Exploring Factors of Middle School Teachers’ Satisfaction with Online Training for Sustainable Professional Development under the Impact of COVID-19

Abstract

During the COVID-19 pandemic, as offline learning activities were blocked, teachers’ training activities were moved from face-to-face to online training. Therefore, teachers had to join an increasing number of online training sessions. However, few studies have focused on teachers’ satisfaction with online training. To address this gap, based on the American user satisfaction theory model (ACSI), this study established the factors of expectation of online training quality, perceived online training quality, perceived online training value, and teacher satisfaction with online learning, and aimed to explore their relationships with six hypotheses. A total of 397 middle school teachers who had online training experience participated in the survey through an online questionnaire. SPSS 26.0 and AMOS 23.0 were used to analyze the data. The results showed that (1) expectation of online training quality was positively correlated with perceived online training quality; (2) expectation of online training quality was negatively correlated with perceived online training value; (3) perceived online training quality was positively correlated with perceived online training value; and (4) perceived online training value was positively correlated with online training satisfaction. The findings imply that teachers should be informed in advance of various difficulties that may be encountered in online training, so as to reduce their expectations of online training quality. In addition, in order to improve teachers’ perceived quality and perceived value of online training, intervention strategies should be proposed, online training platforms should be optimized, and online training methods should be innovated to improve teachers’ sustainable development ability.

1. Introduction

Teachers’ professional competence greatly influences their teaching, as well as the academic achievement and future development of their students [1,2]; thus, many countries pay great attention to teacher education and aim to improve teachers’ sustainable development ability. With the outbreak of COVID-19 on a global scale, the original offline teacher training activities were disrupted. During the COVID-19 pandemic, most teachers’ professional development activities were moved from offline to online, and online teacher training developed rapidly [3,4]. The addition of informational communications technology (ICT) also makes training platform designers aware that new possibilities can be provided for teacher training via the internet [5,6]. Teachers’ professional development and their training content, resources, and environments have also greatly changed due to ICT [7,8].

During the COVID-19 pandemic, there have been both opportunities and challenges to improve teachers’ training systems and professional skills. Online training is rapidly becoming one of the most important models for teachers to improve their professional skills [9,10]. With online training, teachers can carry out training activities according to their personal schedule without the limitation of having to attend sessions at a certain time. This effectively solves the conflict between teachers’ working and learning time [11,12]. In addition, online training has broken through the limits of location, networks, and online communities, offering teachers the opportunity to share knowledge and learn with peers who are located far away from each other [13]. However, with the development of teachers’ online training activities, there are some problems in practice. For example, when teachers use online learning media, they might encounter problems such as a low level of perceived usefulness and perceived ease of use, technical problems, and a lack of real-time and synchronous interactions [14]. There are two main reasons for this situation. Firstly, at that time, it was the first time that teachers had received online training, and the lack of experience made teachers prone to technical problems in use, which in turn negatively influenced their perceived usefulness and perceived ease of use. Secondly, online training had just been launched, and the functions and services had not yet been perfected [4]. Not all teachers are fully involved in online communities; most are still observers, lacking learning in teaching skills and meaningful participation [15,16]. In this study, teachers’ sustainable professional development refers to teachers’ use of various forms in their work to improve their individual and collective professional abilities [17]. Since being affected by the pandemic, the sustainable professional development of teachers around the world has increasingly been carried out in the form of online training. At the same time, the technology platform for online training is becoming ever more mature, providing more support for teachers’ online training. Therefore, online teacher training will become one of the most important forms of sustainable professional development of teachers in the future. In fact, during the COVID-19 pandemic, education departments around the world have carried out a larger scale of online teacher training activities. In this process, do teachers’ perceptions of the quality and value of online training meet their expected requirements? On the whole, how satisfied are teachers with online training during the COVID-19 pandemic? Looking to the future, what measures need to be taken by online training organizations to promote the continuous improvement of teachers’ online training activities so as to better increase teachers’ sustainable development ability? In order to answer these questions, this study investigated the online teacher training programs organized by local education departments in China.

2. Theoretical Background and Hypotheses

2.1. User Satisfaction Theory

The academic community generally adopts the definition of user satisfaction developed by Professor Philip Kotler (1989) [18]. He defined user satisfaction as the state of feeling of difference formed by users after comparing their sense of experience after using all of an organization’s services, situations, activities, processes, and so on, with their expectations before using them. Since the 1970s, the importance of customer satisfaction has been widely recognized in both academic and practical communities. According to Oliver (1981) [19], customer satisfaction is a psychological state that can be raised when emotions surrounding unconfirmed expectations are combined with consumers’ prior feelings about the consumer experience. After that, Scheff and Kotler (1997) [20] pointed out that customer satisfaction is derived from the perceived functional characteristics of the product and personal expectations of the product, and the comparison of the two states of pleasure and disappointment. This study drew on the user satisfaction theory, but transferred the original definition of user satisfaction in the economic field to the educational field. The American user satisfaction model (ACSI), as the most complete and widely used system, was applied as the foundation of this study. In particular, in European and American countries, it is widely used in higher education, and it has also become an important reference index for the evaluation and supervision of education quality [21,22]. This model was proposed in 1994 and includes six variables and nine relationships: user expectations, user perception of quality, user perception of value, user satisfaction, user complaints, and user loyalty. The biggest advantage of ACSI is that fewer samples can be used to obtain a more accurate result of a company’s user satisfaction, and it can be used to make cross-industry comparisons, as well as longitudinal comparisons across time. According to previous studies, user expectations, user perceptions of quality, and users’ perceptions of value all directly and positively affect user satisfaction. Among them, user expectations and user perceptions of quality will indirectly affect users’ perceptions of value, and ultimately affect their satisfaction, while user expectations positively affect user perceptions of quality [23]. On the basis of this model, variables were chosen and hypotheses were proposed in this study. Due to the difference between the fields of economics and education, some of the model variables of ACSI were deleted according to the actual situation. Specifically, two variables were removed: user loyalty and user complaints. The reason is that the main question of this study is what factors affect teachers’ satisfaction with online training, rather than how their satisfaction ultimately affects whether they choose or complain about training content. Since the researchers of this study invited the specific participants who were trained with the same programs which included the basic online teaching strategies or skills, they did not need to choose certain training content.

2.2. Expectation of Online Training Quality

Customer expectation is a basic construct in marketing, which reflects a desire of customers to accept a product or service [24]. In this study, the concept of “expectation of online training quality” was derived from the “expectation of user” index in the ACSI model. As trainees of online training, teachers will form an a priori psychological reaction before using training resources, which includes what kind of resources, what kind of functions, what kind of feedback, and so on. Therefore, the expectations of the quality of online training in this study are divided into three dimensions: expectations of content, functions, and services.

2.3. Perceived Online Training Quality

According to Zeithaml (1988) [25], perceived quality refers to consumers’ judgment of the overall excellence or superiority of a product. Because of information asymmetry, consumers perceive the quality of a product based on their limited knowledge of it [26]. In this study, perceived online training quality was defined as comparing teachers’ expectations before taking online training with their feelings after taking it. Therefore, as the trainees of online training, teachers’ perceived quality can be affected by the quality of the online training resources and personalized services.

Xu et al. (2017) [27] divided customer expectation into three kinds, and found that deserved attribute expectation had a negative impact on perceived service quality, while proper and desired attribute expectations had a positive influence. However, Liu and Zhang (2021) [21] developed the TSI model based on the ACSI model to understand teacher satisfaction with online learning, and found that teacher expectation had a positive effect on teacher-perceived quality. Obviously, the above-mentioned research has generated controversy regarding whether quality expectations have a positive or negative impact on perceived quality. Therefore, it is necessary to carry out further scientific empirical confirmation of this issue. It is worth asking, in the context of online teaching training, does the positive relationship between teacher expectation and teacher perceived quality still hold true? To answer this question, combined with the belief that quality expectations in the ACSI model have a positive impact on perceived quality [28], hypothesis 1 (H1) was proposed.

Hypothesis 1 (H1).

Expectation of online training quality has a positive impact on perceived online training quality.

2.4. Perceived Online Training Value

Sánchez-Fernández and Iniesta-Bonillo (2007) [29] stated that the nature of perceived value was complex and multi-dimensional. Boksberger and Melsen (2011) [30] pointed out that the perceived value of services was a combined evaluation of consumers’ perception of benefits and sacrifices. In this study, perceived online training value means that teachers feel the improvement of ideological standards or the promotion of teaching after taking online training. A number of studies have suggested that perceived value is the most important predictor of customer satisfaction [31,32,33].

Frank and Enkawa (2010) [34] found that perceived value is positively influenced by lagged economic expectations. Wang (2008) [35] found that perceived value was influenced by information quality, system quality, and service quality, and users’ perceived value is positively correlated with quality. Revilla-Camacho et al. (2017) [36] conducted an empirical study in the field of hotel management, and the results indicated that the quality perceived by the customers had a positive impact on the perceived value of the service, but not on satisfaction. However, in the field of online teaching and training, it is unclear whether teachers’ perception of the value of online training is affected by their expectations and perceptions, so further scientific empirical confirmation of this issue is necessary. Through the analysis of relevant research, it can be known that the ACSI model has relevant application research in the fields of economics, management, and education, so this model has good general explanatory power. In the ACSI model, user expectations and user perceptions of quality positively affect user perception of value [28], so combining the issues raised in the literature and the ACSI model, Hypothesis 2 (H2) and Hypothesis 3 (H3) were proposed.

Hypothesis 2 (H2).

Expectation of online training quality has a positive impact on perceived online training value.

Hypothesis 3 (H3).

Perceived online training quality has a positive impact on perceived online training value.

2.5. Teacher Satisfaction with Online Training

Customer satisfaction is a psychological reaction after the customer demands are met and a judgement of the degree to which customers are satisfied with the products and service [37,38]. In online teaching training, teachers’ overall satisfaction refers to the overall satisfaction that is accumulated and finally formed in the process of use. Therefore, this study combined the dimensions of perceived quality to divide satisfaction from quality, service, and function, namely: online training quality satisfaction, online training service satisfaction, online training function satisfaction, and overall online training satisfaction.

Different studies have different views on the impact of expected quality on satisfaction. Some researchers have claimed that customer expectation has a positive influence on satisfaction [39], while others have argued that the influence of customer expectation on satisfaction is negative [34,40,41]. Some researchers even found that customer expectation has no substantive effect on satisfaction [42,43]. Therefore, in the field of online training, the controversy mentioned above still exists, and whether expected quality will affect teachers’ online training satisfaction and what kind of impact it will have on teachers’ online training satisfaction need to be further verified. Considering that the quality expectations in the ACSI model have a positive impact on user satisfaction [28], and the ACSI model has good general explanatory power, Hypothesis 4 (H4) was proposed.

Hypothesis 4 (H4).

Expectation of online training quality has a positive impact on teacher satisfaction with online training.

Askariazad and Babakhani (2015) [44] applied ECSI in a business to business (B2B) context, and claimed that perceived quality can positively influence both satisfaction and perceived value. Ciavolino and Dahlgaard (2007) [45] carried out a case study based on the ECSI model, and found that perceived value had a positive impact on customer satisfaction. Wong and Dioko (2013) [46] found that perceived value was positively related to customer satisfaction, and partially mediated the relationship between perceived performance and customer satisfaction. Koukopoulos et al. (2020) [47] found that educational quality will positively influence satisfaction with the use of Culture Gate. Sarantidou (2017) [48] examined the role of customer satisfaction in retailers’ loyalty as well as its impact on the retailer’s brand strength, and the results showed that perceived product quality had a positive impact on both customer satisfaction and perceived value. According to previous studies, in the fields of economy, management, and education, both perceived quality and perceived value have a positive impact on user satisfaction, and this situation is consistent with the variable relationship in the ACSI model; Hypothesis 5 (H5) and Hypothesis 6 (H6) were therefore proposed.

Hypothesis 5 (H5).

Perceived online training quality has a positive impact on teacher satisfaction with online training.

Hypothesis 6 (H6).

Perceived online training value has a positive impact on teacher satisfaction with online training.

Based on the above literature and integrating the above hypotheses, the study intended to explore structural relationships among factors influencing teacher satisfaction with online training. Expectation of online training quality, perceived online training quality, and perceived online training value were perceived as critical variables affecting teacher satisfaction with online training. The research model for this study is shown in Figure 1.

3. Methods

3.1. Participants

The target population of this study is middle school teachers who participated in online training activities during the closure caused by COVID-19 in China. Considering the practicability of the questionnaire, we chose W County, Anhui Province, China, for this study. Teachers covered a wide range of subjects, grades, ages, and genders. Some of them had previous experience of online training, while others participated in online training activities for the first time during the pandemic.

Online questionnaires (Appendix A) were distributed via Questionnaire Star (www.wjx.cn) (accessed on 28 October 2021), a professional online survey tool widely used in China. The link to the online questionnaire was sent to several local middle schools, which in turn sent the link to the teachers, ensuring that the questionnaires were filled out without duplication or omission. The anonymity and voluntariness to participate in this survey, as well as the confidentiality of the collected data and information, were stated to the participants in the preamble to the questionnaire before they filled it out. A total of 466 questionnaires were recovered in this study, excluding 69 questionnaires that were incompletely filled in or which selected more than 80% of the same items in a row. Finally, 397 valid questionnaires were obtained, and the effective recovery rate of the questionnaire was 85.19%.

In this study, there were 155 male teachers and 242 female teachers. From the perspective of age, the sample over 46 years old was the largest, with 160 teachers, followed by 36–45 years old, with 125, which is in line with the characteristics of the brain drain in county-level schools and the general age of teachers. Judging from the teaching period, primary school teachers answered the most questionnaires, with 194, comprising nearly half, followed by high school teachers, with 114. In terms of the subjects taught, Chinese, mathematics, and English had the largest number of teachers, with 111, 100, and 52, respectively, which is related to the curriculum structure of primary and secondary schools. The number of teachers in these subjects will also be correspondingly higher than the number of teachers in other disciplines.

3.2. Instrument

To measure the variables, the perceived online training quality scale, perceived quality scale, perceived training value scale, and teachers’ online training satisfaction scale were used in our questionnaires. They are all on a 5-point Likert scale, with 1 to 5 representing “strongly disagree” to “strongly agree”. The larger the number chosen by the respondents, the higher their satisfaction, with 3 representing a neutral attitude. When using the questionnaire, we first translated it directly according to Chinese language habits, and then invited some scholars and middle school teachers to review it, further optimizing the language expression of the questionnaire items. Then, we conducted a pre-test on 51 people, and the data collected in the pre-test proved that the questionnaire could be used for the next step of the actual test. The measurement items of other variables referred to the relevant questionnaires which were revised in the practical application of this study. Demographic information about the respondents was collected in the first part of the questionnaire, including gender, age, discipline, years of teaching, and so on.

3.2.1. Expectation of Online Training Quality

This scale was adapted from the scale of perceived online training quality [49,50] and corresponded to items of perceived online training quality, which was designed to explore the gap between expected quality and perceived quality. Before taking online training, users already had psychological expectations about its content, functions, and services. Different expectations of training quality would affect user satisfaction.

3.2.2. Perceived Online Training Quality

To assess perceived online training quality, this study designed a questionnaire of perceived quality based on the literature review. To make the scale specific to online training, we adapted all items by adding wording related to online training. The adjusted scale comprised 11 items. This scale was adapted from Chen et al. (2020) [49], Harsasi et al. (2018) [50], and Chen et al. (2020) [51] to assess perceived online training quality according to aspects such as resource quality, function quality, and service quality of perceived online training. Eleven items were designed to evaluate participants’ perceived online training quality. Sample items include: “Online research resources match with subject knowledge and are closely related” and “Online research management functions are convenient to use”.

3.2.3. Perceived Training Value

This scale of perceived training value synthesized the questionnaires of Harsasi et al. (2018) [50] and Liu et al. (2021) [21], in which the former proposed some items for measuring the value gained from the cost of time and intelligence, and the latter designed some items to assess the influence of online training on teachers’ teaching improvement and professional development. Sample items are: “The benefits of using online research are consistent with the time cost” and “Online study is helpful for your teaching improvement”.

3.2.4. Teachers’ Satisfaction with Online Training

Six items were used to measure teachers’ satisfaction with online training. This scale was adapted from Chen et al. (2020a) [49] and Liu et al. (2021) [21] to assess teachers’ satisfaction with online training from aspects such as satisfaction with resource content and functionality, satisfaction with resource services, and overall satisfaction with online study. Sample items are: “You are satisfied with the functions of the current online research resources” and “You are satisfied with the overall use of the resources for the current online study”. In addition, the dimension of willingness to continue using online study and recommend to others was also added in the current study, and this dimension was based on Harsasi et al.’s (2018) [50] scale. Sample items include: “You will continue to use online workshops if necessary or if conditions permit” and “You are willing to recommend the online research platform or course to others”.

3.3. Effectiveness and Reliability of Tools

The original questionnaire had a total of 32 items, including 11 for expectation of online training quality, 11 for perceived online training quality, 4 for perceived online training value, and 6 for teacher satisfaction with online training. First of all, teachers majoring in Modern Educational Technology and Psychology were invited to modify the questionnaire. Afterwards, the reliability and validity of the measurement scale were tested by issuing pre-test questionnaires, and confirmatory factor analysis (CFA) was used to determine the internal validity of each structure, and items with high residual values (> 0.5) and low normalized factor loadings (< 0.5) needed to be removed [52,53]. The reliability and validity results showed that the Cronbach’s α coefficient of each variable was higher than 0.7, indicating that the scale had good reliability. Finally, 32 items were reserved for further analysis and formal distribution of the questionnaire. The factor loadings of the formal questionnaire items were all greater than 0.5, and the combined reliability (CR > 0.7) and average variance extracted (AVE > 0.5) all met the requirements, which indicated that the questionnaire had good reliability and validity (see

Table 1. Reliability and Validity Analysis of the Prediction Questionnaire.

Variable	Items	Factor Loading	Cronbach’s Alpha Coefficient	CR	AVE
Expectation of online training quality (EX)	EX1	0.840	0.973	0.974	0.7744
	EX2	0.926
	EX3	0.949
	EX4	0.949
	EX5	0.746
	EX6	0.778
	EX7	0.787
	EX8	0.919
	EX9	0.945
	EX10	0.918
	EX11	0.890
Perceived online training quality (QUA)	QUA1	0.802	0.978	0.9791	0.81
	QUA2	0.968
	QUA3	0.906
	QUA4	0.961
	QUA5	0.904
	QUA6	0.907
	QUA7	0.887
	QUA8	0.935
	QUA9	0.831
	QUA10	0.906
	QUA11	0.879
Perceived online training value (VAL)	VAL1	0.960	0.955	0.9549	0.8412
	VAL2	0.863
	VAL3	0.928
	VAL4	0.915
Teacher satisfaction with online training (SAT)	SAT1	0.907	0.943	0.9448	0.7415
	SAT2	0.905
	SAT3	0.888
	SAT4	0.920
	SAT5	0.734
	SAT6	0.796

and

Table 2. Reliability and validity analysis of the revised questionnaire.

Variable	Items	Factor Loading	Cronbach’s Alpha Coefficient	CR	AVE
Expectation of online training quality (EX)	EX1	0.889	0.985	0.9847	0.8545
	EX2	0.936
	EX3	0.952
	EX4	0.954
	EX5	0.945
	EX6	0.960
	EX7	0.958
	EX8	0.938
	EX9	0.900
	EX10	0.890
	EX11	0.838
Perceived online training quality (QUA)	QUA1	0.820	0.981	0.9814	0.828
	QUA2	0.901
	QUA3	0.874
	QUA4	0.910
	QUA5	0.928
	QUA6	0.938
	QUA7	0.924
	QUA8	0.944
	QUA9	0.936
	QUA10	0.949
	QUA11	0.877
Perceived online training value (VAL)	VAL1	0.922	0.969	0.9691	0.8868
	VAL2	0.924
	VAL3	0.963
	VAL4	0.957
Teacher satisfaction with online training (SAT)	SAT1	0.902	0.959	0.9591	0.7969
	SAT2	0.945
	SAT3	0.949
	SAT4	0.938
	SAT5	0.813
	SAT6	0.796

).

3.4. Data Analysis

SPSS 26.0 and AMOS 23.0 (IBM, Armonk, NY, USA) were used to analyze the data. First, the final questionnaire was tested to ensure that its reliability and validity reached the standard, and that further analysis could be carried out. Secondly, descriptive statistical analysis and a normality test were performed on the data. Subsequently, in order to explore the potential relationship between these four variables, a Pearson correlation analysis was performed among EX, QUA, VAL, and SAT. Finally, AMOS 23.0 was applied to construct a structural equation model to verify whether the assumptions were valid, and combined the data to correct the structural equation model to obtain the final structural equation model and the corresponding assumptions.

4. Results

4.1. Descriptive Statistical Analysis

Since we aimed to construct a structural equation model, the ratio of the number of samples to the number of items needed to meet a certain standard to ensure the validity of the structural equation model. Hu and Bentler (1998) [54] recommended that a ratio of the number of samples to the number of items (N/t) greater than five would be acceptable. There were a total of 32 measurement items in this study, so a minimum sample size of 160 was required. After two weeks of distribution, a total of 466 questionnaires were recovered in this study, excluding 69 questionnaires that were incompletely filled in or that selected more than 80% of the same items in a row; finally 397 valid questionnaires were collected. If N/t is greater than five, further analysis and exploration can be carried out.

Table 3. Descriptive analysis statistics.

Variable	Items	The Number of People Who Chose This Option					Mean		Standard Deviation
		1	2	3	4	5
Expectation of online training quality (EX)	EX1	9	3	30	63	292	4.58	4.61	0.845
	EX2	8	4	19	80	286	4.59		0.801
	EX3	9	3	22	66	297	4.61		0.817
	EX4	9	3	20	70	296	4.62		0.801
	EX5	11	1	20	68	297	4.61		0.830
	EX6	10	2	23	60	302	4.62		0.828
	EX7	10	3	21	67	296	4.60		0.834
	EX8	8	1	23	63	302	4.64		0.778
	EX9	8	3	19	66	301	4.63		0.785
	EX10	7	2	26	68	294	4.61		0.782
	EX11	8	3	29	69	288	4.58		0.824
Perceived online training quality (QUA)	QUA1	6	5	62	101	223	4.34	4.33	0.891
	QUA2	7	6	69	105	210	4.27		0.922
	QUA3	6	5	60	99	227	4.35		0.888
	QUA4	6	5	68	101	217	4.30		0.902
	QUA5	5	3	56	110	223	4.37		0.844
	QUA6	5	3	61	101	227	4.37		0.859
	QUA7	4	4	57	113	219	4.36		0.837
	QUA8	4	7	67	95	224	4.33		0.887
	QUA9	4	5	62	105	221	4.35		0.858
	QUA10	5	6	67	98	221	4.32		0.894
	QUA11	7	9	68	94	219	4.28		0.946
Perceived online training value (VAL)	VAL1	4	14	84	133	162	4.10	4.13	0.919
	VAL2	5	12	83	136	161	4.10		0.917
	VAL3	6	11	77	128	175	4.15		0.929
	VAL4	6	8	78	130	175	4.16		0.911
Teacher satisfaction with online training (SAT)	SAT1	10	10	141	168	68	3.69	3.76	0.872
	SAT2	6	16	133	175	67	3.71		0.847
	SAT3	7	12	131	164	83	3.77		0.875
	SAT4	6	13	136	161	81	3.75		0.868
	SAT5	8	11	117	164	97	3.83		0.898
	SAT6	11	14	121	157	94	3.78		0.941

shows the general responses to the data collected from the questionnaire, with 1 to 5 representing “strongly disagree” to “strongly agree”. Taking EX1 as an example, nine teachers indicated that they were very dissatisfied, while 292 teachers were very satisfied. The mean value of EX was 4.61, which was close to 5, representing that for the variable EX (expectation of online training quality), the respondents’ expected quality was very high. The standard deviation is a measure of the spread of the mean of a set of data, where a smaller standard deviation means that the values are closer to the mean [55]. In this study, the standard deviation is less than one, which indicates that the choice of the respondents was relatively concentrated, and the fluctuations above and below the mean were small.

4.2. Correlation Analysis

Correlation analysis is the study of the correlation between variables. It can be used to study the direction and degree of the relationship between two or more random variables. It is one of the basic statistical analysis methods. The measurement standard is the correlation coefficient [56]. Pearson correlation coefficient r was calculated to conduct correlation analysis.

As shown in

Table 4. Correlation analysis.

	EX	QUA	VAL	SAT
EX	1
QUA	0.530 **	1
VAL	0.325 **	0.779 **	1
SAT	0.232 **	0.462 **	0.548 **	1

Note. ** p < 0.01.

, according to the correlation coefficient analysis results, all r values were greater than zero; that is, the main variables such as the expectation of online training quality, perceived online training quality, and perceived online training value were all related to user satisfaction. Among them, the relationship between the expectation of online training quality and teacher satisfaction with online training were low-level linear positive correlations, whereas the relationships between other variables were all moderately linearly correlated.

4.3. Model Fitting Analysis

4.3.1. Initial Model Analysis and Correction

Model fit reflected the fit between the constructed model and the actual collected data. According to the variables and relationships in this study, the initial structural equation model was constructed, and the corresponding fitting index was obtained, as shown in

Table 5. Initial Model Fit Index.

Indicator Name	Evaluation Standard		Actual Value	Fitting Results
	Excellent	Good
CMIN/DF (Chi-squared degrees of freedom ratio)	1–3	3–5	5.380	Bad
RMSEA (Root Mean Square Error of Approximation)	< 0.05	< 0.1	0.105	Bad
GFI (Goodness-of-Fit Index)	> 0.9	0.7–0.9	0.686	Bad
PGFI (Parsimonious Goodness-of-Fit index)	> 0.9	> 0.5	0.602	Good
TLI (Nonnormal Fit Index)	> 0.9	0.7–0.9	0.880	Good
CFI (Comparative Fit Index)	> 0.9	0.7–0.9	0.888	Good
IFI (Incremental Fit Index)	> 0.9	0.7–0.9	0.889	Good

below. However, the overall fitting results were not satisfactory, so further analysis of the initial model was required. The path coefficients were analyzed. In path coefficient analysis, the significance and direction of paths are mainly judged by the C.R. value (critical ratio), the p value, and the estimate value (path coefficient). The p value is significant. If p < 0.001, it will be displayed with the symbol “***”, indicating that it has reached a significant level. If p > 0.001, the size of the p value will be directly displayed, indicating that it has not reached a significant level. The estimate value indicates the degree of influence of this path. The larger the absolute value, the more significant the influence level is. The estimate value > 0 indicates that the path variables are positively correlated, and the estimate value < 0 indicates that the path variables are negatively correlated. The results are shown in

Table 6. Initial model path coefficients.

Hypothesis	Path	Estimate	S.E.	C.R.	p	Supported
H1	QUA←EX	0.571	0.053	10.810	***	Yes
H2	VAL←EX	−0.155	0.043	−3.584	***	No
H3	VAL←QUA	0.913	0.048	19.132	***	Yes
H4	SAT←QUA	0.035	0.069	0.511	0.609	No
H5	SAT←EX	0.036	0.046	0.773	0.439	No
H7	SAT←VAL	0.328	0.061	5.396	***	Yes

Note. *** p < 0.001.

. If there are two assumptions which do not hold, one assumes a negative correlation. Therefore, the model should be modified accordingly by combining the fitting index, path coefficient, and MI correction index.

4.3.2. Model Fitting Analysis after Correction

After correction, the final model of this study was obtained, as shown in Figure 2 below, and its fitting index and path coefficient are shown in

Table 7. Modified model fitting index.

Indicator Name	Evaluation Standard		Actual Value	Fitting Results
	Excellent	Good
CMIN/DF (Chi-squared degrees of freedom ratio)	1–3	3–5	4.624	Good
RMSEA (Root Mean Square Error of Approximation)	<0.05	<0.1	0.096	Good
GFI (Goodness-of-Fit Index)	>0.9	0.7–0.9	0.733	Good
PGFI (Parsimonious Goodness-of-Fit index)	>0.9	>0.5	0.642	Good
TLI (Nonnormal Fit Index)	>0.9	0.7–0.9	0.901	Excellent
CFI (Comparative Fit Index)	>0.9	0.7–0.9	0.908	Excellent
IFI (Incremental Fit Index)	>0.9	0.7–0.9	0.908	Excellent

and

Table 8. Corrected model path coefficients.

Hypothesis	Path	Estimate	S.E.	C.R.	p	Supported
H1	QUA←EX	0.563	0.052	10.779	***	Yes
H2	VAL←EX	−0.150	0.042	−3.594	***	No
H3	VAL←QUA	0.887	0.048	18.390	***	Yes
H7	SAT←VAL	0.392	0.041	9.672	***	Yes

Note. *** p < 0.001.

below. It can be seen that in the revised model, the fitting index is higher, indicating that the model has better explanatory power, and all paths are established, one of which is negatively correlated with the assumption, and the rest are positive correlations that are consistent with the assumption.

5. Discussion

5.1. Expected Quality Positively Affects Perceived Quality

The results of this study showed that EX was positively correlated with VAL (β = 0.563, p < 0.001; H1 supported), and this result was consistent with the hypothesis and existing studies [21,27]. These findings indicated that appropriate and expected attribute expectations had a positive impact on perceived service quality. Thus, when teachers who used online training had higher expectations regarding the quality and service of online training resources, they tended to increase their desire to explore and use them. The current network training resources are relatively rich, and they tend to be more practical for teaching [4]. Compared with the single and limited training resources of traditional offline teacher training, these useful network training resources can allow teachers to obtain the required resources more conveniently, which greatly improved teachers’ expectations for online training.

Therefore, in order to properly improve EX, it is possible to enrich the types and quantities of resources of the training platform, and make tutorials for the trained teachers, so as to help them understand the richness and convenience of platform resources.

5.2. Expected Quality Negatively Affects Perceived Value

The results of this study showed that EX was negatively correlated with QUA (β = 0.150, p < 0.001; H2 not supported, negative correlation); this result was not consistent with the hypothesis and existing studies. Frank and Enkawa (2010) [34] pointed out that perceived value was positively affected by lagging economic expectations, but according to the data statistics of this study, the expected quality of teachers had a negative impact on perceived value. After daily communication with the front-line teachers, I learned that when they did not use the online training, they were very much looking forward to the teaching examples and teaching skills in the online training. If the gap between the high expected quality and the perceived actual value is too large, the teacher’s perceived value will be greatly reduced.

Therefore, the platform can hire professional teachers to review the training resources, and ensure the quality of resources while having abundant resources, thereby reducing the gap between teachers’ expected quality and perceived quality.

5.3. Perceived Online Training Quality Is Positively Related to Perceived Online Training Value

The study results showed that VAL was positively associated with QUA (β = 0.887, p < 0.001; H3 supported), which is consistent with the previous studies on the impact of VAL on QUA [35,44,48]. The value perceived by users was directly proportional to the quality, and this path had the largest coefficient, which showed that VAL had a positive influence on QUA. The quality of resources, functions, and services that teachers perceive when using the platform will directly affect the overall perceived value of teachers after using the platform. The better the quality of the resource, the higher the value perceived by teachers. Only when the online training platform really starts from the actual needs of teachers, solves the difficulties of teachers, and focuses on teachers, will teachers’ perceived quality and perceived value of online training continue to improve.

Therefore, an important way to improve the perceived value of teachers’ online training is to review the quality of online training resources by experts, design the platform and maintain the daily operation of the platform by professionals, and ensure timely service to teachers with problems.

5.4. Expectation of Online Training Quality Has No Obvious Effect on Teacher Satisfaction with Online Training

The results of this study showed that EX was uncorrelated with SAT (p > 0.001; H4 was unsupported). Previous studies had different opinions on the influence of EX on SAT, including positive influence [39], negative influence [40], and no substantial influence [42]. In this study, we tentatively determined the positive influence of EX on SAT in the initial model. However, based on the results of empirical research, we think that EX had no obvious effect on SAT.

The path data obtained in this study show that in the process of using online training, teachers will not preconceive the degree of satisfaction of the training based on their own expectations of the quality of resources, but will perceive the specific experience after actual use. Therefore, online training quality expectations have no significant impact on teachers’ online training satisfaction.

5.5. Perceived Online Training Quality Has No Obvious Effect on Teacher Satisfaction with Online Training

The study results showed that VAL was uncorrelated with SAT (p > 0.001; H5 was unsupported). In the initial literature review, some studies showed that VAL had a positive effect on SAT [44,47], while others showed no significant effect [36]. Considering these different views, this study determined the positive influence of VAL on SAT in the initial model. However, according to the results of the further empirical study and model specification, there was no obvious relationship between VAL and SAT.

From the path data obtained in this study, it can be seen that teachers need to comprehensively consider the expected quality and perceived quality, and only after the perceived online training value can be obtained, will the final satisfaction be affected by the perceived value. This shows that online training for teachers needs to be promoted in advance to help teachers understand and use this platform, instead of directly plugging the training platform to teachers, allowing teachers to conduct online training without expectations.

5.6. Perceived Online Training Value Is Positively Related to Teacher Satisfaction with Online Training

In this study, when teachers had stronger QUA, they also had stronger SAT (β = 0.392, p < 0.001; H6 supported), which was consistent with the previous studies on the impact of QUA on SAT [31,32,33]. In addition, this study verified that QUA was jointly influenced by expectation of online training quality and perceived online training quality, and ultimately, QUA affected SAT [46]. Therefore, it is effective for teachers to advance their satisfaction with online training through improving perceived online training value by enhancing the expected quality and perceived quality of teachers’ online training.

Based on the needs of front-line teachers, the expected quality of online training for teachers should be improved by publicizing the richness of the platform’s resources before training, and issuing manuals to guide teachers on how to use the platform. The quality of resources, the completeness of functions and the timeliness of services for teachers when using the platform are also an important part of improving the perceived quality of teachers’ online training. Combining the above practices can truly provide front-line teachers with the resources they need, and ultimately improve teachers’ satisfaction with online training.

6. Conclusions

During the COVID-19 pandemic, online training activities for teachers expanded, and online training is now rapidly becoming an important mode for teachers to improve their professional skills. Many scholars have also conducted research on the impact of the new coronavirus pandemic on the field of education and teaching. Liu et al. (2022) also similarly confirmed that improving the resources, functions, and services of online training platforms is very effective in terms of improving teachers’ online training, which is a direct factor affecting the effect of online training [1]. Therefore, it is particularly important to improve teachers’ satisfaction with online training activities. The conclusions of this study provide some suggestions for improving teacher satisfaction with online training.

The results of this study showed that EX was positively related to VAL (H1), negatively correlated with QUA (H2), and uncorrelated with SAT (H4). VAL was positively related to QUA (H3) and uncorrelated with SAT (H5). QUA was positively related to SAT (H6). Accordingly, it is not only beneficial but also crucial to improve the computer ability and willingness of middle school teachers, while improving the content quality, service, and function of online training platforms. This study puts forward appropriate suggestions for online training platforms and teachers, including how to improve the quality of resources and personalized service level of online training platforms, and how to improve teachers’ personal abilities.

6.1. Implications

The contribution of this research to society is reflected in the following aspects. In terms of theoretical contributions, previous studies have mainly focused on the situation of teachers’ offline training. Although online training has also developed, before the large-scale sweep of the COVID-19 pandemic, online training was more of a tool to assist offline training. Since the advent of the post-pandemic era, online training for teachers has become the main mode of training today. Therefore, the research on online training is very different before and after the pandemic. This research enriches the literature on online training in the post-pandemic era. Another theoretical contribution of this study is that it highlights the dominant role of the users of online training—teachers. However, in the past literature research, more attention was paid to the design of objective platform resources, and few studies have focused on teachers. Starting from the main body and paying attention to teachers’ ideas, it ignores the improvement of teachers’ sustainable development ability in teacher training. Therefore, this study starts from the satisfaction of teachers, which makes up for the neglect of the important influence of teachers on online training in previous studies.

A practical significance of this study is that, starting from the dominant role of teachers, it analyzes the collected data and then explores the factors that affect teachers’ satisfaction, so as to put forward various suggestions on how to improve teachers’ online training satisfaction in the post-pandemic era. First of all, among the variables of perceived quality, teachers have the lowest average satisfaction with resource quality. Therefore, online training platforms should focus on improving the quality of training resources to ensure their richness, quality, novelty, and reference. Secondly, teachers are not very satisfied with the functions of the online training platform. Therefore, the online training platform should launch personalized services to improve user satisfaction from the aspects of diversification of service types, timely service feedback, and personalized push services. Finally, as the main body of online training, teachers’ individual ability is also crucial. Therefore, schools can help teachers learn about online training by regularly organizing online training, focusing on training some young teachers, and strengthening online training quality publicity, so as to maximize teachers’ expected quality of online training and adequate learning of training resources.

A final practical implication of this study is that it provides teachers with interventions and strategies to support teachers in improving their own satisfaction with the use of online learning based on their own circumstances. By improving personal computer capabilities, teachers can also improve their perceived quality of online training in the process of using online training, which would then in turn support the improvement of teachers’ satisfaction with using online training. Improvements in satisfaction should make teachers more willing to participate in relevant training in the future, and help teachers establish the habit of sustainable learning, which would undoubtedly greatly enhance their sustainable development ability.

6.2. Limitations and Future Works

Although this study focused on the satisfaction of middle school teachers using online training and its influencing factors from the perspective of teachers, there are still some deficiencies to be improved in the follow-up research.

First, due to the limitations of time, the authors’ experience, and survey subjects, in order to ensure the feasibility and operability of the study, we only distributed questionnaires to middle school teachers in one county. Therefore, future studies should be extended to more schools or regions, so as to collect a broader and more representative sample to verify the conclusions of this study.

Second, variables can be added to the questionnaire in future studies to reduce the deviation. Although we designed the final questionnaire on the basis of referring to experts and relevant literature, due to the limitations of our own understanding of teacher training and the limited literature research on teacher online training satisfaction at present, the measurement indexes may not accurately fit the actual indexes that affect the satisfaction of online study, so relevant influencing variables should be improved and supplemented to reduce the deviation of results.