key: cord-1008764-3hbdw341 authors: Chen, Tinggui; Peng, Lijuan; Yin, Xiaohua; Rong, Jingtao; Yang, Jianjun; Cong, Guodong title: Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic date: 2020-07-07 journal: Healthcare (Basel) DOI: 10.3390/healthcare8030200 sha: 8cf9edf4d841feea23f9c00028273cfd79daceb6 doc_id: 1008764 cord_uid: 3hbdw341 The outbreak of Corona Virus Disease 2019 (COVID-19) in various countries at the end of last year has transferred traditional face-to-face teaching to online education platforms, which directly affects the quality of education. Taking user satisfaction on online education platforms in China as the research object, this paper uses a questionnaire survey and web crawler to collect experience data of online and offline users, constructs a customer satisfaction index system by analyzing emotion and the existing literature for quantitative analysis, and builds aback propagation (BP) neural network model to forecast user satisfaction. The conclusion shows that users’ personal factors have no direct influence on user satisfaction, while platform availability has the greatest influence on user satisfaction. Finally, suggestions on improving the online education platform are given to escalate the level of online education during the COVID-19 pandemic, so as to promote the reform of information-based education. The global spread of COVID-19 resulted in the suspension of classes for more than 850 million students worldwide, disrupting the original teaching plans of schools in these countries and regions. Soon later, many countries started to offer online teaching to students by Zoom, Skype, FaceTime, etc. in order to promote online education and restore the normal teaching order, and on 6 February 2020, the Ministry of Education of the People's Republic of China announced to vigorously support information-based education and teaching, and enhance the platform's service capacity to support online teaching. In response to the outbreak of the epidemic, the online classroom has become a necessary way to maintain normal teaching order. Ding Ding, Fanya, and other office meeting software tools in China deliver services such as an online classroom and online teaching. However, these online education platforms have problems such as system jams and the inability to replay live broadcasts. It is necessary to study whether these network education platforms can meet the needs of teachers and students, whether the network teaching can complete the teaching tasks with high quality, whether the network education can become an effective means of special period education, and put forward suggestions to promote the development of network education according to the research results. assessment framework, helping MOOC organizations make a series of measures for monitoring and improving. Hrastinski [11] put forward a theory in his research: if we wanted to enhance online learning, we needed to enhance online learner participation. Miri and Gizell [12] showed in their research the need for rethinking the way conventional online ethics courses are developed and delivered; encouraging students to build confidence in learning from distance, engaging them in online active and interactive experiences. Anderson et al. [13] pointed out that healthcare professionals could share their expertise through online education and incorporate this teaching into their annual learning. Kamali and Kianmehr [14] pointed out that the public's interest in online education was growing, while educational institutions' interest in online education was going down. They held the view that in order to change the negative effect of online education, it was necessary to provide students with a suitable network environment, and discussed online education from the perspective of students. Alcorn et al. [15] evaluated satisfaction of online education from the number of class participants, the participation rate of homework, the completion rate and the improvement of grades. Asarbakhsh and Sars [16] believed that the broken-down system, failed video connection or unusable use affected user satisfaction. From the perspective of users and designs, David and Glore [17] pointed out visual content was quite important to improve participation and interaction of users. Based on the technology acceptance model and taking 172 online learning users as the objects, Roca et al. [18] analyzed online learning satisfaction. The results showed that the user's online learning satisfaction was mainly determined by the user's perception of the usefulness and quality of the course, the quality of the platform and the website service and the degree of expected achievement. Lin and Wang [19] believed that students' satisfaction would be influenced by the difference of technology, the characteristics of teachers, students and courses. Panchenko [20] held the view that the MOOC teaching mode could develop teachers' careers, improve teaching skills, and enable teachers to consider and examine their teaching activities from more perspectives. Kravvaris and Kermanidis [21] testified that social networks contributed to MOOC development. The literatures [22, 23] found that learners' autonomy played an important role in learning through the empirical study of MOOC. Through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), Parra-González and Segura-Robles [24] concluded that "game" was regarded as a motivating factor in the educational process, which could promote students to participate in the learning process more actively. According to the above research results, many scholars study online education and establish many evaluation models. However, in the process of carrying out online education during this epidemic, many new problems arise in the new form of online education. This requires that new factors affecting user satisfaction be taken into account in the study. Based on this, this paper collects online user comment data to obtain the new factors affecting user satisfaction and establishes an evaluation system that can better reflect the satisfaction of online education platforms during the epidemic. Customer satisfaction is the state of pleasure or disappointment formed by the comparison of the perceived effect of a product or service with the expected value. Previous scholars and experts have conducted many studies on customer satisfaction and established models, which can be divided into macro-and micro-models. Macro model: since the 1990s, many countries have carried out a national customer satisfaction index measurement work, regarding customer satisfaction index as a macroeconomic indicator to measure the customer satisfaction degree of a product or service. For instance, in 1989, Fornell [25] put forward the customer satisfaction index (CSI) by considering customer expectation, post-purchase perception and purchase value. Under the guidance of professor Fornell, based on the annual customer survey data of more than 100 enterprises over 32 industries, a Swedish Customer Satisfaction Barometer (SCSB) was constructed by using the Fornell model and calculation method. Under the guidance of Anderson and Fornell [26] , America published the American Customer Satisfaction Index (ACSI) on the basis of the SCSB. The ACSI added perceived quality to measure the reliability of a product or service, as well as customer satisfaction. In 1992, Germany constructed the Deutche Kundenbarometer (DK) model, which consisted of 31 industries [27] . The European Union constructed the European Customer Satisfaction Index (ECSI) by adopting a comparative advantage over a wide variety of countries. This model omitted customer complaints but added company image, dividing perceived quality into perceived hardware quality and perceived software quality [28] . Micro model: the measurement model of customer satisfaction in micro fields is abundant. For instance, Tversky [29] put forward a variation model in 1969. Oliver [30] established a general model for measuring subjective inconsistencies in 1980. Sasser et al [31] proposed customer model with service level. Parasuram et al. [32] created the SERVQUAL scale to evaluate service quality. They divided the factors that determine service quality into five categories: reliability, responsiveness, assurance, empathy and tangibility. From the above research outcomes, many scholars and institutions of various counties study the satisfaction evaluation system and establish many models. However, previous studies did not consider the impact of public health emergencies. On the basis of full reference to previous studies, this paper, in the context of the COVID-19 pandemic, optimizes the indicators used in previous studies and establishes a satisfaction evaluation model by considering the impact of public health emergencies. In this paper, data are obtained through a questionnaire survey and web crawler. The online data obtained by web crawler technology are trustable and objective without restriction. Therefore, this paper uses the data obtained by web crawler to make a macro analysis of the user experience on the current online network teaching platform, and finally summarizes the main factors affecting the user experience satisfaction. Although the traditional questionnaire has many limitations, the obtained data are more targeted, diverse and abundant, which can test the ranking of impact factors summarized by the crawler data. Therefore, this paper combines the two methods to comprehensively acquire online and offline experience data of users. At present, there are a large number of online teaching platforms in China, such as MOOC, and Tencent Class. We are unable to assess all platforms. Thus, it is necessary to select representative platforms to evaluate. In this study, data samples of online education platforms were selected on ASO100 (a big data service platform for analyzing the App Store, Qimai, Beijing, China), and the ranking of the education category (updated on 17 April 2020) was screened based on the download volume, comments and popularity of the platform as the representative measurement criteria of the platform. The platform ranking results are presented in Table 1 . From the comments on ASO100, it is difficult to determine all the factors that affect an online teaching platform. To obtain a more targeted evaluation of user experience, this study adopted a questionnaire survey, whose targets were primary school, middle school, high school, university, and postgraduate students. By sorting and analyzing relevant literature, we designed the questionnaire with three parts, as demonstrated in Table 2 . In the second part, user experience satisfaction questions used a Likert scale. The scoring system was 1-5, where 5 represented strong agreement and 1 represented strong disagreement. The higher the score was, the more strongly the respondents agreed with the statement. During the epidemic period, the questionnaire tool named Wenjuanxing was used to collect information. After investigation, a total of 800 questionnaires were received, with 712remainingafter the removal of invalid questionnaires. After data collection, 712 questionnaires were coded and entered into SPSS statistical software (SPSS Statistics 25.0 HF001 IF007, IBM, Armonk, NY, USA) to perform descriptive analysis, and reliability and validity analysis. The reliability test, which measures data reliability, is used to test the stability and consistency of questionnaire data. In this study, Cronbach's α was used to test the internal consistency of the questionnaire data, whose coefficient was between 0 and 1. In general, a coefficient greater than 0.7 indicates that the questionnaire can passes the internal consistency test. In contrast, a coefficient less than 0.7 indicates that some questions must be discarded. The reliability test results are presented in Table 3 . In this questionnaire, six Cronbach's α coefficients were all greater than 0.7, indicating that the internal reliability of each first-level indicator of the questionnaire was high. The validity test can be divided into content validity and structure validity. The questions in this questionnaire scale used relevant literature for reference to ensure high content validity. The structure validity passed the KMO (Kaiser-Meyer-Olkin) test and the Bartlett test. Generally, when KMO is greater than 0.5, and the significance level of the Bartlett test meets the significance requirement of a two-tailed test, it is considered that the questionnaire passes the validity test. The results of the validity test are presented in Table 4 . It can be seen that the test values of the KMO and Bartlett test of the six first-level indicators in the questionnaire all met the requirements, indicating that they passed the validity test. Referring to Bawa's method of data analysis which includes descriptive statistics, analysis of variance (ANOVA) and T-tests [33] , this paper analyzes the questionnaire data as follows: In this questionnaire survey, 26.6% of respondents were male while 73.4% of respondents were female. The majority of the participants were middle and high school students, junior college students, undergraduate students, and graduate students. Primary school students may produce invalid questionnaires due to their difficulties in text comprehension. According to the survey on the terminal types of online teaching platforms used by participants, mobile phones accounted for 84.62%, followed by laptop computers and tablet computers. The key questions in the questionnaire were analyzed to understand the data characteristics, as illustrated in Figure 1 . As can be seen from Figure 1 , during the epidemic period, teachers mainly taught online using Ding Ding and self-established social groups (such as QQ group and WeChat group). As work management software, Ding Ding added on online teaching function in a timely manner in view of the epidemic. The results demonstrate that more than 50% of users continued using Ding Ding as an online learning platform after the epidemic ended. As can be seen from Figure 1 , during the epidemic period, teachers mainly taught online using Ding Ding and self-established social groups (such as QQ group and WeChat group). As work management software, Ding Ding added on online teaching function in a timely manner in view of the epidemic. The results demonstrate that more than 50% of users continued using Ding Ding as an online learning platform after the epidemic ended. As can be seen from Figure 2 , most of the online teaching platforms can provide five learning modes and eight online interactive modes, which can effectively meet the existing teaching needs and provide feedback at any time. The two main teaching methods are online live broadcasting and existing courses on the platform. The MOOC platform contains rich teaching resources and has thus been favored and used as an online education platform for a long time. As can be seen from Figure 1 , during the epidemic period, teachers mainly taught online using Ding Ding and self-established social groups (such as QQ group and WeChat group). As work management software, Ding Ding added on online teaching function in a timely manner in view of the epidemic. The results demonstrate that more than 50% of users continued using Ding Ding as an online learning platform after the epidemic ended. As can be seen from Figure 2 , most of the online teaching platforms can provide five learning modes and eight online interactive modes, which can effectively meet the existing teaching needs and provide feedback at any time. The two main teaching methods are online live broadcasting and existing courses on the platform. The MOOC platform contains rich teaching resources and has thus been favored and used as an online education platform for a long time. As can be seen from Figure 3 , there are 11 types of common problems regarding online teaching and courses that can be attributed to the problems mentioned in online comments, such as "network congestion", "live interactive stuck" and "unable to log in personal information". Therefore, to 0% 10% 20% 30% 40% 50% 60% 70% 80% As can be seen from Figure 3 , there are 11 types of common problems regarding online teaching and courses that can be attributed to the problems mentioned in online comments, such as "network congestion", "live interactive stuck" and "unable to log in personal information". Therefore, to improve these issues, the five online teaching platforms can begin by addressing their live broadcast functions, system quality, and capacity enhancement. improve these issues, the five online teaching platforms can begin by addressing their live broadcast functions, system quality, and capacity enhancement. The relationship between tutoring work and student emotions is of great significance to the cognitive re-evaluation of students. All comments were divided into different topics through data processing, and the key content in the comments was observed. In this study, the ROST (Regional The relationship between tutoring work and student emotions is of great significance to the cognitive re-evaluation of students. All comments were divided into different topics through data processing, and the key content in the comments was observed. In this study, the ROST (Regional Operations Support Team) [34] software was used to divide the emotional tendencies into three critical sets: positive, neutral and negative. Because the emotion dictionary of ROST is limited, the NLPIR-Parser(Natural Language Processing and Information Retrieval) [35] was used to score the emotion, which can be divided into the total score, positive score and negative score, to identify the platforms with better user experience. Using word frequency analysis, the advantages and disadvantages of platforms were extracted based on good or poor user experience. Based on ROST CM5.8.0 It was shown that those students who carried out activities related to their emotions and the improvement of coexistence in tutoring had a greater cognitive reevaluation. Therefore, this paper makes an emotional analysis of user comments [36] . Based on the analysis results of ROST, this study integrated the positive, neutral and negative comments of the five platforms, as illustrated in Table 5 . Analysis of the positive, neutral, and negative comments indicated that Ding Ding and Tencent Class had more positive comments than negative comments, while Tencent Meetings, Chaoxing Learning, and Chinese MOOC demonstrated the opposite trend. In particular, Chaoxing Learning, and Chinese MOOC had more negative comments than positive comments. A semantic network expresses the structure of human knowledge through the network. It is composed of nodes and arcs among the nodes. Nodes stand for concepts (e.g., events, things), while arcs represent the relationship between them. Mathematically, a semantic network is a directed graph, corresponding to a logical representation. In this study, the semantic network relationship diagrams of the five platforms were obtained through ROST analysis. A partial image of the MOOC semantic network relationship is presented in Figure 4 . A semantic network expresses the structure of human knowledge through the network. It is composed of nodes and arcs among the nodes. Nodes stand for concepts (e.g., events, things), while arcs represent the relationship between them. Mathematically, a semantic network is a directed graph, corresponding to a logical representation. In this study, the semantic network relationship diagrams of the five platforms were obtained through ROST analysis. A partial image of the MOOC semantic network relationship is presented in Figure 4 . (1) According to the semantic network relationship graph of Ding Ding, we use "study" as a node, and keywords that are close to this node are "epidemic" and "convenience". This is because during the epidemic, Ding Ding expanded the educational function on its platform, enabling colleges to use it as an online teaching platform. Taking "software" as a node, a closer keyword is "live broad cast", which also reflects that the teaching method of Ding Ding is mainly a live broadcast rather than students watching videos on their own. This method also increases the interactivity of online teaching, better mobilizes the learning atmosphere, and improves the quality of teaching. At the same time, due to the network congestion and negative user experience, most users gave a one-star rating to Ding Ding. This was explained by a Ding Ding official in time, leading to the popularity of the topic of "five-star payment by installment" on Weibo. It was due to the timely response that more users gave a five-star rating. (2) According to the semantic network relationship graph of Tencent Meeting, we see that "five-star" and "meeting" are important nodes for Tencent Meeting evaluation. Taking "five-star" as a node, close keywords are "good reputation" and "epidemic", indicating that during the epidemic, users felt positively about the platform. Taking "meeting" as a node, the keywords are "convenience" and "screen", indicating that the Tencent Meeting platform was more convenient to use, and the quality and manner of screen presentation will affect the user experience. (3) According to the semantic network relationship graph of Tencent Class, "teacher", "software" and "class" were important nodes, and close keywords were "epidemic", "attend class" and "many problems", indicating that users valued the attendance function of Tencent Class, however, there are also several problems. (1) According to the semantic network relationship graph of Ding Ding, we use "study" as a node, and keywords that are close to this node are "epidemic" and "convenience". This is because during the epidemic, Ding Ding expanded the educational function on its platform, enabling colleges to use it as an online teaching platform. Taking "software" as a node, a closer keyword is "live broad cast", which also reflects that the teaching method of Ding Ding is mainly a live broadcast rather than students watching videos on their own. This method also increases the interactivity of online teaching, better mobilizes the learning atmosphere, and improves the quality of teaching. At the same time, due to the network congestion and negative user experience, most users gave a one-star rating to Ding Ding. This was explained by a Ding Ding official in time, leading to the popularity of the topic of "five-star payment by installment" on Weibo. It was due to the timely response that more users gave a five-star rating. (2) According to the semantic network relationship graph of Tencent Meeting, we see that "five-star" and "meeting" are important nodes for Tencent Meeting evaluation. Taking "five-star" as a node, close keywords are "good reputation" and "epidemic", indicating that during the epidemic, users felt positively about the platform. Taking "meeting" as a node, the keywords are "convenience" and "screen", indicating that the Tencent Meeting platform was more convenient to use, and the quality and manner of screen presentation will affect the user experience. (3) According to the semantic network relationship graph of Tencent Class, "teacher", "software" and "class" were important nodes, and close keywords were "epidemic", "attend class" and "many problems", indicating that users valued the attendance function of Tencent Class, however, there are also several problems. (4) According to the semantic network relationship graph of Chaoxing Learning, "software", "rubbish", "study", and "course" were important nodes, and closer keywords were "submit", "server", "login", "collapse" which reflected the many problems that occurred in the Chaoxing Learning, such as server crashes, inability to log in, and inability to submit the learning duration, which all had a negative impact on the user experience. (5) According to the semantic network relationship graph of MOOC, "learning", "rubbish", "course", and "software" were important nodes, and close keywords were "failed", "connect", "period", "server", and "progress". From these nodes, we can see that the MOOC platform often failed to connect, the learning time could not be submitted, and the server crashed. The independent nodes "account" and "homework" indicate that the platform was unable to register an account, could not submit a job, and could not refresh. "Delay" and "severity" indicate that the delay in the MOOC platform was quite significant. "College" and "abundant" reflect that MOOC users were mainly college students, and the course types were abundant due to the characteristics of the MOOC platform. MOOC focuses on video teaching and conducts self-study courses, which are the primary reasons for its use. Based on the analysis of the semantic network relationship graph obtained above, it can be seen that "epidemic", "student", "software", "teacher", "study", "five-star" and "every time" were important nodes that appeared together in the five platforms. The closer the nodes are to the words, the closer their relationship is. The presence of "every time" and "five-star" was caused by the timely response to problems in Ding Ding, thus indicating the large influence of the official Ding Ding group. NLPIR emotion analysis mainly uses two technologies. The first is the automatic recognition of emotion words and the automatic calculation of weights. The co-occurrence relationship and bootstrapping strategy is adopted to repeatedly produce new emotion words and weights. The second technology is a deep neural network for emotion discrimination. Based on a deep neural network, the extended calculation of emotion words is performed, which is integrated into the final result. By analyzing the comment data of the online teaching platforms, the emotion scores of the five teaching platform reviews were obtained, including the total emotion score, positive score, and negative score, as displayed in Table 6 . From the above emotion scores, it can be seen that the total emotion scores of Ding Ding, Tencent Meeting, and Tencent Class were all positive, while the total emotion scores of Chaoxing Learning and MOOC were negative, indicating that Ding Ding, Tencent Meeting and Tencent Class provided good user experience. In addition, the shortcomings of Chaoxing Learning and MOOC were more evident, as these platforms were not satisfactory for users. Because the negative score of Chaoxing Learning was much lower than the positive score, it is important to analyze the problems in the Chaoxing Learning platform to propose corresponding improvement measures. NLPIR adopts POS-CBOW (Problem Oriented System, Continuous Bag of Words), integrating the distribution characteristics of speech and words, using the word2vectormodel to train educational corpora, and automatically extracting semantic association relations. This paper expands the relevant semantics of high-frequency words on Ding Ding, Tencent Meeting, Tencent Class, Chaoxing Learning and MOOC. In addition, it captures new words and keywords with higher weight, and summarizes the factors that affect user experience. The part of the semantic graph related to Ding Ding is presented in Figure 5 . According to the relevant semantic expansion of the five platforms, the following words and phrases had the highest weight and the most frequent occurrence: "flash back", "convenient and swift", "customer service", "projection screen", "peep screen", "horizontal screen", "pop-up windows", "staff service", "prevention and control", "interactive panel", "dark mode", "abnormal network", "mobile office", "Ding mail", "call the camera", "bundled software", "shared screen", "client end", "verification code", "vertical screen" and "network anomaly", "recording", "web version", "screen recording", "no privacy", "blocking sight", "rotating the screen", "black screen", "background playback", "failed to load", "scan code", "system halted", "submit a job", "close microphone", "network fluctuations", "gesture check-in", "personal information", "submit homework", "main interface experience", "incompatibility", "lost connection", "self-rotating screen" and "mobile end". NLPIR adopts POS-CBOW (Problem Oriented System, Continuous Bag of Words), integrating the distribution characteristics of speech and words, using the word2vectormodel to train educational corpora, and automatically extracting semantic association relations. This paper expands the relevant semantics of high-frequency words on Ding Ding, Tencent Meeting, Tencent Class, Chaoxing Learning and MOOC. In addition, it captures new words and keywords with higher weight, and summarizes the factors that affect user experience. The part of the semantic graph related to Ding Ding is presented in Figure 5 . According to the relevant semantic expansion of the five platforms, the following words and phrases had the highest weight and the most frequent occurrence: "flash back", "convenient and swift", "customer service", "projection screen", "peep screen", "horizontal screen", "pop-up windows", "staff service", "prevention and control", "interactive panel", "dark mode", "abnormal network", "mobile office", "Ding mail", "call the camera", "bundled software", "shared screen", "client end", "verification code", "vertical screen" and "network anomaly", "recording", "web version", "screen recording", "no privacy", "blocking sight", "rotating the screen", "black screen", "background playback", "failed to load", "scan code", "system halted", "submit a job", "close microphone", "network fluctuations", "gesture check-in", "personal information", "submit homework", "main interface experience", "incompatibility", "lost connection", "self-rotating screen" and "mobile end". By classifying the new words and phrases mentioned above, we summarize the influencing factors that affect user experience, namely platform suitability, platform service type, platform privacy, platform teaching type, platform functionality, platform design environment, and network technology environment. By summarizing the factors influencing user experience for online teaching platforms during the epidemic, we determine the following table 7. Table 7 . Influencing factors. Description Platform Suitability "computer", "web", "tablet", "mobile terminal", "incompatibility" Platform privacy "peep screen", "prevention", "call the camera", "personal information" Platform service type "online customer service", "staff service" Platform teaching type "recorded broadcast", "live streaming" Platform design environment "blocking sight", "simple", "convenient and swift", "dark mode", "sharing the screen", "main interface experience", "interactive panel" Platform functionality "projection screen", "horizontal screen", "verification code", "close microphone", "vertical screen", "rotating screen", "scan a code", "submit homework", "self-rotating screen" Network technology environment "pop-up windows", "network anomaly", "bundled software", "server exception", "blank screen", "load fail", "system halted", "network fluctuation", "lost connection" By classifying the new words and phrases mentioned above, we summarize the influencing factors that affect user experience, namely platform suitability, platform service type, platform privacy, platform teaching type, platform functionality, platform design environment, and network technology environment. By summarizing the factors influencing user experience for online teaching platforms during the epidemic, we determine the following Table 7 . Table 7 . Influencing factors. Platform Suitability "computer", "web", "tablet", "mobile terminal", "incompatibility" Platform privacy "peep screen", "prevention", "call the camera", "personal information" Platform service type "online customer service", "staff service" Platform teaching type "recorded broadcast", "live streaming" Platform design environment "blocking sight", "simple", "convenient and swift", "dark mode", "sharing the screen", "main interface experience", "interactive panel" Platform functionality "projection screen", "horizontal screen", "verification code", "close microphone", "vertical screen", "rotating screen", "scan a code", "submit homework", "self-rotating screen" Network technology environment "pop-up windows", "network anomaly", "bundled software", "server exception", "blank screen", "load fail", "system halted", "network fluctuation", "lost connection" By classifying the new words mentioned above, we summarize the influencing factors that affect user experience, namely, platform suitability, platform service type, platform privacy, platform teaching type, platform functionality, platform design environment, and network technology environment. By summarizing the factors influencing user experience for online teaching platforms during the epidemic, we can determine the following: (1) The design environment of the platform should be more concise and easy to operate, and additional modes should be designed for different users at different times. For example, a "dark mode" at night can have better protective effect on the eyesight of students. (2) At present, the types of electronic devices continue to rise. To expand the use of the platform, it is necessary to increase the development of each port of the tablet. In addition, to make students more comfortable during an online class, the platform should be able to adjust the horizontal and vertical screen any time. (3) To improve the utilization and popularity of online teaching education platforms, customer services are essential. In the use of the platform, online customer service should always be available to address problems to prevent the wasting of learning time. (4) During the epidemic, not only college students and graduate students, but also primary and secondary school students, must study online. However, the concentration abilities of the latter groups are relatively limited, therefore, teachers cannot blindly teach by rote and lecturing, but must use a variety of different methods, such as "you ask me to answer", "face to face", "students record learning videos", and "real-time lecture". The platform should enhance the type of functions and improve the quality of interactive devices while setting software functions. (5) A stable network technology environment is the most important basis for improving teaching quality. If "network congestion" or "flash back" often occur in the use of the platform, the user experience as well as the usage rate will decrease accordingly. Based on the factors influencing user experience obtained by emotion, the advantages and disadvantages of online education noted by the users in the questionnaire (as illustrated in Figures 1-3) , and a large number of documents, this study aims to establish an effective but non-redundant index system. It combines Webqual 4.0 (availability, information quality, interaction quality) and the D&M (DeLone and McLean) system success model (information quality, system quality, service quality) to refine the influencing indicators. The indicators at each level correspond to the questions in the questionnaire. Among them, information quality and system quality are expressed together with subjective multiple choice questions, while others are expressed on Likert scales, as illustrated in Table 8 . Use frequency Per2-How often did you use an online teaching platform before COVID-19? Per3-When you use the online teaching platform for the first time, you will hold a completely negative attitude towards the platform because of some dissatisfaction with the use of the platform (such as registration trouble, slow login, etc.) Platform choice A6-What platforms will you use as learning aids during and after the COVID-19 pandemic? Structural equation modeling (SEM) is a common method to solve complex multivariable problems in social sciences. For example, in research fields such as social science, it is sometimes necessary to explore the relationship between more than one dependent variable and the influence path between hidden variables that cannot be directly measured. SEM can estimate abstract hidden variables through observable variables [37] . According to the above user satisfaction indicators, this paper uses the structural equation model to build the indicator system model and obtains the influence path coefficient of the latent variables on user satisfaction to draw the conclusion that the effects on user experience satisfaction weights are different. By using path analysis for the structural equation to determine the correlation between the indicators, and by decreasing the number of indicators to avoid redundant indicator construction, suggestions for improving the main influencing factors are proposed. The IS (information systems) success model proposed by DeLone and McLean [38] measured user satisfaction on a website in terms of the service quality. McKnight and Chervany [39] constructed the factors influencing customer belief and supplier intention from the perspective of psychology and sociology, and each structure was further decomposed into two to four measures. Lao et al. [40] used text mining technology to establish a curriculum quality evaluation model that included five first-level indicators: curriculum content, instructional design, interface design, media technology, and curriculum management to provide a base standard for learners to evaluate the quality of the curriculum. Huang et al. [41] constructed an overall evaluation index system based on online education using four primary indices: system structure, educational resources, interactive mode, and market environment. Based on the above analysis, this paper examines the factors influencing user satisfaction with the continuous usage of the intention of online teaching platforms by examining the four aspects of interaction quality, service quality, availability, and personal factors, and proposes the following hypotheses: Hypothesis 1. The interactive quality of the online teaching platform has a significantly positive influence on user satisfaction. The service quality of the online teaching platform has a significantly positive influence on user satisfaction. The availability of the online teaching platform has a significantly positive influence on user satisfaction. The personal factor of the online teaching platform has a significantly negative influence on user satisfaction. The user satisfaction with the online teaching platform has a significantly positive influence on the user's willingness to continue using the platform. The parameter analysis results of the initial model are listed in Table 9 . After estimating the initial model, the significance test of the path coefficient and load coefficient was required. The "C.R." (critical ratio) value was obtained by the disparity between the estimated parameter and standard parameter. When the absolute value of "C.R." was greater than 1.96 and the corresponding probability P value was less than 0.05, it can be stated that there was a significant difference between the path coefficient and the estimated parameter value of 0 at 95% confidence. Therefore, it is The parameter analysis results of the initial model are listed in Table 9 . After estimating the initial model, the significance test of the path coefficient and load coefficient was required. The "C.R." (critical ratio) value was obtained by the disparity between the estimated parameter and standard parameter. When the absolute value of "C.R." was greater than 1.96 and the corresponding probability p value was less than 0.05, it can be stated that there was a significant difference between the path coefficient and the estimated parameter value of 0 at 95% confidence. Therefore, it is assumed that the influence of the path coefficient was significant. It can be seen that the path coefficient of "user personal factors" on user satisfaction was unable to pass the significance test. Note that *** reflects the significance level when p < 0.001.C.R is the abbreviation of critical ratio. S.E is the abbreviation of Standard Error. <-> reflects the influencing factors are correlated.