Analysis of the Current Situation of the Research on the Influencing Factors of Online Learning Behavior and Suggestions for Teaching Improvement

Abstract

With the rapid development in online education and the recurrence of COVID-19 around the world, people have temporarily turned to online education. To identify influencing factors of online learning behavior and improve online education, this study used CiteSpace to visually analyze research on influencing factors of online learning behavior on WoS. It discusses the research status, hotspots, and trends. Then, through cluster analysis and literature interpretation, the paper summarizes the types of online learning behavior and the influencing factors of different online learning behaviors from positive and negative dimensions. The findings of this paper are as follows. (1) The number of studies on the influencing factors of online learning behavior has increased in the last decade, especially after the outbreak of COVID-19. The research countries and institutions in this field lack contact and cooperation. (2) Online learning behaviors mainly include online learning engagement behavior, continuous behavior, procrastination behavior, and truancy behavior. (3) Online learning engagement behavior is mainly affected by perceived usefulness, perceived ease of use, individual characteristic differences, and other factors. (4) Online learning continuous behavior is mainly affected by quality, perceived usefulness, learning self-efficacy, and other factors. (5) The influencing factors of online learning procrastination mainly include learning environment, individual characteristics, social support, and pressure. (6) The main influencing factors of online learning truancy behavior are social interaction, participation, and learner control. At the end of this paper, according to the action mode of the influencing factors of online learning behavior, some suggestions for teaching improvement are put forward from the two perspectives of promoting positive online learning behavior and avoiding negative online learning behavior, which can provide a reference for teachers and schools in the future when conducting online education.

1. Introduction

With the rapid development of the Internet, online learning is becoming increasingly popular, especially after the outbreak of COVID-19. Offline education has been fully transformed into online education, and various schools have also begun to carry out online teaching comprehensively. With limited space and direct communication, the traditional offline education classroom has been transformed into online learning with time and space differences. The change in learning methods will inevitably accompany a shift in learning behavior, thus affecting the learning effect. Therefore, learning behaviors will have a different impact when affected by various factors and the extent of the effect. Jianhua examined the relationship between teacher involvement and student performance in online teaching. The results show that teacher involvement influences students’ English learning performance through the linkage mediation of autonomous motivation and positive learning emotions (enjoyment and relief) [1]. Cai-Yu explored the relationship between college students’ online learning self-efficacy, monitoring, willpower, attitude, motivation, strategies, and online learning effectiveness in the context of online education during the COVID-19 epidemic and confirmed that these factors affect online learning effectiveness by influencing online learning strategies and online learning motivation [2]. It can be seen that discussing online learning behavior and influencing factors plays a vital role in improving student’s learning methods and learning performance. Although significant achievements have been made in epidemic prevention and control and schools have also resumed offline teaching, due to repeated epidemics, school teaching may be switched to online courses at any time, and this situation will also become routine in the current period. Moreover, with the development of online education and the popularity of mobile devices among students, online learning systems will become an essential tool for future education [3]. Therefore, it is of great practical significance for teachers and schools to explore the influencing factors of online learning behavior in the post-epidemic era to improve online teaching strategies and methods.

The structure of this paper is as follows. First, the research methods and data sources are introduced. Then, the data are processed to obtain visualization results after CiteSpace processing, and research hotspots, trends, and clusters are analyzed. Finally, the results are discussed and some suggestions for teaching improvement are proposed.

2. Research on Influencing Factors of Online Learning Behavior

The research on the influencing factors of online learning behavior can be divided into three stages. First, from 2001 to 2007, there was little published literature, which was the initial exploration stage of the research on the influencing factors of online learning behavior. At this stage, the research on influencing factors of online learning behavior mainly focused on whether learners were willing to accept or adopt online learning. Early research believed that positive user attitude and acceptance were critical factors of online learning behavior [4]. Saade used an extended version of the technology acceptance model (TAM) to explain the acceptability of the online learning system and provide support for cognitive absorption as a variable affecting TAM variables [5].

Secondly, from 2008 to 2012, the rapid development of Internet computer technology greatly affected our learning methods. In the past five years, the research on the influencing factors of online learning behavior has increased year by year, and deeper influencing factors have been studied. Combining innovation diffusion theory with the technology acceptance model and adding the two research variables of perceived system quality and computer self-efficacy, a new hybrid technology acceptance model is proposed to study students’ behavioral intentions when using online learning course websites [6]. Sung and others studied the influence of five social factors in the online learning environment: social respect (such as timely response), social sharing (such as sharing information or expressing belief), openness (such as giving consent or receiving positive feedback), social identity (such as being named) and intimacy (such as sharing personal experience) on online learning behavior [7]. Artino explored the relationship between several achievement-related discrete emotions (boredom, depression, and enjoyment) and self-regulated learning behavior (refinement and metacognition) in online courses [8].

The third stage is the research on influencing factors of online learning behavior from 2013 to 2022. More methods and models were introduced, more learning backgrounds explored, and better teaching strategies proposed. Hew KF found five factors behind the popularity of massive open online courses (MOOCs) based on a case study of three top MOOCs, sorted by importance: (1) problem-centered learning, with clear explanations; (2) teachers’ accessibility and enthusiasm; (3) active learning; (4) peer interaction; and (5) use helpful course resources. The specific design strategies related to each factor are further discussed to provide helpful guidance for teachers [9]. After the outbreak of COVID-19, research on the influencing factors of online learning behavior, especially mental health, has attracted a lot of attention. Hong conducted confirmatory factor analysis using a structural equation model to explore the invalidity of online learning during the COVID-19 blockade. The results showed that procrastination was negatively correlated with six substructures of self-regulated e-learning: task strategy, emotion regulation, self-evaluation, environmental structure, time management, and help-seeking [10]. Maqableh conducted two online surveys to assess online learning and student satisfaction and identify the positive and negative aspects of online learning. The results showed that during the prevalence of COVID-19, students faced some problems, such as technology, mental health, time management, and the balance between life and education. In addition, over one-third of the surveyed students were unsatisfied with the online learning experience. The most critical factors behind dissatisfaction are distraction and reduced attention, psychological problems, and management problems [11].

In general, the trend of research and development on the influencing factors of online learning behavior is that after the emergence of online learning, the TAM technology acceptance model was used to verify and discuss the research on the influencing factors of online learning acceptance behavior. The subsequent verification of the influencing factors of different online learning behaviors is based on different learning environments and platforms. In recent years, much research has been carried out on the influencing factors of online learning behavior in the context of COVID-19, and more attention has been paid to the psychological health of online learners. The design and optimization of teaching strategies have also been proposed. In the past two years, some scholars have also reviewed and analyzed the research field of e-learning [12,13]. However, these studies lack a summary of online learning behavior types and their influencing factors. Therefore, this research used CiteSpace to visually analyze the research status of the Web of Science (WoS) on the influencing factors of online learning behavior in the last decade to summarize the types of different online learning behaviors and their influencing factors. Finally, we put forward some suggestions and thoughts based on the influencing factors of online learning behavior.

3. Data Collection and Processing

We chose CiteSpace5.8 R3 as the research tool. CiteSpace is a software for document data mining and information visualization based on the Java environment [14]. The advantage of the software is that it is easy to operate. Secondly, it can process a large quantity of data and cluster it. Finally, the cooperation network of countries, institutions and authors, research hotspots and research trends over time in the research field are displayed in the form of images, which helps researchers to quickly grasp the research situation [15,16,17].

We selected literature from the Science Citation Index—Expanded (SCIE), Social Sciences Citation Index (SSCI) and Emerging Sources Citation Index (ESCI), three databases on the Web of Science, as the research objects. The keywords selected were “online learning behavior,” “online study behavior,” “online study,” “online learning,” “influence factor,” “influencing factor,” and “factor,” according to the theme of the article. First, search the keywords separately, select “Article” and “Review” as the types, and select “English” as the language. Then, in the advanced retrieval, the “online learning behavior,” “online study behavior,” “online study,” and “online learning” are combined for OR retrieval, and the “influence factor,” “influencing factor,” and “factor” are combined for OR retrieval. Finally, the two groups are combined for AND retrieval, resulting in 1301 articles. To filter these articles, select the articles from 2013 to 2022 and then choose the type of literature. Then export the documents in plain text file form twice, store them in the folder for data processing, and import them into CiteSpace to delete duplicate data. Finally, 665 valid articles were obtained.

4. Results of Bibliometric Analysis

4.1. Number of Publications, Countries, and Institutions

Figure 1 is obtained by statistics from the collected literature, which shows the number of published studies on influencing factors of online learning behavior in the past decade. From 2013 to 2017, the number of studies developed steadily. After 2017, the number of documents on influencing factors of online learning behavior continued to increase, and the growth rate has accelerated in the past two years, especially in 2021, where the number of studies on influencing factors of online learning behavior increased dramatically.

The number of studies on the influencing factors of online learning behavior has increased dramatically. One of the reasons for the enormous growth is that Internet technology has driven the development of online education. Online learners have a variety of learning channels. The teaching methods of various schools have also changed from traditional classroom teaching to mixed teaching, that is, the combination of online and offline. One of the crucial reasons for the sharp increase in research in 2021 is the impact of COVID-19, which forces teaching to go online. That is also consistent with the statements of Al-Kumaim and Navarro et al. [18,19].

By analyzing the countries that have studied the influencing factors of online learning behavior, Figure 2 was obtained. Among them, China ranked first with 181 published articles, accounting for 27.2% of the total published articles in ten years, the United States ranked second with 76 pieces, accounting for 11.4%, and Saudi Arabia and Malaysia ranked third and fourth with 51 and 42 articles, respectively.

Table 1. Countries studying the influencing factors of online learning (top 10).

Rank	Country	Number	Percentage	Centrality
1	China	181	27.2%	0.08
2	USA	76	11.4%	0.17
3	Saudi Arabia	51	7.7%	0.03
4	Malaysia	42	6.3%	0.06
5	Australia	41	6.2%	0.19
6	South Korea	37	5.6%	0.00
7	Spain	31	4.7%	0.05
8	England	30	4.5%	0.14
9	Turkey	29	4.4%	0.04
10	Iran	28	4.2%	0.10

lists the top ten countries regarding the number of publications and their centrality.

By analyzing research institutions’ publications on influencing factors of online learning behavior, Figure 3 was obtained. National Taiwan Normal University ranked first with 18 published articles, King Saud University ranked second with 14 published articles, and Beijing Normal University ranked third with 10 published articles.

Table 2. Institutions researching influencing factors of online learning (top 5).

Rank	Institution	Number	Centrality
1	National Taiwan Normal University	18	0.01
2	King Saud University	14	0.01
3	Beijing Normal University	10	0.01
4	King Khalid University	8	0.00
	Nanjing Normal University	8	0.00
	University of Malaya	8	0.00
5	Islamic Azad University	7	0.00

lists the top five institutions with the highest number of publications and their centrality.

However, from the perspective of the centrality of research countries and research institutions, these countries and institutions are not closely linked. This indicates that their cooperation is not close, possibly due to different educational methods and concepts in different countries.

4.2. Keyword Co-Occurrence and Cluster Analysis

Keywords can quickly and accurately reflect the theme and focus of a paper, and their frequency in the literature of a specific research field can reflect the topics that researchers pay attention to together in a certain period, that is, the hot topics in the whole research field. Research on keywords helps analyze the burning issues in a specific scientific area. First, the documents in the WoS folder are imported into CiteSpace to remove duplicate samples. Then, due to a large number of pieces, pruning is selected to cut the network to improve the readability of the network. Finally, Figure 4 shows the co-occurrence of keywords in the WoS literature. A node represents a keyword, and the larger the node’s size, the more frequently the keyword appears, indicating that the keyword is a research hotspot in this field to some extent. When two keywords appear at the same time, there is a line. It can be seen from Figure 4 that the high-frequency keywords of WoS research on influencing factors of online learning behavior include online learning, user acceptance, satisfaction, higher education, model, technology acceptance model, education, student, etc. “Centrality” is an indicator to measure the importance of a node in the network. A high centrality value indicates that the more keywords associated with it, the greater the link effect and the higher the importance. Therefore, by comparing the centrality and frequency of keywords, we can find out more accurately and deeply the research hotspots of the influencing factors of online learning behavior and sort out the top 10 keywords in terms of frequency and the top 10 keywords in terms of centrality and centrality, as shown in

Table 3. Top 10 keywords in frequency.

Rank	Keyword	Frequency	Centrality
1	online learning	125	0.03
2	user acceptance	89	0.00
3	satisfaction	88	0.05
4	higher education	83	0.00
5	model	81	0.07
6	technology acceptance model	76	0.15
7	education	74	0.06
8	student	74	0.01
9	adoption	73	0.01
10	technology	69	0.02

and

Table 4. Top 10 keywords in centrality.

Rank	Keyword	Centrality	Frequency
1	performance	0.25	62
2	perceived usefulness	0.20	42
3	antecedent	0.20	9
4	distance education	0.17	23
5	communication	0.17	5
6	intention	0.16	47
7	technology acceptance model	0.15	76
8	structural equation model	0.15	11
9	information technology	0.14	68
10	environment	0.14	31

.

According to the frequency and centrality of the keywords, the research hotspots of online learning behavior influencing factors in WoS literature include online learning, user acceptance, satisfaction, technology acceptance model, higher education, etc. It can be seen from Figure 4 that some keywords are closely related and some are sparsely related. To explore the profound relationship between these keywords, you can use the clustering function of CiteSpace. The clustering results are shown in Figure 5. The modular degree Q in the clustering results is 0.7377, much higher than 0.3. Generally, a clustering modular value (Q value) of >0.3 means that the clustering structure is significant—the contour value S = 0.8851 is higher than 0.7. A clustering average contour value (S value) of >0.5 means that clustering is reasonable, and S > 0.7 that clustering is convincing. As such, the structure of this clustering is remarkable and effective.

A total of 22 clusters have been formed in the clustering process, but not every cluster is effective. These 22 clusters need to be screened. The screening criteria are the number of cluster members and the S value. The number of cluster members should be greater than 10 at first, and then the S value should be greater than 0.7. An S value > 0.7 indicates that the internal compactness of the cluster members is good, the inner members are similar, and the clustering is convincing. Finally, 15 clusters are left after screening. These are clustered according to title, and the labels are as follows: #0 e-learning system, #1 student course selection, #2 learning ineffectiveness, #3 planned behavior, #4 user engagement, #5 extended technology acceptance model, #6 different factor, #7 user experience, #8 e-learning service quality influence, #9 systematic review, #10 influence assessment, #11 academic emotion, #12 cross-sectional study, #13 digital transformation, and #14 learning motivating factor.

Through the analysis of cluster members, the clustering of online learning behavior influencing factors can be further divided into two groups. The first group is different online learning behaviors, including #0, #1, #2, #3, #4, #7, and #11. Online learning behavior includes positive online learning behavior and negative online learning behavior. Positive online learning behavior contains two types: online learning engagement and continuous learning [20]. The engagement behavior of online learning users can be divided into adopting or accepting online learning [21] and the degree of online learning investment [22]. Negative online learning behaviors include online learning procrastination [23] and truancy [24].

The second group is the influencing factors, including #5, #6, #8, #9, #10, #12, #13, and #14. Online learning behaviors have various influencing factors, including the influencing factors of online learning satisfaction.

Table 5. Summary of influencing factors of different online learning behaviors.

Online Learning Behavior			Influence Factors
Positive online learning behavior	Online learning engagement behavior	Accepting online learning	perceived usefulness, perceived ease of use, subjective norms, and quality (system quality, information quality, content quality) [25,26].
		Investing degree in online learning	self-efficacy and learning emotion, differences in individual characteristics (gender, personality, etc.), family support, meta cognitive self-regulation [27,28,29].
	Continuous learning behavior		quality (system quality, curriculum quality and service quality), curiosity, perceived convenience, perceived enjoyment, performance expectation, online learning self-efficacy, effort expectation, social impact [30,31,32,33].
Negative online learning behavior	Online learning procrastination behavior		learning environment, differences in individual characteristics (gender, personality, etc.), social support and pressure [34,35,36].
	Online learning truancy behavior		social interaction and participation [24], Learner control [37]

summarizes the influencing factors of online learning behaviors.

Online learning engagement behavior refers to the extent to which learners are willing to adopt or accept online learning and invest time in online learning. In addition to the influence of COVID-19, the use of information technology by schools is on the rise. Mobile devices and the Internet are widely spread among students. Therefore, online learning will become an essential tool for students. The most basic is the willingness to accept and adopt online learning and the degree of investment in online learning [38,39]. Continuous learning behavior refers to the willingness of online learners to continue to use and use online learning systems. Schools will adopt online learning, and learners will learn through some online learning systems, such as MOOCs. The most important thing for these online learning systems is to enable users to continue using them [31]. Procrastination in online learning refers to online learners’ procrastination, slackness, and anxiety when they are not under face-to-face supervision. Under the influence of COVID-19 closed management, the psychological state of learners will also be greatly affected, resulting in online learning delays [40]. Online learning truancy is more serious. Some learners will not participate in the course directly, and some will carry out activities unrelated to learning without opening the camera, such as leaving their seats, playing games, browsing other web pages, etc.

In addition to some common factors, such as perceived usefulness, perceived ease of use, quality, and other factors, some scholars have also done corresponding research on some unique factors—for example, differences in individual characteristics, gender, personality, age, etc. In addition, some factors need to be paid attention to. For instance, Luiz Antonio Joia verified that teachers’ digital ability on the technical platform and metacognitive support in the digital environment are important factors for online courses to achieve their teaching goals successfully [41]. Tsai discussed the influence of metacognition on online learning interest and persistence [42]. Metacognition refers to implicit or explicit information generated by individuals on their cognition and coping strategies [43]. The results show that improving learners’ metacognition helps enhance online learning interest and continuous learning through MOOC.

4.3. Empirical Analysis: Key Literature Analysis

The co-citation analysis helps us understand the critical literature in this research field. When another piece at the same time cites two articles, there is a co-citation relationship between the two pieces. The key literature analysis further explains how different online learning behaviors are affected by factors. Figure 6 shows the cited network of the literature on the influencing factors of online learning behavior. Literature by Al-Fraihat D (2020) has the highest cited frequency, totaling 31 times.

Table 6. Co-citation frequency and centrality of literature on influencing factors of online learning behavior.

Rank	Frequency	Reference	Centrality
1	31	Evaluating E-learning systems success: An empirical study [44]	0.06
2	26	E-learning success determinants: Brazilian empirical study [45]	0.09
3	23	Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic [46]	0.03
4	23	Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analyzing commonly used external factors [47]	0.29
5	22	Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model [48]	0.03

lists the top five pieces of literature cited most frequently and their centrality.

Among the five highly cited papers, the first analyzes the critical success factors of accepting online learning and online learning satisfaction through the structural equation model. The factors involved include technical system quality, information quality, service quality, support system quality, learner quality, teacher quality, and perceived usefulness. Perceived usefulness is the critical determinant of acceptance of online learning. If students think the e-learning system is helpful, they are more likely to use it. Another factor, technical system quality, helps overall satisfaction and perception of system effectiveness. However, there is no significant impact on the use of online learning systems because students still use the specific e-learning platform adopted by the school, regardless of its quality. Secondly, the quality of the education system, such as the existence and interactivity of communication tools, the diversity of learning styles, and the provision of evaluation materials (such as tests and assignments) to students, have a significant impact on the use of e-learning systems. The aspects related to teacher quality are closely related to students’ perceived satisfaction and usefulness of the system but have nothing to do with their use. The second literature adopts the structural equation model and takes collaboration quality, information quality, and user-perceived satisfaction as the influencing factors of e-learning use behavior. And the driving factors of user-perceived satisfaction are information quality, system quality, teachers’ attitude towards e-learning, diversity of evaluation, and learners’ perception of interaction with others.

The third piece of literature is an interview on subject analysis through NVivo software, which mainly analyzes the key influencing factors of using online learning during the popularity of COVID-19. The key factors that need to be considered are technical, e-learning system quality, trust, self-efficacy, and cultural factors. Among the quality factors of the e-learning system, perceived ease of use has a significant relationship with the use of online learning. First, if students find the plan challenging, they may lose confidence in it. Secondly, it meets the needs of students and can also be effectively adopted and used. Trust factors include system protection, information privacy, and system reliability. One of the vital trust factors for students to increase their use of e-learning systems is to provide effective and transparent e-learning activities through e-learning system projects and ensure security and freedom from threats. Self-efficacy is one of the core factors in deciding whether to adopt an e-learning system. To increase the adoption of e-learning systems, it is necessary to ensure that students have high self-efficacy to meet the expected functions. Otherwise, if students have low self-efficacy, it is not easy to achieve learning activities through e-learning systems. Cultural factors are also essential factors. One of the factors that should be implemented to increase the use of e-learning systems is to improve the ICT literacy and skills of e-learning users. Social interaction can also increase their participation.

The fourth and fifth articles both adopt the technology acceptance model. The former analyzes the factors of adopting e-learning, mainly involving self-efficacy, enjoyment, experience, computer anxiety, and subjective norms. The latter is to analyze the influencing factors of the continuous use of online learning. The main factors involved include perceived usefulness and attitude, which are crucial to the constant intention of using MOOCs. In addition, perceived ease of use, task technology suitability, reputation, social cognition, and social impact play an important role in predicting persistent intention.

5. Conclusions and Suggestions

5.1. Conclusions

In this paper, CiteSpace was used to visually analyze the research status of the influencing factors of online learning behavior. Through the study of the results of the visual analysis and the content of key literature, the following conclusions are drawn.

(1) The research on the influencing factors of online learning behavior has received significant attention in the context of COVID-19, especially in China and the United States. However, research institutions in various countries are not closely linked and lack cooperation.

(2) Research hotspots of influencing factors of online learning behavior mainly include online learning user participation, satisfaction, online learning acceptance, technology acceptance model, higher education, etc.

(3) Accepting online learning in online learning participation behavior is mainly affected by perceived usefulness, perceived ease of use, interaction, subjective norms and quality factors. Online learning investment is mainly affected by self-efficacy, learning emotion, individual characteristics (gender, personality, etc.), and meta-cognitive self-regulation.

(4) Continuous learning behavior is mainly affected by system quality, curriculum quality and service quality, curiosity, perceived convenience, enjoyment, performance expectation, online learning self-efficacy, effort expectation, and social impact.

(5) Online learning procrastination is mainly affected by the learning environment, individual characteristics (gender, personality, etc.), social support, and pressure factors.

(6) Online learning truancy behavior is mainly affected by social interaction participation and learner control factors.

5.2. Suggestions

After the analysis above, online learning behavior is divided into positive and negative learning behavior. Given these two learning behaviors and their influencing factors, some suggestions for teaching improvement are proposed.

5.2.1. Suggestions for Positive Online Learning Behavior

As for the influencing factors of active online learning behavior, perceived usefulness and perceived ease of use are the two most frequent influencing factors. These two factors play a significant role and have a positive impact on online learning behavior [25]. To promote active online learning behavior, teachers must fully consider the usefulness and ease of use of online learning from different perspectives. Furthermore, the influence of individual differences requires teachers to teach students according to their aptitude. In order to promote active online learning behavior, three suggestions are proposed:

(1) Teachers should pay attention to improving students’ information literacy awareness, knowledge and skills, and make them familiar with the functions of online learning system. Online learning system is easier to operate, which can improve the frequency of learners’ participation and continuous use. The functions of each online learning platform are different, and there are also differences in the video, shared screen, and other aspects. Different platforms used by each teacher will also cause inconvenience and confusion to students. Because of the differences in students’ learning abilities, their understanding of the functions of each online learning platform is also different, which significantly affects their willingness and efficiency to use the learning platform. Teachers can arrange online courses on information literacy to acquire practical knowledge in this field and gain familiar with and use some commonly used information learning tools.

(2) The online learning mode selected by teachers should meet the needs of students, consider their online learning experience, and enhance the perceived usefulness of online learning. Compared with the recorded class, most students think the live class cannot bring good results. This may be related to the difference in network speed and time between the two sides and the rhythm of teachers’ lectures. The recorded and broadcast courses also may lead to a high dropout rate. This requires teachers to understand the learners’ personality characteristics fully, learning motivation, learning barriers, and other information when selecting teaching methods. The arrangement of curriculum content should reflect the mixed characteristics to achieve effective convergence and integration of live broadcast and recorded broadcast curriculum content.

(3) Encourage learners to take the initiative, optimize online learning evaluation, and improve learners’ online learning expectations and self-efficacy. The efficient development of online learning evaluation must be integrated with online teachers’ organizational management, monitoring, and emotional motivation. Teachers can develop some incentive mechanisms in the interactive activities, timely encourage with words, or increase the usual performance scores to reward and enhance learners’ willingness to learn online and improve their sense of achievement. Learners actively participating in thematic activities will naturally get a better learning experience.

5.2.2. Suggestions for Negative Online Learning Behavior

In order to avoid negative online learning behavior, in addition to some personal characteristics, for example, girls are more likely to have online learning ineffectiveness than boys [34], we should also consider the impact of the learning environment and how to improve online learning interaction and learner control. In the context of online learning, there is a positive correlation between students’ pressure and learning procrastination. Social support inhibits procrastination through psychological resilience [35]. Social interaction and participation can help reduce the dropout rate [24]. Based on the influence of these factors, three teaching suggestions are proposed.

(1) Flexible teaching, enriching course content, and strengthening interaction. Change the online teaching form, not necessarily copy the offline teaching arrangement and time, and flexibly adjust the online learning mode according to the status of most students. At the same time, it enriches the course content and strengthens interaction. You can take turns answering questions or arrange for students to share and interact. Online teaching content should be more attractive than offline content to focus students’ attention. Because online learning has no space restrictions or teacher supervision, students are easily distracted and do other things. Teachers can investigate students’ preferred online teaching forms and interaction methods in advance, whether one-way output or multidirectional discussion, and arrange them flexibly. Each class adopts different forms to improve students’ learning input and interaction with teachers.

(2) Flexible sign in and sign out, spot-check whether they are online, and always pay attention to students’ online learning state and psychological state. Because of the time and space gap, teachers can not directly see the state of students and cannot make timely adjustments. Without teachers’ direct supervision, students often relax or have vague roles and do not know what to do. There is a time–space difference in the interactive feedback of online learning, which affects the classroom effect and efficiency. Given environmental factors and the general outbreak of the epidemic, most students are isolated in the dormitories, and many dormitories are noisy and easy to slack off. Therefore, teachers need to pay attention to students’ online learning status, whether they answer questions, whether there is interaction, etc. And because it is isolated in the dormitory, the narrow space and activity range will make many students have anxiety and anxiety psychological state, and timely communication with students to relieve these psychological states and ensure the humanization of online learning.

(3) Fully understand the learning performance of learners and accurately implement teacher support. Teachers are not only the designers of teaching activities but also the leaders, supporters, and supervisors of teaching activities. In the pre-class link, teachers can release teaching objectives in timely fashion, put forward guiding questions for the teaching content of each class, and require students to preview before class, which can improve students’ enthusiasm for learning, and assign homework after class according to the teaching content. It is suggested that teachers throw out exploratory questions in all aspects of teaching and try to keep the tasks within half an hour when assigning self-study tasks before class to improve the quality of students’ self-study and teachers’ teaching efficiency. Secondly, teachers should do a good job in emotional support, such as acceptance and persuasion, encouragement and praise, which can alleviate or eliminate students’ anxiety, loneliness, and negative fatigue in online learning and improve students’ learning motivation and enthusiasm. Through professional support such as question-and-answer feedback and technical guidance, students can solve the difficulties and obstacles encountered in online learning and enhance their confidence and satisfaction in continuous learning.

6. Contribution and Future Research

This paper presents a visual analysis of the research status quo on influencing factors of online learning behavior, discusses the research hotspots and research trends of the influencing factors of online learning behavior, summarizes the types and influencing factors of online learning behavior, and puts forward some suggestions for teachers to improve teaching, so as to build a knowledge analysis framework of influencing factors of online behavior, as shown in Figure 7, which provides valuable reference for future exploration.

The research contribution of this paper is to use the method of combing CiteSpace clustering analysis and literature interpretation, summarizing different types of online learning behaviors from the positive and negative dimensions, and further refining the influencing factors of different online learning behaviors through in-depth literature mining. Finally, according to the factors that affect online learning behavior, this paper puts forward suggestions for teaching improvement in the post-epidemic education era from the perspective of teachers. It provides strategies for schools and teachers to turn to online education after the epidemic.

In this study, some aspects need to be improved in summarizing online learning behavior and its influencing factors. In future research, scholars can use text data mining and content analysis to process first-hand materials reflecting online learning and identify potential influencing factors. For scholars in the field of education research, some new research methods can be adopted in future research on the influencing factors of online learning behavior. Traditional research usually adopts a technology acceptance model or structural equation model. However, this approach also has some limitations. For example, most studies use self-reporting to measure usage, ignoring actual usage. In the future, neural network algorithms [49] or machine learning models [50] can be used to directly process the data of users’ actual use of online learning systems and combined with big-data methods to explore deeper influencing factors.