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Abstract

Tens of thousands of students worldwide have been forced to start learning online, and online learning has never been more compelling. Understanding the effectiveness and satisfaction of online learning has become crucial. Previously, researchers have explored many determinants of online learning, but so far, only a few have focused exclusively on the field of higher education in dance. Therefore, this study delved into the satisfaction derived from online dance learning by employing the stimulus–organism–response framework, social learning theory, and constructivism theory. The aim was to examine how the effectiveness of online learning and the characteristics of online systems influence satisfaction in dance online learning. Data collection took place from January 1, 2023, to January 31, 2023, with university students majoring in dance participating as respondents in China. The analysis utilized structural equation modeling for data interpretation. The results reveal that the organism (students online learning effectiveness and the online system characteristics) directly impacted students’ satisfaction with online education. While changes in four stimulus factors (learning experiences; instructor’s inspiration; learning hindrance; quality of technology) indirectly impact online learning satisfaction. Our research enriches the exploration of students’ satisfaction factors in dance higher online education, thus filling the research gap in related fields, intending to guide relevant practitioners in the future.

Plain Language Summary

Tens of thousands of students worldwide have been forced to start learning online, and online learning has never been more compelling. Understanding the effectiveness and satisfaction of online learning has become crucial. Previously, researchers have explored many determinants of online learning, but so far, only a few have focused exclusively on the field of higher education in dance. Therefore, this study delved into the satisfaction derived from online dance learning by employing the stimulus–organism–response framework, social learning theory, and constructivism theory. The aim was to examine how the effectiveness of online learning and the characteristics of online systems influence satisfaction in dance online learning. Data collection took place from January 1, 2023, to January 31, 2023, with university students majoring in dance participating as respondents in China. The analysis utilized structural equation modeling for data interpretation. The results reveal that the organism (students online learning effectiveness and the online system characteristics) directly impacted students’ satisfaction with online education. While changes in four stimulus factors (learning experiences; instructor s inspiration; learning hindrance; quality of technology) indirectly impact online learning satisfaction. Our research enriches the exploration of students’ satisfaction factors in dance higher online education, thus filling the research gap in related fields, intending to guide relevant practitioners in the future.

Keywords

online dance education satisfaction online dance education effectiveness stimulus–organism–response framework social learning theory constructivism theory structural equation modeling

Introduction

Although the impact of the Covid-19 crisis on human society is gradually shrinking, the experience and lessons it brings are enough to make people reflect deeply—this will never be the first time in human history to face a pandemic (Crosby, 1967), nor will it be the last. Therefore, people should remember the valuable and specific online learning experience and lessons gained during the crisis and use them to develop online learning activities in peacetime for future contingencies (Wenger, 1999).

Online learning has been a prominent research topic over the past few decades (Hofer et al., 2021). Nevertheless, there are few online learning programs in university education, and researchers are even less focused on online learning courses such as art or dance. According to the announcement of the Ministry of Education of China, as of 2020, the number of applicants for dance art majors in China has reached as high as 1.17 million.¹ When the pandemic crisis came, such an enormous group of students had to pivot to online education briefly; people started to think seriously about online education’s critical role in the crisis. (Bozkurt & Sharma, 2020). Meanwhile, the “Emergency Remote Learning” that this crisis has brought about presents tremendous challenges and a wealth of experience for students and instructors alike (Murphy, 2020)—never have people been forced to use online learning more widely and with such frequency (Lokken & Slimp, 2017; Williamson et al., 2020).

The COVID-19 outbreak presents numerous crises and challenges (Zarei & Mohammadi, 2022), including but not limited to the economy, environment, health, and lifestyle (Ratten, 2021). Developed countries now possess a relatively mature technology, enabling them to transition more quickly from traditional education to online learning. In contrast, developing countries need to be better equipped for this transition (Aboagye et al., 2021). Often, faulty or insufficient technology infrastructure, along with a lack of practical support, pose challenges for both students, teachers, and other stakeholders. In addition, the authors, Vahle et al., believe that the pandemic has led to a variety of norm violations and administrative policies, particularly concerning grading, that can impact students’ satisfaction when they encounter online courses (Vahle et al., 2023). Nevertheless, few scholars have discussed the opportunities and challenges faced by online learning of higher dance education in China (a vast developing country) after the epidemic crisis.

Exploring the effectiveness and satisfaction of higher dance education in China naturally becomes imminent. Scholars Eom and others believe that there are many factors that affect the effectiveness of online education. (Eom & Ashill, 2018; Karthik et al., 2019; MacLeod et al., 2019). In addition, the effectiveness and the system characteristics also directly impact student satisfaction (Tarhini et al., 2017). Although scholars have made efforts, a comprehensive analysis with a well-defined theoretical framework regarding the effectiveness and satisfaction of internet-based learning in dance is still lacking. Consequently, this study aims to fill the current research gap, systematically explore the satisfaction of dance students in higher education and explore a set of structural models and antecedents suitable for measuring online dance education.

This study consists of the following parts: Part 2 introduces the literature review, model background, and hypotheses. Part 3 outlines the research methodology. Part 4 provides an in-depth analysis of the findings. Finally, part 5 presents conclusions and other relevant findings

Literature Review

Explanation of Key Variables

Online Dancing Education

In this study, online dance education refers to the delivery of dance courses to student remotely with the assistance of technologies. Dance instructors use the existing online platforms, such as Zoom and TikTok. Instructors can use video conference or live streaming to provide instructions. The existing literature has identified a few benefits and limitations of online dance education. For instance, Bakirova et al. (2022) found that distance learning has the benefits of providing effective education to students during the COVID-19 (Bakirova et al., 2022). In another study, an exploration was conducted on the utilization of online dance education for cultivating students’ 4C skills, including creativity, communication, and others. This exploration involved gaining insights from both students and teachers within the realm of higher dance education (Z. Li et al., 2022). Moreover, You (2022) explored the prospects of applying Internet technology in dance education and, concurrently, determined the appropriate online education for students in related fields and its subsequent impact (You, 2022). The study found that online dance education training is a promising direction. Scholars pointed out that online dance learning can be a promising tool in future dance education (You, 2022). Nevertheless, due to limited research, more studies should be taken to further improve students’ satisfaction (Zarei & Mohammadi, 2022). Considering that COVID-19 is a sudden and stressful change, this study aims to integrate the theory of stimulus organism response theory and social learning theory, enrich research on online dance education, and comprehensively explore the satisfaction and effectiveness of online dance education in China. The primary research subjects for this study are college students in dance universities.

Online Learning Satisfaction

The literature exploring higher dance education satisfaction is minimal, particularly in online higher dance education. Thus, this study expanded the scope of the investigation to complete our literature review better.

Mishra et al. (2020) detail how higher education institutions have used existing resources to continuously learn lessons and make formal education more effective and satisfactory during the pandemic (Mishra et al., 2020). In addition, some scholars such as Aristovnik et al., also believe that due to the impact of the epidemic, online education is becoming more prominent and nearly permanent (Aristovnik et al., 2020); student satisfaction is higher than expected, but this has not been verified in the field of higher dance education (Maatuk et al., 2022).

Simamora et al. (2020) employed narrative analysis as a research methodology to delve into the perspectives and viewpoints of higher education lecturers concerning online education (Simamora et al., 2020). The researchers concluded that although there are some barriers to online instruction (such as insufficient Internet access) (Aljaraideh & Al Bataineh, 2019), the benefits of convenient, fast, and has excellent potential for development can improve the satisfaction level (Ramij & Sultana, 2020). At the same time, one challenge faced by education is that universities need to develop further, train and improve infrastructure can reduce the satisfaction (Kibuku et al., 2020).

Online Learning Effectiveness

Chickering and Gamson (1987) introduced seven principles aimed at enhancing the effectiveness of undergraduate education (Chickering & Gamson, 1987). Since then, many online learning effectiveness measurement indicators have been derived on this basis (Gorsky & Blau, 2009)). For instance, Bangert (2006) proposed that four factors are critical when used to evaluate online learning effectiveness (Bangert, 2006); Reyes-Fournier et al. (2020) obtained this by using 12 factors to screen Presence, Expertise, Engagement, and Facilitation are four main factors (Reyes-Fournier et al., 2020).

Although the factors for measuring online learning effectiveness are multi-perspective, constructivist learning models have long been specifically recommended as the “golden indicator” for designing (evaluating) online learning effectiveness (Bonk, 1998) (Jonassen, 2000). Therefore, this study mainly focuses on exploring dancing students’ online learning effectiveness. Regarding the research methodology, the theory-driven approach is an important stream of research as it can give high validity to studies. The theory-driven approach is also compatible with educational psychology research.

Research Hypothesis

Research Framework

The social learning theory proposed by Bandura in 1977 pointed out that social behavior is learned by observing and imitating the behavior of others (Bandura & Walters, 1977). This theory was later widely used in various fields, and of course, it also includes measuring various post-pandemic crises behavioral responses to events (X. Li et al., 2021; Yuen et al., 2022). Regarding constructivist theory, constructivist scholars generally believe that the process of learning behavior itself involves active construction (Fosnot & Perry, 1996). These two theories can aid in identifying the factors that influence online learning effectiveness. Building upon the aforementioned theories, we integrated the five dimensions proposed by Bijan Mashaw to assess the effectiveness of online courses (Mashaw, 2012). They are learning experiences, instructor’s inspiration, learning hindrance, interaction participation, and quality of technology. It’s important to highlight that the two aforementioned theories fall short in providing a complete comprehension of the subsequent behavioral outcomes arising from online learning emergencies, such as student satisfaction with suddenly introduced comprehensive online learning (unexpected situation). To address this issue, we are introducing the Stimulus-Organism-Response framework. This framework centers on how stimulus cues (e.g., learning experiences; instructor’s inspiration; learning hindrance; quality of technology) influence an organism’s thoughts and cognition (e.g., online learning effectiveness and online system characteristics), consequently eliciting emotional responses (e.g., online learning satisfaction). The theoretical model proposed in this paper is depicted below (Figure 1).

Figure 1.

Research structure.

Hypothesis Explanation

The learner’s understanding of the subject is a prerequisite for learning; the learner’s thinking attitude or belief has changed during the learning process, and his behavior or belief is in line with expectations (Moyer, 2002); when the subject of learning is aligned with value, students will show more relevance to their learning abilities (Lytras, 2007). The above learning experience has a direct impact on the E-learning effectiveness of the students. In order to measure the effectiveness of online learning for students, we need to understand the final learning effect in terms of students’ expectations of the process outcome (Moyer, 2002). That means that it is imperative to encourage or appropriately motivate students to achieve their learning goals. Therefore, Instructor’s inspiration plays a vital role, and tutors can provide students with the direction, opportunity, and motivation to “promote” learning promptly. This is a hypothesis requiring validation. Therefore, based on the preceding discussion, we present the hypothesis:

H1: Learning experience positively affects online learning effectiveness.

H2: Instructor’s inspiration positively affects online learning effectiveness.

The continuous breakthrough of modern technology explains why digital learning has become universal (Arbaugh & Benbunan-Finch, 2006). However, technology or improper communication is likely to cause unexpected difficulties. Given that one of the critical functions of online learning is to establish a conducive and convenient interactive space that enables students to engage in learning without limitations, anytime and anywhere. Many scholars believe that the technical quality of online learning is crucial in determining learning effectiveness (Bolliger & Wasilik, 2009). Technical issues and insufficient technical assistance can lead to frustration in one’s online learning experiences (Jarvenpaa & Leidner, 1999). But this has yet to be tested among online dance students. Therefore, based on the preceding discussion, we present the hypothesis:

H3: Learning hindrance positively affects online learning effectiveness.

H4: Interaction participation positively affects online learning effectiveness.

H5: The quality of technology positively affects online learning effectiveness.

Whether people have practicability, convenience, and usability of technology is a kind of perceptual experience, and these perception experiences directly affect the environmental experience in online learning—that is, online system characteristics (Chiu & Wang, 2008; Tarhini et al., 2017). However, this needs to be tested in online dance learning. Therefore, we proposed:

H6: The quality of technology positively affects online system characteristics.

Bandura pointed out in 1977 that learning (its behavior or result) is produced by interacting with cognition, behavior, and environment (Bandura & Walters, 1977). Unlike face-to-face learning, in a digital learning environment, the characteristics of the online system are one of the important factors that influence learning efficiency. The literature review found that most researchers need to measure the perception problem related to the network learning environment. However, it is an essential part of the network learning environment. Based on this, we recommend the following:

H7: The effectiveness of online learning is positively influenced by the characteristics of the online system characteristics.

Confabulation of online learning satisfaction cannot avoid the impact of online learning effectiveness and online system characteristics (Finkel et al., 2012). The foremost factors influencing students’ satisfaction with digital learning are their perception of effectiveness and their experience with online system characteristics. Consequently, building upon the aforementioned discussion, we recommend the following:

H8: The online learning satisfaction is positively influenced by the online effectiveness of learning.

H9: The online learning satisfaction is positively influenced by the online characteristics of the system.

Materials and Methods

Survey Design

The questionnaire consists of three parts. The initial part introduces the survey’s purpose and scope (for example, asking respondents whether they had ever taken online dance courses) and asked participants to clarify the background of the questionnaire. In the subsequent step, we present a comprehensive list of questionnaire questions, as detailed in Table 1. These questions are assessed using a seven-point likert scale. Additionally, we included attention-checking items in this section to ensure the credibility of the final questionnaire (e.g., “Please select the fifth answer to this question”). The final part pertains to gathering demographic information from participants.

Table 1.

Questionnaire Items.

Construct	Abbreviated	Items	Source
Learning experiences	LE1	Online dance learning is very valuable to me, and it seems to provide me with more or better personal development in the future (for example, I can have more universities to choose from, etc.).	Eom and Ashill (2018); MacLeod et al. (2019)
	LE2	The learning materials provided by the teacher in the online dance course (including but not limited to music videos and dance videos recommended by the teacher, etc.) make me feel very useful, and I have high confidence to use them well (learning Material).
	LE3	When the online dance course is over, I have full confidence to face all the next tests or entrance exams.
Learning hindrance	LH1	Online dance learning is full of technical issues, which makes me feel very stressed.	Eom and Ashill (2018); Ye et al. (2017)
	LH2	In the process of online dance learning, unexpected picture rigidity often occurs unexpectedly (this is probably caused by the system), which makes me very dissatisfied.
	LH3	I think online dance learning lacks guidance or lacks proper learning aids.
Instructor’ inspiration	II1	I think online dance learning can help the teacher to showcase his motivation skills well, which also means that I get unexpected motivation during the class.	Karthik et al. (2019); MacLeod et al. (2019)
	II2	I think online dance learning can give teachers’ timely feedback on my progress.
	II3	Online dance learning has been able to give me great assistance on a personal level.
Interaction participation	IP1	Online dance learning offers me substantial interaction with learning materials.	Eom and Ashill (2018)
	IP2	Online dance learning provides me with meaningful interaction with teachers.
	IP3	Online dance learning facilitates significant interaction among participants
Quality of technology	QT1	Online dance learning software has excellent interface design and usability.	Chiu and Wang (2008); DeLone and McLean (2003); Tarhini et al. (2017)
	QT2	The content, organization, and materials presented by online dance learning software are very attractive to me.
	QT3	There is a lot of flexibility in the study modules that I have control over.
Online learning effectiveness	OLE1	Overall, compared to face-to-face learning, online dance learning allows me to learn knowledge more quickly.	Gorsky and Blau (2009); Reyes-Fournier et al. (2020)
	OLE2	Overall, online dance instruction has allowed me to focus more than face-to-face learning.
	OLE3	Overall, I seem to have more interaction with my teacher or students with online dance learning than with face-to-face learning.
Online system characteristics	OSC1	Overall, I find the online dance learning system offered by dance schools to be highly functional.	Chiu and Wang (2008); Tarhini et al. (2017)
	OSC2	Overall, I think the online dance learning system provided by dance institutions is very easy to use.
	OSC3	Overall, when I encounter technical problems on the learning platform, someone (teacher) will help me solve the problem or provide consulting services in time.
Online learning satisfaction	OLS1	Overall, I am very satisfied with online dance learning as a way of learning.	Chiu and Wang (2008); Tarhini et al. (2017)
	OLS2	Overall, I am satisfied with online dance learning.
	OLS3	Overall, online dance learning can meet my learning needs.

The questionnaire items in the second part are displayed in Table 1. Three items related to learning experience are sourced from Eom and Ashill (2018) and MacLeod et al. (2019). Three items pertaining to learning hindrances are sourced from Eom and Ashill (2018) and Ye et al. (2017). Three items concerning instructors’ inspiration are sourced from Karthik et al. (2019) and MacLeod et al. (2019). Three items related to interaction participation are sourced from Eom and Ashill (2018). Three items about the quality of technology are sourced from Chiu and Wang (2008) and Tarhini et al. (2017). Three items regarding the effectiveness of online learning are sourced from Gorsky and Blau (2009) and Reyes-Fournier et al. (2020). Three items concerning online system characteristics are sourced from Chiu and Wang (2008) and Tarhini et al. (2017). Three items related to the satisfaction of online learning are sourced from Chiu and Wang (2008) and Tarhini et al. (2017).

Before conducting this investigation, the study underwent formal approval for ethical review by the Review Board, with permit number: XFEC-2023-020. The surveys were conducted in collaboration with Wenjuanxing, a reputable online survey company, and informed consent was obtained from individuals who completed the questionnaire.

The respondents mainly consist of college students from dance colleges and universities. They needed to have been enrolled in at least one semester (i.e., 4 months) of online dance classes and attended at least once a week. This ensured they could provide insightful feedback on the learning experience. Prior starting the survey officially, participants were asked to answer the filter questions (i.e., learning institution, online dance learning experience and frequency) to make sure the respondents fulfill the requirement. Volunteer sampling was used to collect data.

Due to the necessity of awaiting ethical permission from the institution, our questionnaire was released on January 1, 2023, and remained available until January 31, 2023, spanning a duration of 1 month. In the end, we collected a total of 900 questionnaires through the Internet. Subsequently, after excluding questionnaire answers that did not meet the aforementioned prerequisites or were incomplete, a sum of 350 complete questionnaires was ultimately acquired. We eliminated the invalid questionnaires, which were characterized by a failure to address attention checker questions. The inability to respond to these questions suggests that the respondents may not have thoroughly reviewed the instructions, potentially leading to the unreliability of their answers.

Finally, 293 valid responses were obtained, and participants were rewarded with $2 each.

Common Method Bias and Non-Response Bias

Non-response can pose challenges in self-administered surveys. To mitigate potential non-response bias, we employed a t-test to compare two groups based on their completion times. The results were in line with our expectations, and there were no significant differences between the two sets of data (Armstrong & Overton, 1977). This means that non-response bias is not a concern in this study.

Harman’s single factor analysis assessed the presence of common method bias, and the final result indicated that the single-factor model accounted for 38.5% of the total variance, which is below the 50% cutoff threshold (Podsakoff et al., 2003). This implies that common method bias was not a significant concern in this study.

Results

The gathered data underwent analysis through structural equation modeling, a process consisting of two sequential steps. Initially, a confirmatory factor analysis was employed to assess the validity and reliability of the measurement model. Subsequently, the study’s hypotheses were tested through structural model analysis. The analysis was conducted using IBM SPSS Amos.

Demographic Information

Table 2 outlines the demographic data, with 71.23% female and 28.77% male respondents. Considering the primary audience of dance courses, our survey respondents are in line with the objective facts. The age distribution reveals that a predominant portion of respondents falls below the age of 30, with a mere 0.68% of respondents surpassing the age threshold of 30 years. Consequently, it can be inferred that the respondents are predominantly young.

Table 2.

Demographic Information.

Demographic information	Frequency	Percentage
Age
15–20	94	32.19%
21–25	177	60.62%
26–30	19	6.51%
31–35	1	0.34%
>35	1	0.34%
Gender
Male	84	28.77%
Female	208	71.23%

Confirmatory Factor Analysis

As indicated in Table 3, the model fit obtained through confirmatory factor analysis is satisfactory. The Tucker-Lewis fit index (TLI = 0.943) and comparative fit index (CFI = 0.953) and both exceeding 0.90. Additionally, the standardized root mean square residual (SRMR = 0.0373) is less than 0.10, and the approximate root mean square error (RMSEA = 0.052) is also below the threshold of 0.08. (Hu & Bentler, 1999).

Table 3.

Confirmatory Factor Analysis.

Construct	Indicator	λ	AVE	CR
Learning experiences	LE1	0.762	0.551	0.786
	LE2	0.775
	LE3	0.687
Instructor’s inspiration	II1	0.794	0.678	0.863
	II2	0.83
	II3	0.845
Learning hindrance	LH1	0.745	0.586	0.800
	LH2	0.737
	LH3	0.784
Interaction participation	IP1	0.707	0.589	0.810
	IP2	0.736
	IP3	0.852
Quality of technology	QT1	0.728	0.527	0.852
	QT2	0.778
	QT3	0.696
Online learning effectiveness	OLE1	0.794	0.642	0.843
	OLE2	0.826
	OLE3	0.783
Online system characteristics	OSC1	0.833	0.663	0.745
	OSC2	0.780
	OSC3	0.829
Online learning satisfaction	OLS1	0.867	0.715	0.855
	OLS2	0.845
	OLS3	0.825

Note. Model fit indices: χ²/df ⁼ (518.9/288) = 1.80, (p < .001); TLI = 0.9437; CFI = 0.953; SRMR = 0.0373; RMSEA = 0.052;.

As portrayed in Table 3, the average variance extracted (AVE) values exceed the threshold of 0.5, while the Composite reliability (CR) values surpass the benchmark of 0.80. The structural reliability is confirmed (Hair et al., 2009).

Furthermore, the AVE values exceed the squared correlations between constructs, affirming the discriminant validity of the structure. With this confirmation, we are now prepared to proceed with structural equation modeling to test our hypotheses.

Structural Equation Modeling

The model fit statistics for the structural equation model are presented in Figure 2 and Table 4. The research model demonstrates robust model fit, χ²/df = (553.551/298 = 1.85; TLI = 0.9437; CFI = 0.953; SRMR = 0.0373; RMSEA = 0.052. The R-squared value for online learning effectiveness is 0.613, while for online learning satisfaction, it is 0.731.

Figure 2.

Structural modeling result.

All hypotheses find empirical support, with the exception of H4. H1 to H3 and H5 are substantiated at a significance level of 0.95, whereas H6 through H9 demonstrate significance at a level of 0.001.

Table 4.

Structural Equation Modeling Results.

Hypotheses	Estimates	p Value	Conclusion
Learning experiences → Online learning effectiveness	0.861	.05*	Supported
Instructor’ s inspiration → Online learning effectiveness	1.609	.05*	Supported
Learning hindrance → Online learning effectiveness	−0.114	.05*	Supported
Interaction participation → Online learning effectiveness	0.524	.149	Not supported
Quality of technology → Online learning effectiveness	1.941	.05*	Supported
Quality of technology → Online system characteristics	0.818	.000^***	Supported
Online system characteristics → Online learning effectiveness	0.767	.000^***	Supported
Online learning effectiveness → Online learning satisfaction	0.454	.000^***	Supported
Online system characteristics → Online learning satisfaction	0.557	.000^***	Supported

p < .05; ***p < .001 .

Results

The results show that the learning experience has a relatively strong positive influence on the online learning effectiveness, which reflects the importance of students’ expectations and experience of online courses during the online learning process. An outstanding course experience will bring students a higher degree of course perception. In accession, our results demonstrate that online courses are highly dependent on students’ self-regulation, so the proper motivation of instructors is crucial. Learning hindrance are less worth mentioning than the former two, which may be because students have confident anticipation and preparation for difficulties and setbacks in online courses.

In our exploration of dance higher education students, we found that interaction participation is not essential for online learning effectiveness, which is contrary to the theory drawn by some past studies (Alqurashi, 2019; Baber, 2020). Scholars generally agree that interactivity and feedback play a vital role in measuring the effectiveness of online learning. One of the explanations is that dance courses were initially highly dependent on “live performance.” Students may need proper participation experience when learning through online learning. Such problems must be discussed and resolved by teachers and students in the future. Quality the significance of technology to online learning effectiveness proves that many previous works of literature mentioned that quality of technology is the cornerstone of online learning, and education workers should attach great importance to it. Many previous studies have revealed that people’s overall quality of technology is a significant factor in determining the performance impact of online learning (Lassoued et al., 2020). This is supported by our empirical research as well. The higher the overall quality of the technology used in online learning, the more efficiently people can learn online, and vice versa. In addition, as we expected, the quality of technology has a high positive correlation with online system characteristics, which is in line with our social learning theory that learning (its behavior or result) is produced by interacting with cognition, behavior, and environment found in previous research (Bandura, 1986; Bandura & Walters, 1977). The result supports the social learning theory and indicates that a high quality technological and social interactions is favorable for students’ online dance learning.

Both online learning effectiveness and online system characteristics highly positively affect online learning satisfaction. This is consistent with previous research (Butnaru et al., 2021; Coman et al., 2020; Gorsky & Blau, 2009). The result extends the relationship to online dance learning and indicates the importance of enhancing online dance effectiveness.

Although our preliminary research attempts to reveal the positive correlation between online learning effectiveness and online system characteristics and online learning satisfaction, this non-linear relationship cannot be discussed. It is worth further research discuss research.

Conclusions

Summary of Findings

The pandemic crisis has forced millions of students worldwide to quickly switch from offline to online courses—such a “spectacle” is almost unprecedented—especially in classes such as art and dance, which previously relied heavily on face-to-face instruction. This is not the first time we have faced a pandemic in human history, nor will it be the last. When the next crisis is approaching, we should clearly understand the effectiveness and satisfaction needs of dance or art students for online learning to adapt to substantial crisis changes that may occur in the world at any time in the future.

Based on previous research, our research aims to improve the research on online learning forms of dance students in higher education by investigating the relationship between online learning effectiveness and online learning satisfaction. The novelty of our study is that we have constructed a relevant theoretical model specifically applicable to measuring dance network education by combining three theoretical foundations: social learning theory, stimulus–organ–response framework, and constructivism theory, which was very infrequent compared to previous studies.

The findings showed that the organism (students online learning effectiveness and online system characteristics) directly impacted students’ satisfaction with online learning. While changes in four stimulus factors (learning experiences; instructor’s inspiration; learning hindrance; quality of technology) indirectly impact online learning satisfaction.

Theoretical and Practical Implications

This study has important theoretical implications for the existing literature. First, we extend the application of organism-stimulus-response model to the context of online dance education. We propose a theoretical model which is based on several suitable theories to explain influential factors of online dance learning. The model is novel and can be refereed by future research. Second, we collect empirical data on online dance learning and empirically test the hypothesis. The result indicates the proposed model is well supported by empirical data and the model has good explanatory power.

The practical implications lie on the future development of online dance learning. First, online dance learning should be made accessible to students by making all functions easy to understand and operate. A proper and readable guidance should be prepared for students to understand the procedure. They shall pay attention to students’ feedback on the software and improve in time. Second, a lively discussion atmosphere should be created. The communication between students and teachers are very importance for dance learning. During the online learning, teachers should have good interactions with students. Third, teachers should try to improve the effectiveness of online dance learning and improve the competence compared with offline learning. They can fully use the technological advantage to create effective class arrangement for students.

Limitations

Our study encompasses several limitations, with the primary constraint being the exclusive administration of the survey within a single metropolis in China. Due to constraints related to time and financial resources, the authors encountered challenges in conducting surveys in other cities or countries independently. Despite efforts to ensure the representativeness of the respondents, the findings may not readily apply to other regions due to potential cultural differences. Thus, we suggest future researchers do surveys in different countries to validate the results. The second limitation pertains to the adaptation of factors derived from the realms of social learning theory, the stimulus–organ–response framework, and constructivism theory. The findings indicate that the model possesses strong explanatory capabilities. However, it’s important to note that there are still unexplored psychological and cognitive factors. Therefore, we recommend that future researchers, building upon the model we have presented, delve into more relevant factors and theories to gain a deeper understanding of online learning satisfaction in the context of dance education. The third limitation lies in our limited exploration of result heterogeneity. Consequently, we propose that researchers employ techniques such as ANOVA or other methods like controlling for variables to gain a more in-depth understanding of the underlying heterogeneity. The fourth limitation is that we could only gather pooled cross-sectional data based on the questionnaire. We did not consider the continuous situation that respondents faced over the duration of their online education. It is recommended that future researchers delve into more in-depth panel data to address this aspect.

Footnotes

Author Contributions

Na Yu has conceptualized the concept, collected data, analyzed the data, and wrote the draft. Liu Xiaolei has conceptualized the idea, wrote the draft.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

An Ethics Statement

This study has obtained a statement of ethics for human research approved by the Ethics Committee of Xinyang Normal University (committee approval number: XFEE-2023-020).

ORCID iD

Na Yu

Notes

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

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Online Dance Learning Satisfaction After the Pandemic: Lessons From the Crisis

Abstract

Plain Language Summary

Keywords

Introduction

Literature Review

Explanation of Key Variables

Online Dancing Education

Online Learning Satisfaction

Online Learning Effectiveness

Research Hypothesis

Research Framework

Hypothesis Explanation

Materials and Methods

Survey Design

Common Method Bias and Non-Response Bias

Results

Demographic Information

Confirmatory Factor Analysis

Structural Equation Modeling

Results

Conclusions

Summary of Findings

Theoretical and Practical Implications

Limitations

Footnotes

Author Contributions

Declaration of Conflicting Interests

Funding

An Ethics Statement

ORCID iD

Notes

Data Availability Statement

References