, which is gMq<gMh, then(6)Mq,new=Mq,old+rand∗Mq−Mh.Otherwise(7)Mq,new=Mq,old+rand∗Mh−Mq.If the function value corresponding to the student’s new positionMq,new is better than the original position Mq,old, the new position is accepted; otherwise, the original position remains unchanged.(3) Algorithm process. According to the above description, the basic TLBO algorithm flow is shown in Figure 5.Figure 5
TLBO algorithm flow.


## 3.3.2. Constraint Handling Skills

The general intelligent optimization methods use unconstrained search methods, but the discovery of constraints greatly reduces the size of the space-efficient domain. Because once there are formula constraints, the effective range of the full space will be reduced to several curves under unconstrained optimization; thus, the discovery of constraints will also cause considerable difficulties in solving optimization problems [19]. Constraint processing technology is an insurmountable and important step to be able to use intelligent algorithms to solve constrained optimization problems. And choosing the appropriate constraint processing technology will provide great convenience for solving the optimal solution of the optimization problem.The penalty function is a common constraint processing technique.The penalty function method is simple in principle, easy to use, and complete with programs, and has high sensitivity of penalty items and strong operability. Due to the above advantages, it has formed the more commonly used constraint processing technology. The main idea is to add the penalty term k(a) constructed by the user in an algorithm to the objective function to construct the fitness function of the algorithm and thus achieve the penalty for impossible solutions. Therefore, the algorithm can better achieve a balance between achieving the optimal prediction of the objective function and meeting the constraints. In this way, the constrained optimization problem can be transformed into the most common unconstrained optimization problem so that the constrained optimization problem can be solved by the unconstrained search technique [20].When the penalty function is constructed, the more messages contained in the population are used, the better the constructed penalty function will be, but this will cause the penalty function to be too complicated, which is not conducive to the calculation of the program. The composition of the penalty term k(a) is usually based on the degree of individual constraint destruction F(a). Among them,Fa=max0,fa, f(a) is the constraint function for the constrained optimization problem.In the static penalty function method, the penalty factor selects a constant value, which does not change with the evolutionary algebraic structure and population changes. The operation of this method is very simple, but the penalty factor remains unchanged in the whole evolution process and will not use any information of population evolution, and the optimization effect obtained by this method is also very general. Among them, the representation of penalty function and fitness function is as follows:(8)ka=∑n=1ktnFn2a,fitnessa=ja+ka.Among them,tnn=1,2,…,k is the penalty factor, the value of which is given by the user of the algorithm, and k is the number of constraints.The death penalty function method is the most severe constraint processing method. It will not select any infeasible solution in the evolution process, so the population evolution information provided by the infeasible solution cannot be used in the evolution process. In the death penalty function method, the fitness value of the impossible solution is defined as infinite, that is to say, the priority of the infeasible solution is arranged after all feasible solutions. However, when there is no feasible solution in the population, the fitness value of all individuals is infinite, the algorithm will stop and cannot continue to run, and a new population needs to be regenerated until there is a feasible solution, and the algorithm can continue. When the feasible region is a convex set or the feasible region occupies most of the search space, the death penalty function method can be used.The penalty factor in the dynamic penalty function method changes with the change of evolutionary algebra. It can enlarge or reduce the penalty factor according to the evolutionary algebra to achieve better control over the penalty factor of the algorithm. The fitness value function is as follows:(9)fitnessa=ja+Srφ∑n=1kFnσa.Among them,r is the evolutionary algebra; S, ϕ, σ are the algorithm parameters; and k is the number of constraints. Although the dynamic penalty function method achieves a better solution effect than the static penalty function method, it is very difficult to select a suitable set of parameters and generally needs to be adjusted after many experiments.The adaptive penalty function method can automatically adjust the penalty factor by using the information of the population in the search process, so the overall optimization effect is better. The fitness value function and the penalty factor update method are as follows:(10)fitnessa=ja+γr∑n=1kFna.Among them,γr is the penalty factor with the following values:If(r modh = 0)(11)γr+1=γrs,ifkj>25%,γr∗s,ifkj<25%,γr,if15%<kj<25%.Else(12)γr+1=γr.end.Among them,h is a fixed constant, which means that γr is updated every h generation, and kj is the feasible solution ratio.According to whether there is a feasible solution in the population, and distinguishing the feasible solution from the infeasible solution, the following fitness value function is proposed:(13)fitnessa=ja+F^a=j^a,wa,j^a2+wa2+1−kjwa+kjj^a.Among them,jmin is the minimum value of the objective function, and jmax is the maximum value of the objective function. j^a is the normalization of the objective function. Fmax is the maximum constraint violation degree, and kj is the proportion of feasible solutions, that is,(14)jmin=jamina,jmax=jamaxa,j^a=ja−jminjmax−jmin,Fmax=Fnamaxa,wa=1k∑n=1kFnaFmax.Other penalty function methods mainly include random sorting algorithm and adaptive isolation constraint processing evolutionary algorithm. The random sorting algorithm is a relatively classical constraint processing technology so far. This method attempts to balance the objective function value and the constraint violation amount when setting the individual fitness value. It replaces the penalty factor with a parameter ofkj. Parameter kj represents the probability that individuals are compared only by the objective function value in the infeasible region. The core evolution process of the adaptive isolation constraint processing evolution algorithm is mainly composed of the following three parts: adaptive penalty function, constraint-based recombination, and isolation selection operator. The penalty factor of the adaptive penalty function is constructed in the following way:(15)φr+1=φrfact,ifδr>δtarget,φr∗fact,else.Among them, fact andδtarget are user-defined parameters, and fact >1, generally take δtarget=0.5; δr is the proportion of feasible solutions; and r is the evolutionary algebra.


## 4. Experimental Results of the Teaching Mode of Modern and Contemporary Literature Courses in Colleges and Universities under the Background of Distance and Open Education

### 4.1. Questionnaire Survey

A total of 62 questionnaires were sent out, 61 were received, and 59 were valid. The content of this questionnaire is about the learning situation of foreign students in Chinese, modern, and contemporary literature classes. The specific content involves the basic information of students, such as major grade, gender, nationality, time spent learning Chinese, and HSK level. Students' suggestions on Contemporary literature, teaching materials, and the course. The research objects are fourth-year Chinese language and literature majors and third-year Chinese international education majors.


### 4.2. Views on Modern and Contemporary Literature Courses

Figure6 shows the students’ interest in modern and contemporary literature courses. Most students in both majors are not very interested in the literature, which may be because literary works are more profound, thoughtful, and not easy to understand.Figure 6
Interest in modern and contemporary literature classes.Figure7 shows the degree of preference for this course. Most Chinese language and literature majors are not clear about their preference for this course and think it should be elective, while the opposite is true for Chinese international education majors. This may be because students majoring in Chinese with speakers of other languages have higher Chinese proficiency, so they are more motivated to study the course.Figure 7
The degree of liking for the course.When asked why they liked the course, more than 40% of students focused on “effectively improving their language communicative competence,” indicating that foreign students felt that the course helped improve their language communicative competence as shown in Table1.Table 1
Reasons for liking the course.
Chinese language and literature major (%)Chinese international education major (%)Interested in the content of the textbook15.331.9Teacher’s teaching method is good23.220.4The classroom atmosphere is relaxed, and students communicate with each other more19.34.6Can effectively improve language communication skills42.243.1Most of the reasons for not liking this course are concentrated on other reasons, thinking that “the content of the text is outdated and meaningless,” indicating that there are problems such as outdated content in this course as shown in Table2.Table 2
Reasons for not liking the course.
Chinese language and literature major (%)Chinese international education major (%)The content of the text is old and boring2321.6The way of class has no characteristics, and it is no different from other classes15.518.3It does not help you improve your language skills7.79.2Other53.849.9When asked what they hope to learn through Chinese, modern, and contemporary literature courses, most foreign students majoring in Chinese language and literature hope to learn “vocabulary and grammar in work” and “understand Chinese culture and national conditions.” This may be because the rich and beautiful language in literary works can expand the vocabulary of international students, and international students can also see the development of Chinese society through literary works.Students majoring in teaching Chinese to speakers of other languages are more likely to “understand Chinese values” and “understand the similarities and differences between Chinese culture and their own cultures” as shown in Table3.Table 3
Hope to learn from Chinese, modern, and contemporary literature courses (multiple choices are allowed).
Chinese language and literature majorChinese international education major (%)Knowledge of vocabulary and grammar in writing46.1%49.9Improve listening and writing skills15.5%31.7Understand Chinese values30.7%77.2Learn about Chinese culture and national conditions46.1%58.9Understand the similarities and differences between Chinese culture and your own country’s culture38.650.2


### 4.3. Views on Literature

Figure8 shows the student interest in various literary genres. The literary genres that most foreign students are interested in are fiction and prose, which may be related to the fact that these two genres are highly narrative and easy to understand. Secondly, the works of favorite writers mainly express the common emotional value of human beings, such as The Back Puppy Baodi. And in poems, the language of which is considered more beautiful by international students, such as Farewell to Cambridge Error. Most of the works of writers who think that they are difficult to understand have many new words, complex language, and profound and difficult ideas, such as The Taoist Pagoda, The True Story of Ah Q, and The Diary of a Madman.Figure 8
Level of interest in each literary genre.


### 4.4. Views on Teacher Teaching

It can be seen from Table4 that the teaching methods of teachers in this course tend to be dominated by teacher explanations.Table 4
Teaching methods of teachers.
Chinese language and literature major (%)Chinese international education major (%)Focuses on explaining vocabulary and grammar7.89.2Mainly to introduce the content of the text and background knowledge53.749.9Mainly focus on students’ speech and discussion on the content of the text7.89.2Combination of teacher’s explanation and student’s speech30.731.7Figure9 shows students’ understanding of what the teacher said in class. Most of the foreign students who can understand what the teacher is saying account for a large proportion, while a small number of students cannot understand it for different reasons. Table 5 shows the reasons why students could not understand. Students majoring in Chinese language and literature think that “the language used by the teacher is too difficult” and “the content of the teacher’s speech is very abstract,” which is related to the relatively low Chinese proficiency of the students in this major, but the teacher’s language also has problems such as difficulty and abstraction. The reason Chinese students majoring in international education do not understand this course is because they “do not preview,” which shows that students of this major can learn this course well by cooperating with the teacher’s classroom explanation on the premise of previewing.Figure 9
Comprehension of what the teacher taught in the class.Table 5
Reasons for not understanding.
Chinese language and literature majorChinese international education major (%)Focuses on explaining vocabulary and grammar. The language used by the teacher is too difficult30.7%13.5The content of the teacher’s speech is very abstract38.6%27.4No preview—49.9Other—9.2Figure10 shows statistics on students’ most preferred form of teaching. Compared with written books, foreign students prefer the teaching forms of “video” and “ppt” because they are more three-dimensional and vivid, which can well arouse students’ enthusiasm for learning. Finally, the suggestions made by the students indicated that teachers should pay more attention to the comparison between Chinese and foreign countries and the arrangement of after-school tasks.Figure 10
Favorite teaching format (multiple choice).


## 4.1. Questionnaire Survey

A total of 62 questionnaires were sent out, 61 were received, and 59 were valid. The content of this questionnaire is about the learning situation of foreign students in Chinese, modern, and contemporary literature classes. The specific content involves the basic information of students, such as major grade, gender, nationality, time spent learning Chinese, and HSK level. Students' suggestions on Contemporary literature, teaching materials, and the course. The research objects are fourth-year Chinese language and literature majors and third-year Chinese international education majors.


## 4.2. Views on Modern and Contemporary Literature Courses

Figure6 shows the students’ interest in modern and contemporary literature courses. Most students in both majors are not very interested in the literature, which may be because literary works are more profound, thoughtful, and not easy to understand.Figure 6
Interest in modern and contemporary literature classes.Figure7 shows the degree of preference for this course. Most Chinese language and literature majors are not clear about their preference for this course and think it should be elective, while the opposite is true for Chinese international education majors. This may be because students majoring in Chinese with speakers of other languages have higher Chinese proficiency, so they are more motivated to study the course.Figure 7
The degree of liking for the course.When asked why they liked the course, more than 40% of students focused on “effectively improving their language communicative competence,” indicating that foreign students felt that the course helped improve their language communicative competence as shown in Table1.Table 1
Reasons for liking the course.
Chinese language and literature major (%)Chinese international education major (%)Interested in the content of the textbook15.331.9Teacher’s teaching method is good23.220.4The classroom atmosphere is relaxed, and students communicate with each other more19.34.6Can effectively improve language communication skills42.243.1Most of the reasons for not liking this course are concentrated on other reasons, thinking that “the content of the text is outdated and meaningless,” indicating that there are problems such as outdated content in this course as shown in Table2.Table 2
Reasons for not liking the course.
Chinese language and literature major (%)Chinese international education major (%)The content of the text is old and boring2321.6The way of class has no characteristics, and it is no different from other classes15.518.3It does not help you improve your language skills7.79.2Other53.849.9When asked what they hope to learn through Chinese, modern, and contemporary literature courses, most foreign students majoring in Chinese language and literature hope to learn “vocabulary and grammar in work” and “understand Chinese culture and national conditions.” This may be because the rich and beautiful language in literary works can expand the vocabulary of international students, and international students can also see the development of Chinese society through literary works.Students majoring in teaching Chinese to speakers of other languages are more likely to “understand Chinese values” and “understand the similarities and differences between Chinese culture and their own cultures” as shown in Table3.Table 3
Hope to learn from Chinese, modern, and contemporary literature courses (multiple choices are allowed).
Chinese language and literature majorChinese international education major (%)Knowledge of vocabulary and grammar in writing46.1%49.9Improve listening and writing skills15.5%31.7Understand Chinese values30.7%77.2Learn about Chinese culture and national conditions46.1%58.9Understand the similarities and differences between Chinese culture and your own country’s culture38.650.2


## 4.3. Views on Literature

Figure8 shows the student interest in various literary genres. The literary genres that most foreign students are interested in are fiction and prose, which may be related to the fact that these two genres are highly narrative and easy to understand. Secondly, the works of favorite writers mainly express the common emotional value of human beings, such as The Back Puppy Baodi. And in poems, the language of which is considered more beautiful by international students, such as Farewell to Cambridge Error. Most of the works of writers who think that they are difficult to understand have many new words, complex language, and profound and difficult ideas, such as The Taoist Pagoda, The True Story of Ah Q, and The Diary of a Madman.Figure 8
Level of interest in each literary genre.


## 4.4. Views on Teacher Teaching

It can be seen from Table4 that the teaching methods of teachers in this course tend to be dominated by teacher explanations.Table 4
Teaching methods of teachers.
Chinese language and literature major (%)Chinese international education major (%)Focuses on explaining vocabulary and grammar7.89.2Mainly to introduce the content of the text and background knowledge53.749.9Mainly focus on students’ speech and discussion on the content of the text7.89.2Combination of teacher’s explanation and student’s speech30.731.7Figure9 shows students’ understanding of what the teacher said in class. Most of the foreign students who can understand what the teacher is saying account for a large proportion, while a small number of students cannot understand it for different reasons. Table 5 shows the reasons why students could not understand. Students majoring in Chinese language and literature think that “the language used by the teacher is too difficult” and “the content of the teacher’s speech is very abstract,” which is related to the relatively low Chinese proficiency of the students in this major, but the teacher’s language also has problems such as difficulty and abstraction. The reason Chinese students majoring in international education do not understand this course is because they “do not preview,” which shows that students of this major can learn this course well by cooperating with the teacher’s classroom explanation on the premise of previewing.Figure 9
Comprehension of what the teacher taught in the class.Table 5
Reasons for not understanding.
Chinese language and literature majorChinese international education major (%)Focuses on explaining vocabulary and grammar. The language used by the teacher is too difficult30.7%13.5The content of the teacher’s speech is very abstract38.6%27.4No preview—49.9Other—9.2Figure10 shows statistics on students’ most preferred form of teaching. Compared with written books, foreign students prefer the teaching forms of “video” and “ppt” because they are more three-dimensional and vivid, which can well arouse students’ enthusiasm for learning. Finally, the suggestions made by the students indicated that teachers should pay more attention to the comparison between Chinese and foreign countries and the arrangement of after-school tasks.Figure 10
Favorite teaching format (multiple choice).


## 5. Discussion

Modern and contemporary literature teaching has both advantages and disadvantages. The advantage is that teachers can adjust the teaching process and teaching content according to the Chinese proficiency of students of different majors. Teachers are good at making students understand the content of the text in the form of storytelling. At the same time, combining some real-life examples can make the classroom more lively and interesting. They have a solid foundation in the literature and will promptly praise students for good performance. However, its shortcomings are as follows: the teaching method of teachers is relatively simple, mainly based on teachers’ teaching; the amount of information is large; it is easy to cause students’ fatigue; and it is easy to be distracted after a period of attention. The teacher’s voice is small and flat, and there are few teaching aids, which affects the teaching effect of the teacher. In addition, teachers expand extracurricular knowledge for students less and do not assign extracurricular homework and extracurricular tasks to students.Teachers should also master certain skills in teaching Chinese as a foreign language, such as the introduction before explaining literary works, and how to actively and effectively explain new words and phrases. Teachers can also learn from the teaching theories and teaching methods of Chinese, modern, and contemporary literature courses for Chinese majors and English language and literature courses for English majors in colleges and universities, and then determine the corresponding teaching plans and teaching methods according to specific teaching objects.


## 6. Conclusions

As increasingly people understand Chinese, increasingly foreign students come to China. Colleges and universities offer a variety of courses. Besides traditional language skills, they also offer various cultural courses such as calligraphy, paper cutting, Chinese culture, and literature. Among them, the literature course, as one of the main courses of the culture course, has played a positive role in learning Chinese for international students. Through the questionnaire survey and interview analysis, this paper puts forward some teaching strategies as a useful supplement to the teaching of Chinese, modern, and contemporary literature for international students. It is hoped that this research can provide a useful reference for the teaching of Chinese, modern, and contemporary literature to international students. Due to my limited time and ability, the number of questionnaires is relatively thin, and the persuasiveness is not strong enough. In addition, some of the views in the article are also subjective, and the interviews with teachers are not in-depth enough, and more of them are classroom observations. I hope that these issues can be continuously improved and perfected in future research.



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*Source: 3517022-2022-08-30.xml*