Abstract
In recent years, the fragmented learning mode of college English is the product of the integration of English teaching reform and information technology. Because of the mobile information environment, how knowledge is presented and distributed has altered significantly. As research on current college students continues to expand, the concept of fragmented learning is being utilized more and more frequently. Fragmented learning is defined as having features of fragmentation in time, space, content, and method of learning. Although fragmented learning makes it easier for college students to study, besides it also has several drawbacks, such as fragmented knowledge being scattered, no structure is developed, fragmented content being jumbled, and fragmented learning being flashy and merely a formality. To overcome these issues, this research work examines the evaluation system for the fragmented learning effect. In addition, it studies the effect evaluation of fragmented English learning in the mobile information environment and uses the integrated BPNN and genetic algorithm to evaluate the effect of fragmented English learning. The results show that this method has a strong effect on the evaluation of fragmented English learning in colleges and universities.
1. Introduction
The rapid expansion of the social economy and information technology, as well as the arrival of the Internet+ era, has resulted in an acceleration of the pace of people’s lives, work, and education. People’s lifestyles have changed as a result of the convenience of Internet technology and the use of various data terminals, which have improved their work and study efficiency [1–3]. However, what has resulted is that people are engaged in more frequent work and study. In addition, all daily activities appear to be accelerated, people can freely manage less time due to which the severity of the condition is increasing all the time, as a result, time fragments have become more prevalent. People’s requirements are increasing as society continues to evolve at a rapid pace [2]. The subject of how contemporary individuals might learn knowledge efficiently, quickly, and in big quantities in a limited and fragmented amount of time has become a highly important one [3, 4]. Furthermore, with the fast advancement of information technology, mobile terminal devices have become increasingly smart, and people’s learning routines and methodologies have shifted. Traditional paper learning cannot fulfill the demand of today’s learners, and fragmented learning has become progressively popular among college students. College students have a lot of classes and are under a lot of academic stress. Pupils can collect English knowledge bit by bit in fragmentary time by using this fresh active learning, rather than only in the restricted time and space of English classes. Students can learn English independently based on their own needs and acceptance capacity to increase their interest in the learning impact of English learning. The implementation of fragmented learning in the field of education has progressively been emphasized, and its impact on English learning has grown in importance.
It is important to know the difference between scattered learning and other types of learning. The definition of scattered learning in academia is currently unclear, and there is no clear definition. Some researchers believe that fragmented learning in the context of big data should be understood from two perspectives: on the one hand, the rapid development of science and technology, as well as the updating of scientific knowledge; and on the other, the fragmentation of learning in the context of big data [5]. As the rate of change has increased, which the total amount of information has increased exponentially, a phenomenon known as a “knowledge explosion” has occurred. So, massive amounts of knowledge have accumulated over time, each with a distinct characteristic. As a result of the introduction of numerous new media, learners are now able to learn in a variety of methods. A growing number of people are using the fragmented time to consume fragmented content and absorb fragmented information. As a result, students must employ new thinking and learning strategies. Furthermore, utilize fragmented time, receive relevant fragmented knowledge via the Internet + platform, and repeat, develop, and process knowledge [6]. Finally, internalize it into one’s knowledge to become knowledge self-sufficient. Similarly, some scholars categorize fragmented learning into two categories: broad and limited [7]. A broader definition of fragmented learning includes formal and informal learning in which learners employ a variety of materials and media that are dispersed over a wide range of time and space, whereas a narrower definition restricts fragmented learning to informal learning exclusively. For this reason, fragmented learning is defined by the authors of [8, 9] as a new type of informal learning that makes use of the Internet to obtain fragmented knowledge at fragmented times according to the needs of learners and then organize and process the fragmented knowledge to promote its development.
At this age of networking, students’ options for learning English are not confined to a single type of medium. One of the alternative carrier mediums for students is provided by teachers’ multimedia courseware and the other is the written teaching materials. For college students to study English in a fragmented manner, the English language information dissemination and the carrier must be diverse and portable [10]. It is important to mention that student self-management and self-selection are essential in the process of students’ English fragmentation learning. However, improving the efficiency of alternative carriers and increasing students’ interest in English learning are difficult goals to achieve solely through student management and self-selection. The conventional oriented function of college English teachers is weakened as a result of the English fragmented learning model, and it is difficult for them to oversee and advise students on time throughout the fragmented learning period of students. Students are more likely to struggle if they lack self-directed learning abilities and awareness or if their motivation to study is low. These include difficulties with perseverance, shifting learning focus, irregular use of pronunciation, grammar, sentence patterns, and other features of language that are challenging to control and understand for both teachers and students [11, 12]. As a result, it is necessary to investigate how to use the convenience of information educational technologies and students’ learning characteristics in the new era against the backdrop of the Internet to innovate traditional English learning techniques and course content. For this reason, the purpose of this paper is to investigate the effect evaluation of fragmented English learning in the mobile information environment. Here, this paper tries to develop a system for fragmented English learning effect evaluation indexing and propose an evaluation model that incorporates BPNN and genetic algorithms. The experiments demonstrate that the strategy described in this work is highly practicable. The main contributions of this paper are listed below: (1)Firstly, this paper investigates fragmented learning features along with puzzle-based teaching(2)Secondly, designs an evaluation model of the English fragmented learning effect based on the BPNN+ genetic algorithm(3)Thirdly, this paper designs a fusion algorithm for English fragmented learning based on the mobile information environment(4)Finally, it experimentally proved the efficiency and reliability of the proposed model. It concludes that this method has a significant impact on the evaluation of fragmented English learning in colleges and universities
The remaining portion of this research study is structured as follows: Section 2 provides comparisons between fragmented learning features and puzzle-based teaching, Section 3 is focused on evaluation model of English fragmented learning effect based on the BPNN+ genetic algorithm, Section 4 is based on the experimental work, and Section 5 concludes this paper.
2. Fragmented Learning Features and Puzzle-Based Teaching
2.1. Features of Fragmented Learning
We are seeing a steady influx of new information expertise in the network as the mobile Internet, information technology, and big data continue to evolve and become more widely available in our range of vision [13]. Additionally, both the content and the format of learning are getting increasingly numerous, and fragmented learning is steadily becoming more respected and widely utilized. Fragmented learning has emerged as a new learning style in the contemporary “Internet+” era, and it is characterized primarily by the characteristics shown in Figure 1.
2.1.1. Gain an Understanding of the Fragmentation of Time and Space
The integrity of the efficient separation is defined by fragmentation. The primary goal is to improve students’ learning abilities. College English learning is better suited to learning on a scattered basis. English word learning is better suited to scattered time learning. Students can help the network platform at any location, any time for English learning, such as in the rest of the train or bus, by studying the fragmentation of time to break the traditional teaching of English learning time constraint. That is to say, pupils’ learning time is more adaptable, which helps to boost their excitement for studying. Fragmented learning will be the primary method of learning in the future, particularly with the widespread use of mobile communications systems, which allows for the fragmentation study of English. College students in the conventional learning mode spend a lot of time in the library or the classroom reading books or articles to gain knowledge and information. Even though the curriculums are largely independent of each other in conventional learning, the entire logic connected with the greater duration, knowledge, and information obtained by college students is relatively comprehensive. Detailed consideration is a term used to describe traditional learning approaches. College students’ learning styles in the mobile Internet era, on the other hand, demonstrate fragmented learning. The speed of life for college students has quickened substantially in recent years, making it difficult to carve out time to read books or articles. Its learning time is largely caused by other things while waiting for the vehicle, starting riding, recess, bedtime, and other fragmented time ranging from a few minutes to large numbers of minutes. Curriculums are also fragmented in the mobile Internet environment. Because of the fragmented learning time provided by the mobile Internet, modern college students’ English learning takes on a fragmented form.
2.1.2. Gain an Understanding of the Fragmentation of Space
Flexible learning in space is possible for students who have fragmented learning. Students can learn English anywhere at any time, with no time or space constraints [14]. Furthermore, with the help of the network platform, students and teachers can interact and answer students’ questions, which provides certain critical information in stimulating students’ interest in learning. In the age of big data fragmentation, the features of mobile learning, which is based on mobile communication technology, wireless network technology, and mobile terminal devices, are expanding in terms of time and space. The quick development of information and communication technology and its application to teaching reform enhances the productivity of teaching reform and provides technical support for teaching reform. Because of the widespread use of mobile Internet among college students, their reading habits have shifted from fixed to jump reading. College students handle books carefully in the conventional way of reading, and their reading materials are fairly fixed. College students’ studies do not allow them to move around between different books; they must stick to a set of readings. Jump reading is when you use a computer to browse the Internet. Because of the use of hyperlinks, college students’ reading is no longer fixed in a book or an article, but rather continues to jump on web pages. When browsing the web, each page contains dozens of hyperlinks that can be used to change the content of the reading. The browser’s multilabel mode makes the realization of the jump reading on the computer more efficient and easy in the mobile Internet environment in the reading mode. Cellphones, microblogs, and the WeChat web browser are all jumping on the reading bandwagon. On a smartphone, clicking on the head of a blogger’s microblog will take you to its microblog page; similarly, clicking on the public account in WeChat’s circle of friends will take you to its public account page. Jump reading has become more common as network technology has advanced and smartphones, microblogs, and WeChat have been upgraded to include images, audio, video, and other exciting, attractive, and powerful reading materials.
2.1.3. Dissection of the Material of the Learning Environment
It is understood that learners have acquired discontinuous and fragmented information when learning content has been fractured. A systematic and comprehensive knowledge has been separated into several little knowledge points to suit the needs of learners in fragmented learning. The introduction of the Internet + era has resulted in the widespread usage of various information technologies by people, who have taken on the roles of broadcasters and receivers of information resources. As a result of their dispersion, a large number of disparate information resources exhibit clear fragmentation characteristics [15]. The first of these is the fragmentation of learning content sources available to students. The advancement of society has had a significant impact on individuals’ lives and ways of thinking. Through the use of multimedia platforms, people can express their thoughts and share the knowledge they have gained over time. As a result, the material of fragmented learning can come from everyone in society and can be of any age.
In the second place, there is a fragmentation of the learning content. Currently, many online courses break down a comprehensive body of knowledge into several smaller knowledge areas, allowing students to make the most of their limited learning time. And finally, the fragmentation of the expression of learning content is a problem. Books and other printed materials are no longer the only means by which learning content is communicated, as was the case in the past. Besides microvideos and photographs, we also encounter audio, animations, and other forms of expression in our daily learning, and the format of the learning content exhibits some peculiarities of its own.
2.1.4. The Division of Learning Forms
People’s learning styles have evolved in tandem with the way knowledge is imparted. The first point to mention is the disparity in learning resources. In the past, learning materials were typically printed on paper; however, the trend is shifting toward electronic books, which have begun to appear in people’s peripheral vision as well. The second point to mention is the diversity in learning styles. The majority of past learning occurs in classrooms, where teachers and students interact in person, and as a result, online teaching styles are becoming increasingly enriched. WeChat, QQ, Dingding, and other messaging apps are frequently utilized [16]. Whenever you have a desire to learn anything new, look for the information you require on the Internet and self-study it in your leisure time.
2.2. Puzzle Teaching
Jigsaw is a collaborative learning approach in which each learner in a “house” team focuses on one component of a subject [17]. Students meet with colleagues who have been allotted the same component, and once they have learned the topic, they back to their “house” team and explain it to their classmates. Each pupil in the “house” team acts as a part of the topic’s jigsaw, and when they all work together as a team, they finish the jigsaw puzzle. The functions of jigsaw teaching can be explained in Figure 2.
2.2.1. Give Full Consideration to the Effectiveness of Classroom Teachers and Construct a Reasonable Knowledge Framework from Fragmented Information
The teacher should give full play to the function of traditional classroom teaching positions when implementing jigsaw-based learning. Teachers should also change their classroom teaching methods on time. They can also be used in tandem with other popular English learning apps to help students organize key sentences. New words and cultural knowledge encountered during fragmented learning are used to assist students in developing an English-language fragmented knowledge framework. Summary results can be posted on a network storage cloud disk that is shared by the class and can access the entire class at any time. Scattered knowledge summarized by teachers and students can be dynamically incorporated into the teaching classroom during daily learning and teaching to strengthen the students’ scattered learning of English.
It should be noted that when teachers innovate in the design of college English classroom instruction, they should consider how to motivate students for self-learning, continuous self-learning awareness, and interest in learning and encourage. In addition, teach students the full range of learning skills, thinking, and self-learning skills. The needs of students from various majors, such as science and engineering majors, English majors, and literature and history students, are taken into consideration when designing an interactive, puzzle-based teaching syllabus that meets students’ majors and learning demands. Based on these, the syllabus is adjusted to meet the various learning needs of students from various majors. Improve the management of macrocontrol and evaluation in the context of fragmented learning, strengthen targeted inspections of students’ English application ability and vocabulary comprehension ability, identify the internalization effect of fragmented English knowledge, and place greater emphasis on the connection between classroom knowledge and fractured knowledge, among other things. Improve the efficiency of college English ecological education by a significant margin.
2.2.2. Increase Students’ Ability to Mine Fragmented Knowledge and Make Full Use of the Usefulness of Information-Based Teaching Tools by Enhancing Their Ability to Mine Fragmented Knowledge
College English teachers can focus on helping students think critically. To this end, they effectively use the flexibility and openness of English scattered information in the application of the integration model of Jags-based teaching and English fragmented learning. They thus promote the value of students’ knowledge of materials, mining thinking, and talent. Using fragmented knowledge as an example, it guides students through the process of classifying difficult material in a jigsaw-based learning environment, and it significantly increases the level of student knowledge selection, sorting, integration, and utilization. Because of the variations in situations, the application of the same knowledge point must be modified. Teachers can help students organize their knowledge by arranging the logical relationships of scattered knowledge. This will encourage students in developing their awareness of organizing systematic learning concepts and macrocontrolling the knowledge system. Considering that college English learning is highly random and scattered in nature, teachers should emphasize the need and necessity of developing a knowledge system in jigsaw-based teaching. To reconstruct fragmented information and integrate it into the mainline, the way, form, and content of fragmented learning are all linked together as an important channel of communication. When everyday communication education and life-based design are combined, the grammar and phrases that students acquire from English songs, movies, and other learning resources can be converted into something more useful for them. Teachers can pick from a variety of instructional techniques, including role-playing, teacher-student interaction, group discussion, and a variety of other activities, depending on their students’ requirements.
The development of information-based teaching tools could be used as a supplement to bridge the gap between fragmented collegiate English learning and jigsaw-based teaching. Because today’s countries’ emphasis on science, education, health, and culture is continuing to strengthen, as is the party and government’s call for improved quality of life for all citizens and lifelong learning, the information resources available for English learning are becoming increasingly abundant. The textbook and text can serve as the foundation for English jigsaw teaching. We can then guide students to gradually accumulate the knowledge points necessary for college English fragmented learning to cultivate students’ comprehensive English application quality and ability by starting from the textbook and text. Teaching English fragmented learning and jigsaw-based teaching are combined in the practical application of the integration model. Teachers set up different types of English knowledge application modules according to actual needs, such as writing and grammar. They also set up corresponding resource supplements based on a database of jigsaw pieces, using a targeted integrated methodology, which incorporates jigsaw-based instruction and English fragmented learning. On the other hand, it can be utilized in conjunction with the features of college English public basic courses to improve students’ understanding of world cultural and humanistic knowledge. Jigsaw-based learning can be applied to teachers who develop general knowledge modules for critical thinking skills based on students’ English learning outcomes and progress. This research also yielded cross-cultural communication ability modules, listening and speaking teaching modules, vocabulary modules, and cultural background knowledge from both Chinese and Western cultures. Students are then presented with modules through the use of microlectures, flipped classrooms, and other methods to enrich and strengthen their English fragmented learning.
2.2.3. With the Assistance of Good Software for English Fragmented Learning, Students Can Significantly Boost Their Ability to Self-Improve through Practice
Mobile fragmented learning has a significant impact on college students’ English proficiency, particularly in the areas of listening and speaking skill development, as well as vocabulary acquisition. Teachers should pay close attention to students’ selection of English fragmentation application carriers when using the jigsaw-based teaching model to guide students’ learning. Following that, they should examine the students’ selection criteria and preferences. Finally, use this information to learn and draw lessons from the objects chosen by the students. It is important to note that each borrowed item has its characteristics, such as English software memory crew including scallop words and Baici chop, BBC 6 minute English, and travel around the United States, which supports video and provides listening and speaking exercises. Besides, IV and VI grade exams are for students and other concerns. Teachers should consider the benefits of jigsaw-based teaching and English fragment learning in their instruction, among other things. This is in addition to the various learning needs of each student when learning and learning from students.
3. Evaluation Model of English Fragmented Learning Effect Based on BPNN+ Genetic Algorithm
There are numerous theories for evaluating the English fragmented learning effect [18]. Figure 3 depicts the construction of an evaluation index system based on relevant theories. The fragmented English learning ability constructed in this paper is based on after class activity index, usual performance index, classroom engagement focus, knowledge interaction index, class focus index, and study duration index.
Backpropagation neural networks (BPNNs) are multilayer feed-forward networks that are trained using the error back-propagation technique. Using this type of network, it is possible to learn the mapping relationship of a large number of input and output patterns on its own, without having to know the mapping connection beforehand. Backpropagation is used to continuously modify the thresholds and weights of the network to reduce the network error at a minimum level. Figure 4 depicts the BPNN conceptual framework.
The formula used to compute the output layer using the input layer as a parameter is as follows. where is the output and is the output of the input layer. where is the weight and is the corresponding threshold value.
The loss function is
Hence, the sum loss function is
Natural selection and evolution are simulated by the genetic algorithm, which is a random search optimization approach that models biological heredity and evolution in nature. This idea is introduced into the encoded tandem population generated by optimizing parameters and then screened through selection, crossover, and mutation to eliminate individuals who have high adaptability. Those with poor physical fitness are weeded out. The individuals who survive inherit the advantages of the preceding generation and are much better than the individuals who preceded them. Essentially, the principle of genetic algorithms is to repeat these processes indefinitely until the requirements are satisfied.
There are three key elements to the fusion method presented in this paper: genetic algorithm optimization, BPNN structure determination, and NN prediction. Genetic algorithm optimization is the first of these aspects. The basic purpose of genetic algorithm optimization is to improve the initial weights and thresholds of BPNN so that the optimized NN can make more accurate predictions. The initial weights and thresholds of the NN are optimized using the genetic algorithm optimization approach. To produce the best prediction output, use individuals to represent the initial weights and thresholds of NN and the prediction error of the initialized NN as the fitness value of individuals to identify the best starting parameters. The flowchart for fusion algorithm can be seen in Figure 5.
The fusion algorithm steps are as follows:
Step 1. Input the initial data of NN.
Step 2. Individual coding, as well as population initialization is required: when using the real number encoding approach, each individual is represented as a real number string, which includes the ownership value and threshold of NN for each individual. A NN with determining weights and thresholds is created in the case of unobstructed NN.
Step 3. The selection of a fitness-related function: It is represented by an individual who represents the initial weights and threshold of the NN, and it is represented by a fitness value whose absolute value indicates the prediction error of the NN started by the individual. wherepredicts the output.
Step 4. . Selection of the probability: probability’s main purpose is to help people make better decisions in the face of uncertainty. It aids objective and data-driven judgment instead of instinctive judgment. The probability can be calculated using equation (10). where is the individual and is the fitness function.
Step 5. . Crossover operation: a crossover is a genetic operator that combines the genetic data of two parents to produce a child. The crossover operation can be calculated using equation (11). In this equation, is a chromosome.
Step 6. . Mutation operation: the mutation operator attempts to introduce new features or gene values into the inhabitants that do not exist in the population pool. The mutation operation can be calculated by using equation (12).
Step 7. . Examine the evolutionary data for consistency: if the evolution has not reached its conclusion, the fitness of the population is recalculated, and the most suitable individual is chosen. The features listed above ensure that the BRF model has a practical use.
4. Experimental Work and Simulations
Several colleges and institutions encourage students to take advantage of the fragmented after-school time to improve their English language skills to meet the needs of social and national strategic development. The goal of this paper is to conduct an empirical examination of students’ fragmented English practice at a Beijing provincial normal university. Then, using the technique outlined in this paper, create a questionnaire survey and assess the impact of practical instruction. A total of 91 undergraduate majors, including 66 nonteaching ones, are offered at the school, which currently includes 20 colleges. It is a provincial upper normal college with clear teacher education characteristics as well as a broad range of nonteaching majors to offer students a well-rounded education.
To begin with, the questionnaire questions are designed to accurately measure the English fragmented learning effect of college students using the six indicators in the indicator system depicted in Figure 3. Each question in the questionnaire has five ABCDE alternatives, with associated scores of 20, 40, and 60. The recovered sample is the sum of the scores for each question in the questionnaire (i.e., 20, 40, and 60). Students from more than 40 different majors across ten different colleges participated in the survey, including the School of Computer Science and Technology, the College of Life Sciences, the College of Political Science and Law, the College of Education, and the College of Physical Education. The questionnaire was distributed and completed for half a month, yielding a total of 1,400 responses. There are 1321 genuine questions, with 400 seniors, 400 sophomores, 400 juniors, and 200 seniors among those who have completed the survey.
For model training, the first 1100 pieces of data from the preprocessed dataset were chosen, while the final 221 pieces of data from the preprocessed dataset were utilized for testing. The normalized data is supplied into the fusion model suggested in this research, which is described in detail below. The observed and expected values for the first 49 sets of data are depicted in Figures 6 and 7, which show the observed and anticipated values for the first 49 sets of data. It can be seen that the model suggested in this research has a decent fitting effect on the observed data.
We also put the fusion algorithm through its paces to see how well it performs. Figure 8 depicts the results of the comparison between BPNN and the genetic algorithm (GEN), respectively. It can be shown that the algorithm presented in this study outperforms the other two algorithms by a large margin. This shows the effectiveness and reliability of the proposed model.
In brief, the algorithm described in this work not only increases students’ learning ability but also improves the model’s general ability and calculating accuracy while reducing mistakes. The former combines the BPNN algorithm’s and the genetic algorithm’s great performance, whereas the latter introduces the crossing rate and mutation rate on this premise so that the optimized method may be better applied to the real-world situation.
5. Conclusions
In the current age of mobile fragmentation, students’ learning is no longer limited to formal classroom learning. As an important new method, the study of mobile fragmentation will have different effects on traditional college English learning. Although fragmented learning makes it easier for college students to study, it also has several disadvantages, including fragmented knowledge being scattered and no structure being developed. Based on these issues, this research work examined the evaluation system for the fragmented learning effect. In addition, the fusion algorithm of the genetic algorithm and the BPNN is investigated in this study. For model training, this paper has chosen 1100 pieces of data from the preprocessed dataset, among which 221 pieces of data from the preprocessed dataset were utilized for testing. The experiments demonstrate that the fused model has superior evaluation capacity in the evaluation of learning effects and that it can accurately anticipate students’ learning effect scores when evaluating learning effects.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The author declares that he has no conflict of interest.