Study on the Mechanism of Online PE Learning Performance: Mediating Effect of Interactions

Study on the Mechanism of Online PE Learning Performance: Mediating Effect of Interactions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Study on the Mechanism of Online PE Learning Performance: Mediating Effect of Interactions Qi Zhang, Shan Ping Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4898477/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: With the rapid development of Internet technology and its wide application in education, online education has become an important way for people to learn knowledge in the digital era. Due to the time-space separation teachers and students, as well as their lack of face-to-face communication during online learning, it is difficult to accurately estimate the learning performance of online physical education (PE). Therefore, the relationship among teachers’ instructional activity (IA) , students’ sports activity and ability (SAA), and online PE learning performance (LP), as well as the mediating effect of interactions (IB), are mainly studied in this paper. Methods: Through the questionnaire of online PE learning activity, this paper investigated the online PE learning situation of 691 college students in Shaanxi Province. SPSS26.0 was used for description and correlation analysis of the variables, and then AMOS26.0 version was used to draw a structural equation modeling. The bias-corrected non-parametric percentage Bootstrap method (5000 repeated samples) was selected to estimate the 95% confidence interval (CI) and test the mediating effect of interactions. Results: (1) There is a significant positive correlation (p<0.001) among four variables. (2) A structural equation model with interactions as the mediating variable was established. All fitting test values of the corrected model reached the fitting standards(c 2 /df=4.494, P＜0.001, RMSEA=0.071, GFI=0.910，NFI=0.938, CFI=0.951, IFI=0.951), so that the mediating effect has been persuasive. (3) According to the corrected model path coefficients, IA can explain 30.6% and 25.6% of the variation of IB and LP, SAA can explain 32.3% and 45.8% of the variation of IB and LP, while IB can directly explain 9% of the variation of LP. (4) The effects of IA, SAA, IB, and LP did not contain 0 in the upper and lower limits of the bootstrap95% CI, indicating that the mediating effect of interactions has been persuasive. Conclusion: (1) Both teachers’ instructional activity and students’ sports activity and ability can exert a positive impact on learning performance. (2) Teachers’ instructional activity is the decisive factor for students participate in learning. Teachers can improve the quality of interactions through teaching behavior, thereby improving students’ learning performance. (3) Students’ sports activity and ability are the driving force for their participate in learning. The stronger students’ sports activity and ability are, the more active they will be in classroom interaction and the better learning performance they will get. (4) Interactions can play a crucial role in learning performance, because the occurrence of interactions can effectively enhance the initiative of students and the classroom interest. Biological sciences/Psychology Health sciences/Health care Teachers’ Instructional Activity Online Learning Classroom Interaction Sports Ability Learning Performance Figures Figure 1 Figure 2 1 Introduction In recent years, China has successively introduced multiple policies for continuously highlighting the powerful engine role of digital transformation in higher education. With the rapid development of Internet technology and its wide application in education, online education has become an important way for people to learn knowledge in the digital era (Cai,2023) [1] . Learning refers to the process in which a subject engages in a series of activities through the acquisition of a certain learning performance in daily life (Wang, 2018) [ 2] . Mohamed Ally & Wu & Zhang (2004) believed that online learning is a process in which learners can acquire necessary learning materials on the Internet, interact with teachers and peers during online, and obtain support during online learning process, so as to acquire knowledge and grow with the deepening of learning[ 3] . Online PE learning refers to a process in which learners aim to fulfil specific PE learning tasks in a certain learning group (Wang, 2009) [4] , and use the Internet information technology and external learning environment to independently completing online PE learning (Li, 2022) [5] . Online PE learning can achieve synchronous and asynchronous interactions between students and teachers, but surveys have found that there are many problems, such as low students’ participation (Shan et al., 2021) [6] , weak online technical support (Xiong et al., 2020) [7] , and learning equipment comfort defects during the online learning process (Chen et al., 2020) [8] . It has been proved that the effectiveness of online PE teaching and learning in public PE classes of a university can exert a positive impact on students (Wang, 2019) [ 9] . Moreover, the effectiveness of online PE learning can be influenced by the emotions and abilities of students (Bray et al., 2008) [ 10] , as well as the abilities and organization of teachers (Xiao, 2017) [ 11] . At first, we look back on the innovation of PE teaching. Nowadays, online education has become a new normal of teaching. Education is moving from informatization to digitalization and intelligence now. In order to enrich the curriculum system, many universities have implemented open courses for students to learn independently, and recognized their academic credits (Xue, 2023) [12] . Due to the time-space separation teachers and students, as well as the lack of their face-to-face communication and teacher supervision during online learning (Cui et al., 2022) [13] , it has been increasingly important to study how to improve students’ online PE learning performance and explore the mechanism of affecting students’ online PE learning performance. The current research on students’ learning performance mainly focuses on the impact of teaching level and post-class evaluation on learning performance. There are few cross studies on the impact of students’ sports activity and ability and interactions on learning performance. In order to adapt to the development of online PE, this study, mainly based on the mechanism of affecting students’ online PE learning performance, explores the impact of two variables, including teachers’ instructional activity (IA) and students’ sports activity and ability (SAA), on learning performance (LP), as well as the mediating effect of interactive behavior (IB). Furthermore, this study can optimize the PE classrooms through the construction of teacher-student relationship, thus providing a reference and a practical basis for the integrated PE development in the future. 2 Theoretical Basis Online learning is guided by Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), and Technology Acceptance Model (TAM). It is believed in the TRA that an individual’s implementation of a certain action is determined by his or her action intention, which is jointly determined by his or her attitude towards the action and subjective norms. On the basis of these theories, an online learning model can be established. In the study of online activity models, Li and Hill both put forward a model of online learning activity for college students. According to the model, commitment, action, learning atmosphere, learning attitude (Li et al., 2012) [14] , self-efficacy, systematic knowledge, and previous knowledge level (Hill et al., 1997) [15] can jointly affect online learning. Scholar Ma Zhiqiang considered cognitive, emotional, and behavioral engagements in the theory of engagement as important dimensions to measure learning engagement, and discussed that these dimensions can predict students’ academic performance (Ma et al., 2017) [16] . Based on the New Constructivist Learning Theory and the Behavioral Science Theory, through analyzing the data recorded on online teaching platforms, scholars summarized the recording modes and collection elements of online learning activity, and collected the impact of online learning activity on learning performance (Liu, 2017; Huang, 2019) [17-18] . Relevant studies based on different theories were all aimed at verifying that there are many factors affecting learning performance, and affirming the importance of online learning activity on learning performance. Nowadays, many studies have paid more attention to the extraction of students’ own observable variables, and conducted more detailed and consistent analysis on their learning activity. The above studies are rich in theoretical support. The action theory can provide support for the students’ interactive trends in this paper. The subjective factors mentioned by scholars, such as learning attitudes and knowledge reserves, can provide ideas for the setting of students’ sports activity and ability in this paper. The development of Internet+ education has promoted the connection and integration of PE teaching with the era. Online and offline blended learning has been the foundation for the development of PE teaching in colleges and universities in the new era. Scholars tend to explore solving the problem of how the PE teaching mode in colleges and universities under the background of information technology development can be applied to online education. The PE teaching process can be roughly divided into three parts: pre-class, in-class, and post-class, so that the Internet+ PE teaching mode in colleges and universities is mainly constructed from three aspects: teaching preparation before class, mutual learning and inquiry in class, and feedback evaluation after class (Li et al., 2018) [19] . Zhang et al. proposed the 020 mode, with the Internet technology to realize the study and exchange between teachers and students on network teaching platforms, and built a new teaching mode of two-way interaction between online teaching and offline exchange (Liu, 2018) [20] , which has been applied to the PE practice in colleges and universities. The 020 mode was divided into three phases: preparation, teaching, and evaluation, and then the teaching phase was further divided into pre-class, in-class, and post-class (Zhang et all., 2020) [21] . The pre-class, in-class, and post-class activities of teachers have been adopted in this paper. Related surveys have found that there is no ideal interaction between teachers and students in online PE classes. A teacher gives a wonderful lesson, while students remain indifferent. In addition, a teacher may perform attentively, while students fall asleep. Some students even put their phones aside and do other things by themselves (Guo et al., 2020) [22] . Foreign studies have found that students and teachers were less familiar with online teaching and learning during the process of online PE instruction. Moreover, students have expressed that there were limitations in the learning process, but the PE videos were reused for daily learning (Laar R. A. et al., 2021) [23] . Students have also reported some problems with participating in online PE classes, such as the impact on sports performance, the limited scope of physical activities, and the inability to properly transfer the core values of PE. Generally speaking, research on online PE learning is still incomplete. Due to the PE particularity, with obvious and specific activity tendencies of students, more research space has been given to scholars. It is the specific problem to be solved in this paper that teachers observe the activity tendencies of students, and then track and adjust them in teaching, in order to create a harmonious and efficient PE classroom. 3 Methods 3.1 Participants In this study, a stratified random sampling was used to select non-PE major students from 10 colleges and universities in Shaanxi Province for online PE instruction during the COVID-19 epidemic. There were 80 freshmen, sophomores and junior college students in each university, with the ratio 1:1 of male and female students and a total sample size of 800. In the form of survey questionnaires, 800 questionnaires were distributed, 109 invalid questionnaires were eliminated, and 691 valid questionnaires were finally collected, with an effective rate of 86.4%. There were 330 (47.8%) male students and 361 (52.2%) female students. The valid questionnaires included 522 (75.5%) freshmen, 142 (20.5%) sophomores, and 27 (4%) specialist students. 3.2 Instruments (1) Teachers’ Instructional Activity Scale A total of three items in the questionnaire were used to evaluate teachers’ online PE instructional activity in three aspects: pre-class, in-class, and post-class. The items include a 5-point Likert scale, ranging from “strongly disagree” to “strongly agree”, with corresponding scores from 1 to 5. The higher the score is, the better teachers’ instructional activity and the more comprehensive the teaching process will be. (2) Students’ Sports Activity and Ability Scale This questionnaire, consisting of 6 items, was used to evaluate students’ sports activity and ability. The items include a 5-point Likert scale, ranging from “strongly disagree” to “strongly agree”, with corresponding scores from 1 to 5. The higher the score is, the stronger the students’ sports activity and ability will be. (3) Online Sports Interactive Behavior Scale The scale of “PE Learning Interactive Behavior” by Yan (2019) was used in this study to measure the learning interactive of participants according to the dimensional standards of PE learning activity [ ] . Based on the Delphi method, the scale can continuously correct observed indicators. Through three rounds of Delphi method, the variation coefficient of each indicator can reach 0.4 or below. The P-values are all less than 0.001 during the three rounds of expert consultation, indicating a high credibility in the coordination of the three rounds of expert consultation. Interactive behavior is mainly composed of four items, which measure learner-teacher interaction, learner-learner interaction and learner-content interaction respectively. The scale is scored by 5-level Likert. The five alternative answers range from “never” to “always”, with corresponding scores from 1 to 5. The higher the score is, the better the participant interactions will be. (4) Online PE Learning Performance Scale The survey on learning performance includes five aspects: emotions, knowledge, skills, physical fitness (Liu et al., 2017) [25] , and achievement (Hu et al., 2020) [26] . Students can evaluate their learning performance from five aspects: sports emotions, sports knowledge, sports skills, physical health, and academic performance. This questionnaire items includes a 5-point Likert scale, ranging from “very inconspicuous” to “very conspicuous”, with corresponding scores from 1 to 5. The higher the score is, the better online PE learning performance of students will be. Reliability analysis and factor analysis were used to test the consistency and validity of the questionnaire items (see Table 1). After calculation, the Cronbach’s α values for various dimension in the questionnaire ranged from 0.827 to 0.948, with the KMO values for validity from 0.738 to 0.888, indicating that the reliability and validity of these questionnaires are relatively reliable. Table 1 The reliability and validity results of online PE learning activity Variables Measurement Items Cronbach’s α KMO Instructional Activity 3 0.880 0.738 Sports Activity and Ability 6 0.827 0.851 Interactive Behavior 4 0.897 0.803 Learning Performance 5 0.948 0.888 3.3 Data Processing SPSS 26.0 was used for description and correlation analysis on the data in this study, and the significance level of all indicators was set at α= 0.05. In this paper, teachers’ instructional activity and students’ sports activity and ability were set as the independent variables, students’ interactions in online PE classrooms were the mediating variable, and online PE learning performance was the dependent variable. As the first step in data analysis, we observed the characteristics of the data. According to the criterion, “the absolute values of skewness and kurtosis≤1 indicates that the data conforms an approximate normal distribution”, the results have shown that further data analysis can be carried out. In addition, SPSS was used to calculate standardized data for each variable. Afterwards, the Structural Equation Modeling (SEM) was drawn in the AMOS26 version for conducting a mediating effect analysis on the questionnaire data. The bias-corrected non-parametric percentage Bootstrap method (5000 repeated samples) was selected to estimate the 95% confidence interval (CI) and test the mediating effect of interactions (Preacher, K. J. et al., 2004) [27] . If the 95% CI of the mediating effect does not include 0, it can indicate that the mediating effect is significant. 4 Results 4.1 Common method deviation test This study used the self-report approach to collect data, which may lead to errors in common methods. On the basis of procedural control for the possible errors in common methods (such as filling in wrong time, reverse scoring of some items, etc.), Harman’s one-factor analysis was further used to test the errors in common methods (Zhou et al., 2004) [ 28] . The results of exploratory factor analysis extracted a total of four factors with characteristic roots greater than 1. The maximum factor variance of interpretation was 23.510%, less than the critical standard of 40% (Tang, D. D. and Zhong, Y., 2020) [29] , indicating that the errors in common methods has been within an acceptable range. Therefore, there is no serious general error in the survey data of this study. 4.2 Description and Correlation According to the mean and standard deviation of each variable and the Pearson correlation analysis results (see Table 2 ), the score of teachers’ instructional activity was 4.07, close to the full score of 5, indicating that teachers can ensure the integrity of teaching links during the process of online PE instruction, and better interact with students in the three links. In terms of students’ sports activity and ability, the average score was 3.59, above the medium level, indicating that there is still a large space for improvement in college students’ sports activity and ability. Students’ interactive behavior score was 3.42, slightly higher than the median value, indicating that college students have had poor interactive experience during the process of online PE learning. Students’ online PE learning performance was above the average score (3.66), indicating that students have had a positive attitude towards the improvement of their own learning performance after online PE instruction, but the teaching objective of comprehensive education have not been yet achieved. As seen from the correlation analysis of instructional activity, sports activity and ability, interactive behavior, and online PE learning performance (see Table 2 for details), teachers’ instructional activity is significantly positively correlated with students’ interactive behavior and online PE learning performance, respectively (p < 0.001). Students’ sports activity and ability are significantly positively correlated with students’ interactive behavior and online PE learning performance (p < 0.001). Additionally, students’ interactive behavior are also significantly positively correlated with their online PE learning performance (p < 0.001). Table 2 Correlation matrix of instructional activity, sports activity and ability, interactive behavior, and online PE learning performance Instructional Activity Sports Activity and Ability Interactive behavior Learning Performance Instructional Activity 1 Sports Activity and Ability 0.473*** 1 Interactive behavior 0.307*** 0.434*** 1 Learning Performance 0.482*** 0.554*** 0.408*** 1 Mean 4.07 3.59 3.42 3.66 SD 0.702 0.691 0.888 0.86 ***在0.001级别(双尾), 相关性显著。***At 0.001 level (double-tailed), the correlation is significant. 4.3 Establishment of Structural Equation Modelling On the basis of relevant analysis, the structural equation modelling was established with teachers’ instructional activity and students’ sports activity and ability as the independent variables, online PE learning performance as the dependent variable, and interactions as the mediating variable. The mechanism of affecting online PE learning performance under the mediating effect of interactive behavior was further explored, and a mediating model of interactions was then constructed (see Fig. 1 for details), with the fit indices shown in the table. The results have showed that various fit indices of the model are satisfactory (χ 2 /df = 5.900, P<0.001, RMSEA = 0.084, GFI = 0.882, AGFI = 0.844, NFI = 0.917, CFI = 0.930, IFI = 0.930). 4.4 Correction of Structural Equation Modelling Based on the results of initial model path, the model was corrected. According to the MI error correction model indices, cointegration analysis was conducted on the variables to discover cointegration relationships between variables. The error correction term was considered as an explanatory variable, and a short-term model was then established together with other explanatory variables reflecting short-term fluctuations. As a result, it was found that there was a correlation between the two items in the interactions, and the model were then corrected (see Fig. 2 ). The fitting test values of the corrected structural equation modelling (hereinafter referred to as “the corrected model”), which can reflect the mechanism of affecting online PE learning performance, were compared with the initial model test results and the fitting standard values to obtain the degree of fitting of the corrected model (see Table 3 below). The results can indicate that all fit indices of the corrected model have been improved to reach the fitting standard values (χ 2 /df = 4.494, P<0.001, RMSEA = 0.071, GFI = 0.910, NFI = 0.938, CFI = 0.951, IFI = 0.951) Table 3 The fitting test values of the corrected model Fitting Indicators CMIN/DF RMSEA GFI NFI IFI CFI Fitting Standard < 5 0.9 > 0.9 > 0.9 > 0.9 Model Fitting 5.900 0.084 0.882 0.917 0.930 0.930 Corrected Model Fitting 4.494 0.071 0.910 0.938 0.951 0.951 The test results for the initial model path coefficient and the corrected model path coefficient are shown in Table 4 . Both the initial model path coefficient and the corrected model path coefficient have shown that the five paths can exert a significant positive impact. With the corrected model path coefficient as an example, instructional activity has a positive and direct impact on interactions (P < 0.001). Under null hypothesis, the standardized effect value was 0.306, indicating that instructional activity can directly explain 30.6% of the variation in interactions. Instructional activity also has a positive and direct impact on learning performance (P < 0.001). Under null hypothesis, instructional activity can directly explain 25.6% of the variation in learning performance. Sports activity and ability have a positive and direct impact on interactions and learning performance (P < 0.001). Under null hypothesis, sports activity and ability can directly explain 32.3% and 45.8% of the variation in interactions and learning performance. Interactions have a positive and direct impact on learning performance (P < 0.05); under null hypothesis, interactions can directly explain 9% of the variation in learning performance. Table 4 Path analysis between factors in model fitting Path Relationship Standardized Path Coefficient S.E. C.R. p Before After Before After Before After Before After IA ➡ IB 0.305 0.306 0.047 0.049 6.726 6.701 *** *** IA ➡ LP 0.256 0.256 0.050 0.050 6.225 6.234 *** *** SAA ➡ IB 0.340 0.323 0.058 0.059 7.171 6.820 *** *** SAA ➡ LP 0.457 0.458 0.067 0.067 9.609 9.692 *** *** IB ➡ LP 0.090 0.089 0.047 0.045 2.241 2.277 * * The bias-corrected Bootstrap method (5000 repeated samples) was further selected to test the mediating effect of interactions (see Table 5 ). The direct effects of teachers’ instructional activity and students’ sports activity and ability on online PE learning performance did not contain 0 in the upper and lower limits of the bootstrap 95% CI, indicating that teachers’ instructional activity and students’ sports activity and ability can directly affect online PE learning performance. The effect of interactions on online PE learning performance did not contain 0 in the upper and lower limits of the bootstrap 95% CI, indicating that interactions can affect online PE learning performance. The indirect effects of teachers’ instructional activity and students’ sports activity and ability on online PE learning performance, namely the mediating effect of interactions did not contain 0 in the upper and lower limits of the bootstrap 95% CI, can indicate that interactions play a partial mediating role. Moreover, teachers’ instructional activity and students’ sports activity and ability can affect online PE learning performance through the mediating effect of interactions. Table 5 Mediating effect test of the corrected model Paths Effect Value SE bias-corrected95%CI Lower Upper IA ➡ LP 0.266 0.039 0.189 0.340 SAA ➡ LP 0.426 0.041 0.343 0.505 IB ➡ LP 0.141 0.039 0.061 0.218 IA ➡ IB ➡ LP 0.017 0.008 0.005 0.038 SAA ➡ IB ➡ LP 0.062 0.018 0.028 0.099 5 Discussion 5.1 Teachers’ instructional activity is the decisive factor for students to participate in learning Teachers’ instruction is explained as pre-class, in-class, and post-class links, and their instructional activity should be carried out throughout these links. It has been proven that their instructional activity can directly affect the learning performance of students. Instructional activity refers to a two-way process between teachers and students, in which the instruction of teachers, as the knowledge transmitters in teaching, can directly affect the learning process of students (Li et al., 2020) [30] . Previous studies have shown that students’ perception of teaching in class and teachers’ emphasis on instruction are the main factors contributing to differences in teaching satisfaction. Teachers’ instruction characteristics, personal qualities, and instructional involvement can exert an impact on such factors as students’ perception, motivation, and ability, all of which are intrinsic factors that affect students’ learning performance (Patrick，H., 2004; Shea P. et al., 2006) [31-32] . Not only that, but we also tested that teachers can engage in teaching interaction through three phases, including pre-class, in-class, and post-class, in order to improve teacher-student interaction and student-student interaction and then enhance students’ learning performance. It can be seen that the attitude, preparation, and engagement of teachers towards classroom greatly affect students’ learning performance. In terms of the research on teachers’ instructional activities before, in, and after class, Yang and Sun (2022) proposed that teachers should promote students’ preview activity before class, and use instructive language to stimulate students’ potential for self-regulated learning. In addition, teachers should increase the interest and professionalism of instructional design during in-class link for encouraging students to engage in interactions. Furthermore, timely Q&A and evaluation by teachers after class can improve students’ self-regulated learning (Yang et al., 2022) [33] , which has also been confirmed in the study by Feng et al. (2022) [34] . When teaching PE online, teachers need to have certain transferability of knowledge and skills, and are able to control the curriculum from different perspectives. For students of different majors, different levels and different personalities, teachers’ instructional design should be comprehensive and wide in coverage, and they should combine different perspectives to explain and demonstrate their skills. Especially on the Internet, it is particularly critical for how teachers mobilize the enthusiasm of students, which can further test their comprehensive teaching capacity. During the process of online teaching, we are more inclined to the teacher-led and student-dominated concept. First of all, as teaching dominant holders, teachers can create a stable and positive teaching environment, and select appropriate learning materials for students to participate in classroom, establish learning motivation, and engage self-regulated learning (Jen Hwang and Yue Zhu, 2020) [35] . Secondly, the solid relationship between teachers and students can not only exert a greater impact on students than academic performance, but also has a potential impact on students’ lives and personalities. It has been proven that teachers’ professional literacy and the teacher-student relationship can make students happier, healthier, and physically stronger (Leaming, K. G., 2020; Lavoie, B., 2022) [36][37]. Teachers should not only have the ability to master modern educational technology and allocate each teaching link in a scientifical and reasonable manner (Wan, M., 2016) [38] , but also reasonably arrange the content and form of pre-class, in-class and post-class teaching activities, thereby ensuring the attention and participation of students in each learning phase and support the effective learning of students (Goodyear V. et al., 2015) [39] . Finally, there are some students who have achieved achievements in completely independent learning during the teaching process, but such situations are actually rare. Many students still need to participate in PE classroom interaction and adjust their learning activities under the guidance of teachers. Therefore, students’ participation in classroom depends on teachers’ instructional activity during the teaching process, so that teachers’ instructional activity is the decisive factor for students’ active participation in classroom. 5.2 Students’ sports activity and ability are the driving force for their participate in learning According to the particularity of online PE teaching, students are required to have self-regulated learning ability and stronger self-discipline. It has been found in this study that students’ sports activity and ability can exert a greater impact on learning performance, indicating that learning autonomy plays a more important role in online PE learning-situated context. The better students’ sports activity and ability are, the better their learning performance will be. Cho, M.& Kim B. J (2013) 40 confirmed that autonomy and self-supervision can play a greater and more significant role in online learning-situated context. Online learning realizes communication and interaction between teachers and students in different time and space, so that this learning-situated context requires the engagement of students’ own learning ability and self-control. Especially in online PE classrooms, skill display and error correction cannot be carried out on-site, and the sense of teaching presence is weakened. Additionally, the temptation of the Internet has become a major factor that distracts students’ attention. Therefore, students should give their subjective initiative into the learning process, and adjust their learning activity as their motivation to carry out learning. Paris S G & Paris A H(2001), Zimmerman BJ & Schunk D H. (2011) proved that learners with strong autonomous learning ability are more able to regulate and monitor their learning process in the learning environment [41-42] . According to this study, students with strong sports activity and ability can more effectively overcome tempting interfaces, participate in PE classroom interactions, and obtain good learning performance in online learning. In addition, Pintrich (1999), Bandura (2012), and Vancouver (2018) confirmed in their studies that students’ sports ability can have a certain regulatory effect on their cognitive and behavioral development during the learning process, and this ability, as a key component for generating learning strategies, can provide emotions and beliefs for students to engage in learning activities [43, 44, 45] . It can be indicated that, on the one hand, students’ sports activity and ability can act as a prerequisite to provide motivation for their own engagement in the classroom context. On the other hand, it can create a connection between students and course performances, and then adjust their learning activities. Students with autonomous learning and sports abilities have better learning strategies, which can effectively enhance their learning performance to a certain extent. When teachers build a loving, respectful and warm learning environment, students can give full play to their own autonomous learning abilities and subjective initiatives. Moreover, through collaborating with teachers’ instruction and guidance, students can gradually develop and participate in self-regulated learning, so as to promote the improvement of teaching effectiveness. 5.3 Interactions plays a crucial role in learning performance The online live learning form puts forward a higher demand for communication and exchange between teachers and students. As for the strength of mutual response in online classroom, teachers should consciously create a good problem situation and an interactive classroom atmosphere, and assist students to participate in class actively and absorb, digest and construct knowledge quickly (Cui et al., 2022) [46] . In addition, students need to actively participate in the classroom, pay a higher emotional value, and overcome the temptation of the Internet. The interaction between teachers and students can exert a positive impact on teaching quality and satisfaction, so that the interaction is called a dialogue response, namely the degree of interaction and communication, and mutual response between teachers and learners. Interaction can deepen students’ understanding of knowledge and improve teaching quality. Anderson T (2003) proposed that online interaction includes Learner-Content (LC), Learner-Teacher (LT), and Learner-Learner (LL) pluralistic interactions [47] . First of all, the occurrence of classroom interaction depends on teachers’ implementation of instructional design and methods, which is regarded as the prerequisite for learners to engage in interactions. In view of classroom interaction in PE teaching, teachers need to arrange interactive links in a proper way, stimulate students’ interest in learning, and guide the interaction between students. For example, students can be divided into skill demonstration groups, in which students with strong sports activity and ability lead other students to participate in the class and exert their subjective status. Secondly, the occurrence of classroom interaction depends on students’ own knowledge and skill reserves. In this study, it has been confirmed that students with stronger sports activity and ability are more actively involved in classroom interaction. Students’ learning activity and ability can be shaped, so that with sufficient support, students can improve their self-regulated learning (Theobald M., 2021; Jansen R. S. et al., 2019) [48, 49] . Most of the students with strong activity and ability are more passionate about sports, and more willing to learn knowledge and acquire skills from the PE classroom. Therefore, for the generation of interactions, it is necessary for teachers to pay attention to this group of strong students, and guide the other group of weak students into the classroom through them, so that more students can profit from online PE classroom. PE classroom interactions can play a crucial role in learning performance, which results from the collaboration between students and teachers (Kuo Y. C. et al., 2014; Hone K. S. et al., 2016) [50-51] . Interactions are beneficial for students to generate a positive learning experience, so that they can build knowledge frameworks, absorb the strengths of others, and enhance their own abilities during the interactions. Teachers should construct a platform for interconnection and value sharing, through which students are first associated with the course to obtain first-hand information from it. Secondly, students are associated with teachers. During the learning process, learners raise questions to teachers and respond to the questions raised by teachers in class. In this way, teachers are required to be very enthusiastic and patient throughout the entire process. The study of Li and Zhong (2020) proved that the more teachers’ input into interactions is, the better the corresponding learners’ learning performance will be [52] . Finally, learners are associated with each other. Through the learner-learner interaction, learners can alleviate their negative emotions and increase teaching engagement (Wang, 2021) [53] . The connection between students is closer than that between teachers and students. As a result, students are able to interact with other peers in a more relaxed and pleasant emotion. Therefore, teachers should pay more attention to the learner-learner interaction in their instructional design, and build an interactive bridge between learners. The learning interactions between learning communities refer to the core process of online learning, and the generation of interactions can inevitably affect learning performance. The generation of good interaction relationship is conducive to the advancement of classroom instruction. As a mediating effect, interactions can bridge the gap between teacher-student relationship and learning performance, and exert an important impact on learning performance through the joint efforts of teachers and students. 6 Conclusions and Suggestions 6.1 Conclusions Under the background of the information era, the AMOS model was used in this study to investigate the relationship among teachers’ instructional activity, students’ sports activity, and learning performance, as well as the mediating role of interactions. From the correlation analysis of teachers’ instructional activity, students’ sports activity and ability, interactions, and online PE learning performance, the better the teachers’ instructional activity is, the more active the students’ interaction in class and the better the online learning performance will be. In addition, the more the students’ sports activities and the stronger their sports abilities are, the easier it is for them to actively engage in interactions, and the better the accompanying learning performance will be. The following conclusions can be drawn in this paper. Firstly, teachers’ instructional activity is the decisive factor for students’ participation in classroom. Teachers can intervene in classroom interaction through their instructional activities, thereby improving students’ learning performance. Secondly, students’ sports activity and ability are the driving force for their engagement in classroom instruction. The stronger the students’ sports activity and ability are, the more active their engagement in classroom interaction and the better their learning performance will be. Finally, classroom interaction plays a crucial role in learning performance, because the occurrence of interactions can effectively enhance the initiative of students and the classroom interest. Students’ interactions in classroom can play a mediating role in the connection between teachers’ instructional activity and students’ learning performance, as well as in the connection between students’ sports activity and ability and their learning performance. 6.2 Suggestions Teaching is an important way to cultivate students in colleges and universities. Teachers’ instructional activity can exert a significant impact on students’ online learning performance. At the teacher level, they should conduct their instructional activities in class and improve their teaching levels, which are beneficial for better classroom effects. Moreover, it is essential for teachers to raise moral constraints on themselves, and stress teaching techniques, including pre-class preparation, in-class arrangement, and post-class feedback. In terms of teaching approaches, classroom discussions, situational teaching, and case teaching can be used to stimulate students’ enthusiasm in online classrooms and improve teaching effectiveness. Greater importance should be attached to the classroom feedback from students, their interest in participation, and the establishment of good teacher-student relationship. Believe in what your teachers teach, and follow their instructions. This constructive interaction between teaching and learning can not only affect students’ learning activity in a positive manner, but also motivate teachers to do a better job. However, teachers in colleges and university are confronted with a dual pressure of scientific research and teaching tasks now. Hence, necessary measures should be taken in colleges and universities to enable teachers to spend more time and efforts on teaching. Furthermore, students’ sports activity and ability can also play an important role in learning performance. In addition to enhancing the teaching level of teachers, colleges and universities should actively carry out the popularization of sports activities and health education, create a positive sports atmosphere, fully motivate students’ willingness and engagement in sports, effectively improve their sports abilities and initiatives, and achieve the improvement of their sports activities and abilities. Declarations 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. Ethics statement The studies involving humans were approved by Chang an University (China). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Author contributions Q.Z, S.C contributed to the conception and design of the study. QZ performed the data collection, developed evaluation tools, and wrote the manuscript. QZ and SC performed the data analysis. QZ, SC revised and significantly contributed to the final version of the manuscript. All authors contributed to the article and approved the submitted version. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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With the rapid development of Internet technology and its wide application in education, online education has become an important way for people to learn knowledge in the digital era (Cai,2023)\u0026nbsp;\u003csup\u003e[1]\u003c/sup\u003e. Learning refers to the process in which a subject engages in a series of activities through the acquisition of a certain learning performance in daily life (Wang, 2018)\u0026nbsp;[\u003csup\u003e2]\u003c/sup\u003e. Mohamed Ally \u0026amp; Wu \u0026amp; Zhang (2004) believed that online learning is a process in which learners can acquire necessary learning materials on the Internet, interact with teachers and peers during online, and obtain support during online learning process, so as to acquire knowledge and grow with the deepening of learning[\u003csup\u003e3]\u003c/sup\u003e. Online PE learning refers to a process in which learners aim to fulfil specific PE learning tasks in a certain learning group (Wang, 2009)\u0026nbsp;\u003csup\u003e[4]\u003c/sup\u003e, and use the Internet information technology and external learning environment to independently completing online PE learning (Li, 2022)\u0026nbsp;\u003csup\u003e[5]\u003c/sup\u003e. Online PE learning can achieve synchronous and asynchronous interactions between students and teachers, but surveys have found that there are many problems, such as low students\u0026rsquo; participation (Shan et al., 2021)\u0026nbsp;\u003csup\u003e[6]\u003c/sup\u003e, weak online technical support (Xiong et al., 2020)\u0026nbsp;\u003csup\u003e[7]\u003c/sup\u003e, and learning equipment comfort defects during the online learning process (Chen et al., 2020)\u0026nbsp;\u003csup\u003e[8]\u003c/sup\u003e. It has been proved that the effectiveness of online PE teaching and learning in public PE classes of a university can exert a positive impact on students (Wang, 2019)\u0026nbsp;[\u003csup\u003e9]\u003c/sup\u003e. Moreover, the effectiveness of online PE learning can be influenced by the emotions and abilities of students (Bray et al., 2008)\u0026nbsp;[\u003csup\u003e10]\u003c/sup\u003e, as well as the abilities and organization of teachers (Xiao, 2017)\u0026nbsp;[\u003csup\u003e11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAt first, we look back on the innovation of PE teaching. Nowadays, online education has become a new normal of teaching. Education is moving from informatization to digitalization and intelligence now. In order to enrich the curriculum system, many universities have implemented open courses for students to learn independently, and recognized their academic credits (Xue, 2023)\u0026nbsp;\u003csup\u003e[12]\u003c/sup\u003e. Due to the time-space separation teachers and students, as well as the lack of their face-to-face communication and teacher supervision during online learning (Cui et al., 2022)\u0026nbsp;\u003csup\u003e[13]\u003c/sup\u003e, it has been increasingly important to study how to improve students\u0026rsquo; online PE learning performance and explore the mechanism of affecting students\u0026rsquo; online PE learning performance.\u003c/p\u003e\n\u003cp\u003eThe current research on students\u0026rsquo; learning performance mainly focuses on the impact of teaching level and post-class evaluation on learning performance. There are few cross studies on the impact of students\u0026rsquo; sports activity and ability and interactions on learning performance. In order to adapt to the development of online PE, this study, mainly based on the mechanism of affecting students\u0026rsquo; online PE learning performance, explores the impact of two variables, including teachers\u0026rsquo; instructional activity (IA) and students\u0026rsquo; sports activity and ability (SAA), on learning performance (LP), as well as the mediating effect of interactive behavior (IB). Furthermore, this study can optimize the PE classrooms through the construction of teacher-student relationship, thus providing a reference and a practical basis for the integrated PE development in the future.\u003c/p\u003e"},{"header":"2 Theoretical Basis","content":"\u003cp\u003eOnline learning is guided by Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), and Technology Acceptance Model (TAM). It is believed in the TRA that an individual’s implementation of a certain action is determined by his or her action intention, which is jointly determined by his or her attitude towards the action and subjective norms. On the basis of these theories, an online learning model can be established. In the study of online activity models, Li and Hill both put forward a model of online learning activity for college students. According to the model, commitment, action, learning atmosphere, learning attitude (Li et al., 2012) \u003csup\u003e[14]\u003c/sup\u003e, self-efficacy, systematic knowledge, and previous knowledge level (Hill et al., 1997) \u003csup\u003e[15]\u003c/sup\u003e can jointly affect online learning. Scholar Ma Zhiqiang considered cognitive, emotional, and behavioral engagements in the theory of engagement as important dimensions to measure learning engagement, and discussed that these dimensions can predict students’ academic performance (Ma et al., 2017) \u0026nbsp;\u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBased on the New Constructivist Learning Theory and the Behavioral Science Theory, through analyzing the data recorded on online teaching platforms, scholars summarized the recording modes and collection elements of online learning activity, and collected the impact of online learning activity on learning performance (Liu, 2017; Huang, 2019) \u0026nbsp;\u003csup\u003e[17-18]\u003c/sup\u003e. Relevant studies based on different theories were all aimed at verifying that there are many factors affecting learning performance, and affirming the importance of online learning activity on learning performance. Nowadays, many studies have paid more attention to the extraction of students’ own observable variables, and conducted more detailed and consistent analysis on their learning activity. The above studies are rich in theoretical support. The action theory can provide support for the students’ interactive trends in this paper. The subjective factors mentioned by scholars, such as learning attitudes and knowledge reserves, can provide ideas for the setting of students’ sports activity and ability in this paper.\u003c/p\u003e\n\u003cp\u003eThe development of Internet+ education has promoted the connection and integration of PE teaching with the era. Online and offline blended learning has been the foundation for the development of PE teaching in colleges and universities in the new era. Scholars tend to explore solving the problem of how the PE teaching mode in colleges and universities under the background of information technology development can be applied to online education. The PE teaching process can be roughly divided into three parts: pre-class, in-class, and post-class, so that the Internet+ PE teaching mode in colleges and universities is mainly constructed from three aspects: teaching preparation before class, mutual learning and inquiry in class, and feedback evaluation after class (Li et al., 2018)\u0026nbsp;\u003csup\u003e[19]\u003c/sup\u003e. Zhang et al. proposed the 020 mode, with the Internet technology to realize the study and exchange between teachers and students on network teaching platforms, and built a new teaching mode of two-way interaction between online teaching and offline exchange (Liu, 2018)\u0026nbsp;\u003csup\u003e[20]\u003c/sup\u003e,\u0026nbsp;which has been applied to the PE practice in colleges and universities. The 020 mode was divided into three phases: preparation, teaching, and evaluation, and then the teaching phase was further divided into pre-class, in-class, and post-class (Zhang et all., 2020)\u0026nbsp;\u003csup\u003e[21]\u003c/sup\u003e. The pre-class, in-class, and post-class activities of teachers have been adopted in this paper.\u003c/p\u003e\n\u003cp\u003eRelated surveys have found that there is no ideal interaction between teachers and students in online PE classes. A teacher gives a wonderful lesson, while students remain indifferent. In addition, a teacher may perform attentively, while students fall asleep. Some students even put their phones aside and do other things by themselves (Guo et al., 2020) \u003csup\u003e[22]\u003c/sup\u003e. Foreign studies have found that students and teachers were less familiar with online teaching and learning during the process of online PE instruction. Moreover, students have expressed that there were limitations in the learning process, but the PE videos were reused for daily learning (Laar R. A. et al., 2021) \u003csup\u003e[23]\u003c/sup\u003e. Students have also reported some problems with participating in online PE classes, such as the impact on sports performance, the limited scope of physical activities, and the inability to properly transfer the core values of PE. Generally speaking, research on online PE learning is still incomplete. Due to the PE particularity, with obvious and specific activity tendencies of students, more research space has been given to scholars. It is the specific problem to be solved in this paper that teachers observe the activity tendencies of students, and then track and adjust them in teaching, in order to create a harmonious and efficient PE classroom.\u003c/p\u003e"},{"header":"3 Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Participants\u003c/h2\u003e\n \u003cp\u003eIn this study, a stratified random sampling was used to select non-PE major students from 10 colleges and universities in Shaanxi Province for online PE instruction during the COVID-19 epidemic. There were 80 freshmen, sophomores and junior college students in each university, with the ratio 1:1 of male and female students and a total sample size of 800. In the form of survey questionnaires, 800 questionnaires were distributed, 109 invalid questionnaires were eliminated, and 691 valid questionnaires were finally collected, with an effective rate of 86.4%. There were 330 (47.8%) male students and 361 (52.2%) female students. The valid questionnaires included 522 (75.5%) freshmen, 142 (20.5%) sophomores, and 27 (4%) specialist students.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Instruments\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003e(1) Teachers\u0026rsquo; Instructional Activity Scale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA total of three items in the questionnaire were used to evaluate teachers\u0026rsquo; online PE instructional activity in three aspects: pre-class, in-class, and post-class. The items include a 5-point Likert scale, ranging from \u0026ldquo;strongly disagree\u0026rdquo; to \u0026ldquo;strongly agree\u0026rdquo;, with corresponding scores from 1 to 5. The higher the score is, the better teachers\u0026rsquo; instructional activity and the more comprehensive the teaching process will be.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e(2)\u0026nbsp;\u003c/strong\u003eStudents\u0026rsquo; Sports Activity and Ability Scale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis questionnaire, consisting of 6 items, was used to evaluate students\u0026rsquo; sports activity and ability. The items include a 5-point Likert scale, ranging from \u0026ldquo;strongly disagree\u0026rdquo; to \u0026ldquo;strongly agree\u0026rdquo;, with corresponding scores from 1 to 5. The higher the score is, the stronger the students\u0026rsquo; sports activity and ability will be.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e(3)\u0026nbsp;\u003c/strong\u003eOnline Sports Interactive Behavior Scale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe scale of \u0026ldquo;PE Learning Interactive Behavior\u0026rdquo; by Yan (2019) was used in this study to measure the learning interactive of participants according to the dimensional standards of PE learning activity \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Based on the Delphi method, the scale can continuously correct observed indicators. Through three rounds of Delphi method, the variation coefficient of each indicator can reach 0.4 or below. The P-values are all less than 0.001 during the three rounds of expert consultation, indicating a high credibility in the coordination of the three rounds of expert consultation. Interactive behavior is mainly composed of four items, which measure learner-teacher interaction, learner-learner interaction and learner-content interaction respectively. The scale is scored by 5-level Likert. The five alternative answers range from \u0026ldquo;never\u0026rdquo; to \u0026ldquo;always\u0026rdquo;, with corresponding scores from 1 to 5. The higher the score is, the better the participant interactions will be.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e(4)\u0026nbsp;\u003c/strong\u003eOnline PE Learning Performance Scale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe survey on learning performance includes five aspects: emotions, knowledge, skills, physical fitness (Liu et al., 2017)\u0026nbsp;\u003csup\u003e[25]\u003c/sup\u003e, and achievement\u0026nbsp;(Hu et al., 2020)\u0026nbsp;\u003csup\u003e[26]\u003c/sup\u003e. Students can evaluate their learning performance from five aspects: sports emotions, sports knowledge, sports skills, physical health, and academic performance. This questionnaire items includes a 5-point Likert scale, ranging from \u0026ldquo;very inconspicuous\u0026rdquo; to \u0026ldquo;very conspicuous\u0026rdquo;, with corresponding scores from 1 to 5. The higher the score is, the better online PE learning performance of students will be.\u003c/p\u003e\n \u003cp\u003eReliability analysis and factor analysis were used to test the consistency and validity of the questionnaire items (see Table 1). After calculation, the Cronbach\u0026rsquo;s \u0026alpha; values for various dimension in the questionnaire ranged from 0.827 to 0.948, with the KMO values for validity from 0.738 to 0.888, indicating that the reliability and validity of these questionnaires are relatively reliable.\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe reliability and validity results of online PE learning activity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasurement Items\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCronbach\u0026rsquo;s \u0026alpha;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKMO\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstructional Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Activity and Ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInteractive Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLearning Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Data Processing\u003c/h2\u003e\n \u003cp\u003eSPSS 26.0 was used for description and correlation analysis on the data in this study, and the significance level of all indicators was set at \u0026alpha;= 0.05. In this paper, teachers\u0026rsquo; instructional activity and students\u0026rsquo; sports activity and ability were set as the independent variables, students\u0026rsquo; interactions in online PE classrooms were the mediating variable, and online PE learning performance was the dependent variable. As the first step in data analysis, we observed the characteristics of the data. According to the criterion, \u0026ldquo;the absolute values of skewness and kurtosis\u0026le;1 indicates that the data conforms an approximate normal distribution\u0026rdquo;, the results have shown that further data analysis can be carried out. In addition, SPSS was used to calculate standardized data for each variable. Afterwards, the Structural Equation Modeling (SEM) was drawn in the AMOS26 version for conducting a mediating effect analysis on the questionnaire data. The bias-corrected non-parametric percentage Bootstrap method (5000 repeated samples) was selected to estimate the 95% confidence interval (CI) and test the mediating effect of interactions (Preacher, K. J. et al., 2004)\u0026nbsp;\u003csup\u003e[27]\u003c/sup\u003e. If the 95% CI of the mediating effect does not include 0, it can indicate that the mediating effect is significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Common method deviation test\u003c/h2\u003e\n \u003cp\u003eThis study used the self-report approach to collect data, which may lead to errors in common methods. On the basis of procedural control for the possible errors in common methods (such as filling in wrong time, reverse scoring of some items, etc.), Harman\u0026rsquo;s one-factor analysis was further used to test the errors in common methods (Zhou et al., 2004)\u0026nbsp;[\u003csup\u003e28]\u003c/sup\u003e. The results of exploratory factor analysis extracted a total of four factors with characteristic roots greater than 1. The maximum factor variance of interpretation was 23.510%, less than the critical standard of 40% (Tang, D. D. and Zhong, Y., 2020)\u0026nbsp;\u003csup\u003e[29]\u003c/sup\u003e, indicating that the errors in common methods has been within an acceptable range. Therefore, there is no serious general error in the survey data of this study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Description and Correlation\u003c/h2\u003e\n \u003cp\u003eAccording to the mean and standard deviation of each variable and the Pearson correlation analysis results (see Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), the score of teachers\u0026rsquo; instructional activity was 4.07, close to the full score of 5, indicating that teachers can ensure the integrity of teaching links during the process of online PE instruction, and better interact with students in the three links. In terms of students\u0026rsquo; sports activity and ability, the average score was 3.59, above the medium level, indicating that there is still a large space for improvement in college students\u0026rsquo; sports activity and ability. Students\u0026rsquo; interactive behavior score was 3.42, slightly higher than the median value, indicating that college students have had poor interactive experience during the process of online PE learning. Students\u0026rsquo; online PE learning performance was above the average score (3.66), indicating that students have had a positive attitude towards the improvement of their own learning performance after online PE instruction, but the teaching objective of comprehensive education have not been yet achieved.\u003c/p\u003e\n \u003cp\u003eAs seen from the correlation analysis of instructional activity, sports activity and ability, interactive behavior, and online PE learning performance (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for details), teachers\u0026rsquo; instructional activity is significantly positively correlated with students\u0026rsquo; interactive behavior and online PE learning performance, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Students\u0026rsquo; sports activity and ability are significantly positively correlated with students\u0026rsquo; interactive behavior and online PE learning performance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, students\u0026rsquo; interactive behavior are also significantly positively correlated with their online PE learning performance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation matrix of instructional activity, sports activity and ability, interactive behavior, and online PE learning performance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInstructional Activity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSports Activity and Ability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInteractive\u003c/p\u003e\n \u003cp\u003ebehavior\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLearning Performance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInstructional Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Activity and Ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInteractive behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.307***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.434***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLearning Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.482***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.554***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.408***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e***在0.001级别(双尾), 相关性显著。***At 0.001 level (double-tailed), the correlation is significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Establishment of Structural Equation Modelling\u003c/h2\u003e\n \u003cp\u003eOn the basis of relevant analysis, the structural equation modelling was established with teachers\u0026rsquo; instructional activity and students\u0026rsquo; sports activity and ability as the independent variables, online PE learning performance as the dependent variable, and interactions as the mediating variable. The mechanism of affecting online PE learning performance under the mediating effect of interactive behavior was further explored, and a mediating model of interactions was then constructed (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for details), with the fit indices shown in the table. The results have showed that various fit indices of the model are satisfactory (\u0026chi;\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;5.900, P\u0026lt;0.001, RMSEA\u0026thinsp;=\u0026thinsp;0.084, GFI\u0026thinsp;=\u0026thinsp;0.882, AGFI\u0026thinsp;=\u0026thinsp;0.844, NFI\u0026thinsp;=\u0026thinsp;0.917, CFI\u0026thinsp;=\u0026thinsp;0.930, IFI\u0026thinsp;=\u0026thinsp;0.930).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Correction of Structural Equation Modelling\u003c/h2\u003e\n \u003cp\u003eBased on the results of initial model path, the model was corrected. According to the MI error correction model indices, cointegration analysis was conducted on the variables to discover cointegration relationships between variables. The error correction term was considered as an explanatory variable, and a short-term model was then established together with other explanatory variables reflecting short-term fluctuations. As a result, it was found that there was a correlation between the two items in the interactions, and the model were then corrected (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe fitting test values of the corrected structural equation modelling (hereinafter referred to as \u0026ldquo;the corrected model\u0026rdquo;), which can reflect the mechanism of affecting online PE learning performance, were compared with the initial model test results and the fitting standard values to obtain the degree of fitting of the corrected model (see Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e below). The results can indicate that all fit indices of the corrected model have been improved to reach the fitting standard values (\u0026chi;\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;4.494, P\u0026lt;0.001, RMSEA\u0026thinsp;=\u0026thinsp;0.071, GFI\u0026thinsp;=\u0026thinsp;0.910, NFI\u0026thinsp;=\u0026thinsp;0.938, CFI\u0026thinsp;=\u0026thinsp;0.951, IFI\u0026thinsp;=\u0026thinsp;0.951)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe fitting test values of the corrected model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFitting Indicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCMIN/DF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFitting Standard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel Fitting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrected Model Fitting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe test results for the initial model path coefficient and the corrected model path coefficient are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Both the initial model path coefficient and the corrected model path coefficient have shown that the five paths can exert a significant positive impact. With the corrected model path coefficient as an example, instructional activity has a positive and direct impact on interactions (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Under null hypothesis, the standardized effect value was 0.306, indicating that instructional activity can directly explain 30.6% of the variation in interactions. Instructional activity also has a positive and direct impact on learning performance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Under null hypothesis, instructional activity can directly explain 25.6% of the variation in learning performance. Sports activity and ability have a positive and direct impact on interactions and learning performance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Under null hypothesis, sports activity and ability can directly explain 32.3% and 45.8% of the variation in interactions and learning performance. Interactions have a positive and direct impact on learning performance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); under null hypothesis, interactions can directly explain 9% of the variation in learning performance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePath analysis between factors in model fitting\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePath Relationship\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eStandardized\u003c/p\u003e\n \u003cp\u003ePath Coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eC.R.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBefore\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAfter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBefore\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAfter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBefore\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAfter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBefore\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAfter\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIA ➡ IB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIA ➡ LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAA ➡ IB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAA ➡ LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIB ➡ LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe bias-corrected Bootstrap method (5000 repeated samples) was further selected to test the mediating effect of interactions (see Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The direct effects of teachers\u0026rsquo; instructional activity and students\u0026rsquo; sports activity and ability on online PE learning performance did not contain 0 in the upper and lower limits of the bootstrap 95% CI, indicating that teachers\u0026rsquo; instructional activity and students\u0026rsquo; sports activity and ability can directly affect online PE learning performance. The effect of interactions on online PE learning performance did not contain 0 in the upper and lower limits of the bootstrap 95% CI, indicating that interactions can affect online PE learning performance. The indirect effects of teachers\u0026rsquo; instructional activity and students\u0026rsquo; sports activity and ability on online PE learning performance, namely the mediating effect of interactions did not contain 0 in the upper and lower limits of the bootstrap 95% CI, can indicate that interactions play a partial mediating role. Moreover, teachers\u0026rsquo; instructional activity and students\u0026rsquo; sports activity and ability can affect online PE learning performance through the mediating effect of interactions.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMediating effect test of the corrected model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePaths\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEffect Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ebias-corrected95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIA ➡ LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAA ➡ LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIB ➡ LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIA ➡ IB ➡ LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAA ➡ IB ➡ LP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cp\u003e5.1 Teachers\u0026rsquo; instructional activity is the decisive factor for students to participate in learning\u003c/p\u003e\n\u003cp\u003eTeachers\u0026rsquo; instruction is explained as pre-class, in-class, and post-class links, and their instructional activity should be carried out throughout these links. It has been proven that their instructional activity can directly affect the learning performance of students. Instructional activity refers to a two-way process between teachers and students, in which the instruction of teachers, as the knowledge transmitters in teaching, can directly affect the learning process of students (Li et al., 2020)\u0026nbsp;\u003csup\u003e[30]\u003c/sup\u003e. Previous studies have shown that students\u0026rsquo; perception of teaching in class and teachers\u0026rsquo; emphasis on instruction are the main factors contributing to differences in teaching satisfaction. Teachers\u0026rsquo; instruction characteristics, personal qualities, and instructional involvement can exert an impact on such factors as students\u0026rsquo; perception, motivation, and ability, all of which are intrinsic factors that affect students\u0026rsquo; learning performance (Patrick，H., 2004; Shea P. et al., 2006)\u0026nbsp;\u003csup\u003e[31-32]\u003c/sup\u003e. Not only that, but we also tested that teachers can engage in teaching interaction through three phases, including pre-class, in-class, and post-class, in order to improve teacher-student interaction and student-student interaction and then enhance students\u0026rsquo; learning performance. It can be seen that the attitude, preparation, and engagement of teachers towards classroom greatly affect students\u0026rsquo; learning performance. In terms of the research on teachers\u0026rsquo; instructional activities before, in, and after class, Yang and Sun (2022) proposed that teachers should promote students\u0026rsquo; preview activity before class, and use instructive language to stimulate students\u0026rsquo; potential for self-regulated learning. In addition, teachers should increase the interest and professionalism of instructional design during in-class link for encouraging students to engage in interactions. Furthermore, timely Q\u0026amp;A and evaluation by teachers after class can improve students\u0026rsquo; self-regulated learning (Yang et al., 2022)\u0026nbsp;\u003csup\u003e[33]\u003c/sup\u003e, which has also been confirmed in the study by Feng et al. (2022)\u003csup\u003e\u0026nbsp;[34]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhen teaching PE online, teachers need to have certain transferability of knowledge and skills, and are able to control the curriculum from different perspectives. For students of different majors, different levels and different personalities, teachers\u0026rsquo; instructional design should be comprehensive and wide in coverage, and they should combine different perspectives to explain and demonstrate their skills. Especially on the Internet, it is particularly critical for how teachers mobilize the enthusiasm of students, which can further test their comprehensive teaching capacity. During the process of online teaching, we are more inclined to the teacher-led and student-dominated concept. First of all, as teaching dominant holders, teachers can create a stable and positive teaching environment, and select appropriate learning materials for students to participate in classroom, establish learning motivation, and engage self-regulated learning (Jen Hwang and Yue Zhu, 2020) \u003csup\u003e[35]\u003c/sup\u003e. Secondly, the solid relationship between teachers and students can not only exert a greater impact on students than academic performance, but also has a potential impact on students\u0026rsquo; lives and personalities. It has been proven that teachers\u0026rsquo; professional literacy and the teacher-student relationship can make students happier, healthier, and physically stronger (Leaming, K. G., 2020; Lavoie, B., 2022) \u003csup\u003e[36][37].\u003c/sup\u003e Teachers should not only have the ability to master modern educational technology and allocate each teaching link in a scientifical and reasonable manner (Wan, M., 2016) \u003csup\u003e[38]\u003c/sup\u003e, but also reasonably arrange the content and form of pre-class, in-class and post-class teaching activities, thereby ensuring the attention and participation of students in each learning phase and support the effective learning of students (Goodyear V. et al., 2015) \u003csup\u003e[39]\u003c/sup\u003e. Finally, there are some students who have achieved achievements in completely independent learning during the teaching process, but such situations are actually rare. Many students still need to participate in PE classroom interaction and adjust their learning activities under the guidance of teachers. Therefore, students\u0026rsquo; participation in classroom depends on teachers\u0026rsquo; instructional activity during the teaching process, so that teachers\u0026rsquo; instructional activity is the decisive factor for students\u0026rsquo; active participation in classroom.\u003c/p\u003e\n\u003cp\u003e5.2 Students\u0026rsquo; sports activity and ability are the driving force for their participate in learning\u003c/p\u003e\n\u003cp\u003eAccording to the particularity of online PE teaching, students are required to have self-regulated learning ability and stronger self-discipline. It has been found in this study that students\u0026rsquo; sports activity and ability can exert a greater impact on learning performance, indicating that learning autonomy plays a more important role in online PE learning-situated context. The better students\u0026rsquo; sports activity and ability are, the better their learning performance will be. Cho, M.\u0026amp; Kim B. J (2013) \u003csup\u003e40\u003c/sup\u003econfirmed that autonomy and self-supervision can play a greater and more significant role in online learning-situated context. Online learning realizes communication and interaction between teachers and students in different time and space, so that this learning-situated context requires the engagement of students\u0026rsquo; own learning ability and self-control. Especially in online PE classrooms, skill display and error correction cannot be carried out on-site, and the sense of teaching presence is weakened. Additionally, the temptation of the Internet has become a major factor that distracts students\u0026rsquo; attention. Therefore, students should give their subjective initiative into the learning process, and adjust their learning activity as their motivation to carry out learning. Paris S G \u0026amp; Paris A H(2001), Zimmerman BJ \u0026amp; Schunk D H. (2011) proved that learners with strong autonomous learning ability are more able to regulate and monitor their learning process in the learning environment \u003csup\u003e[41-42]\u003c/sup\u003e. According to this study, students with strong sports activity and ability can more effectively overcome tempting interfaces, participate in PE classroom interactions, and obtain good learning performance in online learning. In addition, Pintrich (1999), Bandura (2012), and Vancouver (2018) confirmed in their studies that students\u0026rsquo; sports ability can have a certain regulatory effect on their cognitive and behavioral development during the learning process, and this ability, as a key component for generating learning strategies, can provide emotions and beliefs for students to engage in learning activities \u003csup\u003e[43, 44, 45]\u003c/sup\u003e. It can be indicated that, on the one hand, students\u0026rsquo; sports activity and ability can act as a prerequisite to provide motivation for their own engagement in the classroom context. On the other hand, it can create a connection between students and course performances, and then adjust their learning activities.\u003c/p\u003e\n\u003cp\u003eStudents with autonomous learning and sports abilities have better learning strategies, which can effectively enhance their learning performance to a certain extent. When teachers build a loving, respectful and warm learning environment, students can give full play to their own autonomous learning abilities and subjective initiatives. Moreover, through collaborating with teachers\u0026rsquo; instruction and guidance, students can gradually develop and participate in self-regulated learning, so as to promote the improvement of teaching effectiveness.\u003c/p\u003e\n\u003cp\u003e5.3 Interactions plays a crucial role in learning performance\u003c/p\u003e\n\u003cp\u003eThe online live learning form puts forward a higher demand for communication and exchange between teachers and students. As for the strength of mutual response in online classroom, teachers should consciously create a good problem situation and an interactive classroom atmosphere, and assist students to participate in class actively and absorb, digest and construct knowledge quickly (Cui et al., 2022) \u003csup\u003e[46]\u003c/sup\u003e. In addition, students need to actively participate in the classroom, pay a higher emotional value, and overcome the temptation of the Internet. The interaction between teachers and students can exert a positive impact on teaching quality and satisfaction, so that the interaction is called a dialogue response, namely the degree of interaction and communication, and mutual response between teachers and learners. Interaction can deepen students\u0026rsquo; understanding of knowledge and improve teaching quality. Anderson T (2003) proposed that online interaction includes Learner-Content (LC), Learner-Teacher (LT), and Learner-Learner (LL) pluralistic interactions \u003csup\u003e[47]\u003c/sup\u003e. First of all, the occurrence of classroom interaction depends on teachers\u0026rsquo; implementation of instructional design and methods, which is regarded as the prerequisite for learners to engage in interactions. In view of classroom interaction in PE teaching, teachers need to arrange interactive links in a proper way, stimulate students\u0026rsquo; interest in learning, and guide the interaction between students. For example, students can be divided into skill demonstration groups, in which students with strong sports activity and ability lead other students to participate in the class and exert their subjective status. Secondly, the occurrence of classroom interaction depends on students\u0026rsquo; own knowledge and skill reserves. In this study, it has been confirmed that students with stronger sports activity and ability are more actively involved in classroom interaction. Students\u0026rsquo; learning activity and ability can be shaped, so that with sufficient support, students can improve their self-regulated learning (Theobald M., 2021; Jansen R. S. et al., 2019) \u003csup\u003e[48, 49]\u003c/sup\u003e. Most of the students with strong activity and ability are more passionate about sports, and more willing to learn knowledge and acquire skills from the PE classroom. Therefore, for the generation of interactions, it is necessary for teachers to pay attention to this group of strong students, and guide the other group of weak students into the classroom through them, so that more students can profit from online PE classroom.\u003c/p\u003e\n\u003cp\u003ePE classroom interactions can play a crucial role in learning performance, which results from the collaboration between students and teachers (Kuo Y. C. et al., 2014; Hone K. S. et al., 2016) \u003csup\u003e[50-51]\u003c/sup\u003e. Interactions are beneficial for students to generate a positive learning experience, so that they can build knowledge frameworks, absorb the strengths of others, and enhance their own abilities during the interactions. Teachers should construct a platform for interconnection and value sharing, through which students are first associated with the course to obtain first-hand information from it. Secondly, students are associated with teachers. During the learning process, learners raise questions to teachers and respond to the questions raised by teachers in class. In this way, teachers are required to be very enthusiastic and patient throughout the entire process. The study of Li and Zhong (2020) proved that the more teachers\u0026rsquo; input into interactions is, the better the corresponding learners\u0026rsquo; learning performance will be \u003csup\u003e[52]\u003c/sup\u003e. Finally, learners are associated with each other. Through the learner-learner interaction, learners can alleviate their negative emotions and increase teaching engagement (Wang, 2021) \u003csup\u003e[53]\u003c/sup\u003e. The connection between students is closer than that between teachers and students. As a result, students are able to interact with other peers in a more relaxed and pleasant emotion. Therefore, teachers should pay more attention to the learner-learner interaction in their instructional design, and build an interactive bridge between learners.\u003c/p\u003e\n\u003cp\u003eThe learning interactions between learning communities refer to the core process of online learning, and the generation of interactions can inevitably affect learning performance. The generation of good interaction relationship is conducive to the advancement of classroom instruction. As a mediating effect, interactions can bridge the gap between teacher-student relationship and learning performance, and exert an important impact on learning performance through the joint efforts of teachers and students.\u003c/p\u003e"},{"header":"6 Conclusions and Suggestions","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Conclusions\u003c/h2\u003e \u003cp\u003eUnder the background of the information era, the AMOS model was used in this study to investigate the relationship among teachers\u0026rsquo; instructional activity, students\u0026rsquo; sports activity, and learning performance, as well as the mediating role of interactions. From the correlation analysis of teachers\u0026rsquo; instructional activity, students\u0026rsquo; sports activity and ability, interactions, and online PE learning performance, the better the teachers\u0026rsquo; instructional activity is, the more active the students\u0026rsquo; interaction in class and the better the online learning performance will be. In addition, the more the students\u0026rsquo; sports activities and the stronger their sports abilities are, the easier it is for them to actively engage in interactions, and the better the accompanying learning performance will be. The following conclusions can be drawn in this paper. Firstly, teachers\u0026rsquo; instructional activity is the decisive factor for students\u0026rsquo; participation in classroom. Teachers can intervene in classroom interaction through their instructional activities, thereby improving students\u0026rsquo; learning performance. Secondly, students\u0026rsquo; sports activity and ability are the driving force for their engagement in classroom instruction. The stronger the students\u0026rsquo; sports activity and ability are, the more active their engagement in classroom interaction and the better their learning performance will be. Finally, classroom interaction plays a crucial role in learning performance, because the occurrence of interactions can effectively enhance the initiative of students and the classroom interest. Students\u0026rsquo; interactions in classroom can play a mediating role in the connection between teachers\u0026rsquo; instructional activity and students\u0026rsquo; learning performance, as well as in the connection between students\u0026rsquo; sports activity and ability and their learning performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Suggestions\u003c/h2\u003e \u003cp\u003eTeaching is an important way to cultivate students in colleges and universities. Teachers\u0026rsquo; instructional activity can exert a significant impact on students\u0026rsquo; online learning performance. At the teacher level, they should conduct their instructional activities in class and improve their teaching levels, which are beneficial for better classroom effects. Moreover, it is essential for teachers to raise moral constraints on themselves, and stress teaching techniques, including pre-class preparation, in-class arrangement, and post-class feedback. In terms of teaching approaches, classroom discussions, situational teaching, and case teaching can be used to stimulate students\u0026rsquo; enthusiasm in online classrooms and improve teaching effectiveness. Greater importance should be attached to the classroom feedback from students, their interest in participation, and the establishment of good teacher-student relationship. Believe in what your teachers teach, and follow their instructions. This constructive interaction between teaching and learning can not only affect students\u0026rsquo; learning activity in a positive manner, but also motivate teachers to do a better job. However, teachers in colleges and university are confronted with a dual pressure of scientific research and teaching tasks now. Hence, necessary measures should be taken in colleges and universities to enable teachers to spend more time and efforts on teaching. Furthermore, students\u0026rsquo; sports activity and ability can also play an important role in learning performance. In addition to enhancing the teaching level of teachers, colleges and universities should actively carry out the popularization of sports activities and health education, create a positive sports atmosphere, fully motivate students\u0026rsquo; willingness and engagement in sports, effectively improve their sports abilities and initiatives, and achieve the improvement of their sports activities and abilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by Chang an University (China). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ.Z, S.C contributed to the conception and design of the study. QZ performed the data collection, developed evaluation tools, and wrote the manuscript. QZ and SC performed the data analysis. QZ, SC revised and significantly contributed to the final version of the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be\u0026nbsp;construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher’s note\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCai, M. J. (2023). 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Z. and Zhong, Y. (2020). The Influence of Online Teachers\u0026apos; Teaching Input on Students\u0026apos; Learning Performance--Based on the Perspective of Teachers and Students. Research on Open Education.26(3),99-110.\u003c/li\u003e\n\u003cli\u003eWang, S. Y. (2021). A probe into the relationship between the interactive form of online course teaching and the performance of students\u0026apos; learning engagement [J]. Journal of East China Normal University (Education Science Edition). 39(7),38-49.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Teachers’ Instructional Activity, Online Learning, Classroom Interaction, Sports Ability, Learning Performance","lastPublishedDoi":"10.21203/rs.3.rs-4898477/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4898477/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eWith the rapid development of Internet technology and its wide application in education, online education has become an important way for people to learn knowledge in the digital era. Due to the time-space separation teachers and students, as well as their lack of face-to-face communication during online learning, it is difficult to accurately estimate the learning performance of online physical education (PE). Therefore, the relationship among teachers’ instructional activity (IA) , students’ sports activity and ability (SAA), and online PE learning performance (LP), as well as the mediating effect of interactions (IB), are mainly studied in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Through the questionnaire of online PE learning activity, this paper investigated the online PE learning situation of 691 college students in Shaanxi Province. SPSS26.0 was used for description and correlation analysis of the variables, and then AMOS26.0 version was used to draw a structural equation modeling. The bias-corrected non-parametric percentage Bootstrap method (5000 repeated samples) was selected to estimate the 95% confidence interval (CI) and test the mediating effect of interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e (1) There is a significant positive correlation (p\u0026lt;0.001) among four variables. (2) A structural equation model with interactions as the mediating variable was established. All fitting test values of the corrected model reached the fitting standards(c\u003csup\u003e2\u003c/sup\u003e/df=4.494, P＜0.001, RMSEA=0.071, GFI=0.910，NFI=0.938, CFI=0.951, IFI=0.951), so that the mediating effect has been persuasive. (3) According to the corrected model path coefficients, IA can explain 30.6% and 25.6% of the variation of IB and LP, SAA can explain 32.3% and 45.8% of the variation of IB and LP, while IB can directly explain 9% of the variation of LP. (4) The effects of IA, SAA, IB, and LP did not contain 0 in the upper and lower limits of the bootstrap95% CI, indicating that the mediating effect of interactions has been persuasive.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e (1) Both teachers’ instructional activity and students’ sports activity and ability can exert a positive impact on learning performance. (2) Teachers’ instructional activity is the decisive factor for students participate in learning. Teachers can improve the quality of interactions through teaching behavior, thereby improving students’ learning performance. (3) Students’ sports activity and ability are the driving force for their participate in learning. The stronger students’ sports activity and ability are, the more active they will be in classroom interaction and the better learning performance they will get. (4) Interactions can play a crucial role in learning performance, because the occurrence of interactions can effectively enhance the initiative of students and the classroom interest.\u003c/p\u003e","manuscriptTitle":"Study on the Mechanism of Online PE Learning Performance: Mediating Effect of Interactions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-19 15:46:48","doi":"10.21203/rs.3.rs-4898477/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01b33f51-7cfc-4820-8d4e-aee56dde2f58","owner":[],"postedDate":"September 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37576418,"name":"Biological sciences/Psychology"},{"id":37576419,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-04-11T06:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-19 15:46:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4898477","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4898477","identity":"rs-4898477","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}