Research on Innovative Teaching Strategies in Physical Education Empowered by New Technology
Pubblicato online: 03 feb 2025
Ricevuto: 23 set 2024
Accettato: 09 gen 2025
DOI: https://doi.org/10.2478/amns-2025-0001
Parole chiave
© 2025 Yong Wang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
At present, the college physical education classroom has become a key way to penetrate the core qualities of sports to students and improve their physical fitness. Teachers should apply more diversified and innovative teaching methods in college physical education classrooms so that students can form innovative concepts while receiving physical education [1]. Guided by the concept of health first, more and more diversified teaching methods are applied in college physical education classrooms to optimize the classroom teaching effect. Therefore, physical education teachers in colleges and universities should form a deep understanding and cognition of the concept of innovative education from their point of view, look for the fit between it and the physical education curriculum, and vigorously implement the concept of innovative education, so as to achieve the goals of physical education in colleges and universities [2–4].
In the field of education and teaching, the connotation of new technology refers to an emerging technology for rapid acquisition, processing, transmission, and presentation of knowledge, through which teachers are able to process relevant teaching content and knowledge information, thus assisting in the effective and orderly development of classroom teaching activities [5–6]. In general, the application of new technology in physical education has gained better achievements, but the problems that exist in it are also very obvious, and these problems will directly affect the quality of teaching and learning in physical education. Therefore, teachers must pay attention to these problems and try to take effective measures to improve these problems, so that the advantages of the application of new technology in physical education can be maximized [7–8].
Ningthoujam, R. et al. pointed out that physical educators play an important role in the development of physical education professionals. There are no natural sports geniuses; they are developed through participation in sports programs, so every school should have a good physical education infrastructure that enables students to have sufficient time for physical activity [9]. Gil-Gómez, J. et al. compared the effects of the same effect of two different interventions of a learning program on pre-service teachers’ ability to teach. The results indicated that preservice teachers provided immediacy to children with diverse motor functioning, thereby facilitating their motor skill growth [10]. Ovens, A.P. et al. noted that the pandemic has created many challenges for the teacher community. Teachers shared stories of how these challenges were handled in subjects such as physical education. These shares provided valuable suggestions for teachers to learn new strategies for teaching physical education [11]. Castro-Donado, S. et al. developed the affective competencies of higher education students and the basic competencies for a degree in Physical Activity and Exercise Science by following Bisquerra’s five-module model. The aim is to ensure that students are better able to serve others when they become sports professionals [12]. Shang, H. illustrated that innovative teaching in physical education is to develop students’ creative awareness, ability, and spirit, to foster innovative thinking, to explore students’ creative potential, and to improve their creative learning ability. Analyzed the problems of traditional physical education teaching and put forward countermeasures, pointing out that physical education should give full play to the students’ main position [13]. Li, J. studied the problems in college physical education classroom teaching from the perspective of the Internet. It was concluded that physical education teaching in colleges and universities lacks theoretical guidance and the sorting out of concepts, which leads to the failure of students to form a sense of physical education. In addition, there are irrational phenomena in the arrangement of physical education teaching [14].
This paper first takes the mechanism of new technology-enabled physical education as the starting point and innovatively designs the teaching strategy of new technology-enabled physical education from the operational mechanism to the implementation framework. Then, the specific implementation effect of the teaching strategy was analyzed using the controlled experiment method with an independent sample t-test. Finally, the questionnaire method was used to analyze the degree of students’ demand for the intelligent physical education teaching environment constructed on the basis of the innovative teaching strategy of this paper. The five major indicators of the technology acceptance model, perceived usefulness, perceived ease of use, attitude of use, and behavioral willingness, were introduced to analyze the correlation based on the Pearson correlation analysis method. The linear regression model was constructed to analyze the feasibility of it, so as to validate the innovative feasibility and practicability of the teaching strategy in this paper.
In this paper, based on artificial intelligence combined with virtual reality, big data, cloud computing, motion capture and recognition, and other technologies, we design an innovative teaching strategy for physical education empowered by new technologies.
Based on new technology-enabled teaching methods, sports teaching completely subverts the traditional teaching mode.
This study constructs a model of the overall operation mechanism of new technology-enabled physical education teaching, as shown in Figure 1. It is divided into five distinct elements in practical application: educator, learner, teaching mode, educational resources, and teaching feedback and evaluation.
Operation mechanism model
The role of educators has always been dominant in educational activities, and this is also true for physical education teaching, where physical education teachers play a guiding role in education and teaching empowered by new technologies. Based on the medium of the artificial intelligence platform, physical education teachers upgraded to intelligent teachers. According to the statistical results of big data, the teacher is easier to grasp the students’ pre-course preparation, classroom learning, post-course feedback, etc., so as to do according to the student’s abilities, according to the stadium teaching, thanks to the intelligent machine to assist in the teaching, it is easier to control the direction of the teaching.
Based on intelligent data statistics, students can identify their strengths and weaknesses. They can also rely on intelligent teachers to conduct accurate assessments and design teaching programs that are tailored to the material, promoting overall development. Self-study is not bound by the restrictions of traditional teaching time and place, so learning becomes more independent and open.
Aiming at the current situation faced with cumbersome types of educational resources, complex structure, and uneven distribution, this paper integrates and optimizes the educational resources through the artificial intelligence platform and realizes the interconnection between terminal devices through cloud computing so as to make the educational resources reciprocal to all levels of physical education teaching.
Venue factors and weather factors often constrain sports teaching. New technology-enabled sports teaching is completely free from these constraints, the use of virtual reality (VR) technology, which allows teachers and students to make their own choices and to realize the seamless integration of stadiums, sports equipment, and the environment with teaching. Intelligent teaching and accurate teaching are implemented together, making teaching fruitful.
Teaching feedback and evaluation have always been the key to the teaching process of checking for deficiencies and making up for mistakes, and the application of artificial intelligence technology and big data has made it simple and efficient, with data collection and intelligent analysis, allowing educators to become proactive in diagnostic, formative, and summative evaluations, realizing automated and quantitative evaluations, and delivering the information in a timely manner to the students, so that they can learn and grow in accordance with the intelligent planning.
In this paper, according to the design of physical education teaching and the flow of teaching activities, the implementation framework of new technology-enabled physical education teaching strategies is constructed as shown in Figure 2, which includes five aspects: teaching goal setting, teaching content presentation, teaching organization, teaching environment creation, and teaching feedback.
The implementation framework of sports teaching strategies
The intelligent platform enables new technology-enabled sports teaching to set precise teaching goals. First, artificial intelligence technology is used to analyze students’ learning conditions, scientifically track students’ physical health data, exercise hours, mastery of sports skills, and other data through the smart platform, diagnose students’ existing sports foundation, and understand students’ sports learning expectations. Secondly, we match personalized and precise physical education teaching objectives with the students’ learning situations and implement key teaching interventions for specific groups. Finally, effective classroom practice is carried out, and intelligent tools are effectively used to analyze the gap between students and the predetermined goals and provide timely classroom reminders during the practice process. Through the “precise” data collection on the intelligent platform, we have overcome the problems of difficult quantification, recording, supervision, and analysis in traditional physical education teaching and promoted the realization of precise physical education teaching goals.
Precision goal-setting in physical education enables teachers to teach appropriate content based on student learning. The new technology-enabled sports teaching practice has a specific implication:
The appropriateness of the difficulty level of physical education content. New technology-enabled sports teaching can analyze the correlation between students’ interest in sports learning, motor skills, teaching content, classroom physical activity level motor skills achievement, and other variables through data analysis and then predict the learning effect of the students, matching the appropriate level of difficulty of the teaching content. The appropriateness of sports teaching content. New technology-enabled sports teaching is based on artificial intelligence algorithm engine technology to establish electronic files of student growth records, tracking the process of students’ sports learning, helping teachers to select the knowledge conducive to the development of students from a huge amount of information, and at the same time based on the learning efficiency of students, the content of the teaching of the appropriate increase or decrease. The appropriateness of the presentation of sports teaching content. The application of artificial intelligence technology has changed the previous sloppy teaching mode, which can design personalized teaching content for students according to their actual learning situation and evaluate students’ athletic ability in real time and accurately.
The time and space limitations of traditional sports teaching are broken by new technology-enabled sports teaching, resulting in the transformation from traditional indoctrination to interactive teaching. Physical education classroom teaching is no longer limited to fixed sports venues, enabling the combination of online and offline learning, classroom and offline learning on-campus and off-campus learning, and virtual and real space. The application of microteaching, catechism, flipped classroom, and App learning software to formal and informal physical education learning has expanded the sources of students’ sports information input. This type of learning based on information technology to obtain the required information has realized the sharing of high-quality physical education resources and facilitated the formation of an integrated learning path for physical education in time and space.
New technology-enabled sports teaching is able to build smart teaching scenarios based on data-driven construction. For example, the smart playground uses AI artificial intelligence, machine vision, big data analysis, and other advanced technologies to promote the digital upgrading of campus sports and realize the visualization of school sports around the campus sports teaching application scenarios. In sports teaching, students do not need to wear any equipment, smart playground system through the Internet camera, AI motion vision algorithms, long-distance “non-sensory” collection of student sports performance, sports status, technical movements, and other indicators, and a combination of backstage data software for visualization and analysis. At the same time, classification, clustering, text mining, Web mining, and other technologies are used to analyze the teaching behavior of the sports classroom, discover the teaching loopholes of teachers in a timely manner, and provide data support for teachers to improve teaching methods and teaching strategies. The smart sports scene has the advantages of data monitoring, flexibility, and intelligence, and is helpful for scientific research, which helps teachers to implement the integrated physical education teaching goal of “teaching, practicing and competing” and helps students to carry out “normalized” physical exercise.
One of the reasons why traditional sports teaching evaluation often focuses on result-based evaluation and lacks process-based evaluation is the lack of behavioral data on the sports teaching process. New technology-enabled physical education can dynamically monitor and evaluate students’ physical education learning behaviors through pre-course data monitoring, in-class tracking feedback, and post-course improvement and enhancement. Hardware products such as the AI Physical Education and Health Lesson Plan Intelligent Assistant, Physical Education and Health Classroom Intelligent Monitoring System, and the Basic Motor Skills Visualization Assessment System provide perfect hardware and software facilities for new technology-enabled physical education classroom teaching monitoring. Dynamic monitoring and assessment of students’ physical education learning process provides timely feedback and assessment of teaching results while respecting the law of their physical and mental development and growth.
When it is necessary to compare whether there is any difference between the means of two groups in the survey population on a certain characteristic if the two samples taken are independent, the data are equidistant, and the population in which they are located obeys a normal distribution, it is generally possible to infer whether there is a significant difference between the two populations by doing a
Ideas for the independent samples
First of all, the independent samples The variances of two totals are equal: The variances of the two totals are not equal:
Next, The means of two totals are equal: The means of the two aggregates are not equal:
The
If
When the variances are not homogeneous, the
The Technology Acceptance Model (TAM) is a theoretical model that employs the theory of rational behavior to study users’ acceptance of information technology and the process of acceptance [16]. The structure of the Technology Acceptance Model is shown in Figure 3. The model proposes five key elements, which are:
Perceived usefulness: the extent to which individuals perceive that a new system or technology improves their productivity when using it. Perceived ease of use: the extent to which individuals perceive that they need to work hard when using a new system or technology. Attitude toward use: the individual’s positive or negative subjective feelings when using the new technology. Behavioral intention: the measurable degree to which an individual has the will to accomplish a particular behavior. External Variables: External factors affecting the individual’s use of the new technology, including “design features of the technology itself” and “various supporting conditions and interferences, such as the policy environment, organizational structure, and task characteristics.”
TAM model structure
In the technology acceptance model, behavioral intentions are used to represent the acceptance of new technologies, and the two primary drivers that determine acceptance of technology are perceived usefulness and perceived ease of use, respectively. This paper utilizes the model’s clear elements, concise relationships, and strong explanatory and predictive properties of behavior to explain the acceptance of new technology-enabled physical education innovations.
The most commonly used correlation coefficient analysis method Pearson correlation, also called Pearson rank correlation, Pearson correlation is both an upgrade of the Euclidean distance and an improvement of the cosine distance when the dimensionality is missing [17]. It is usually used to measure the similarity of the linear relationship between two variables, and the range of Pearson correlation coefficient values is generally {-1, 1}, and its formula can be expressed in equations (3) and (4):
The principle of action of regression analysis is to study the relationship between independent variables and dependent variables based on quantitative statistics of interdependence or influence between multiple sets of independent variables. In regression analysis, if there are two or more independent variables, it is called multiple regression [18].
After several years of popularization and application, the multiple linear regression analysis method has been widely used to deal with various kinds of problems. Its defined model is briefly described as follows:
In real life, if
The system of equations can be expressed in a matrix as:
Style:
Matrix
In the actual use of the model, hypothesis testing is also needed to determine whether the model has a good fit with the actual data, the significance of the linear relationship of the model, and so on.
In order to investigate the teaching effect of physical education teaching strategies based on new technology empowerment, this paper takes aerobics teaching as an example and conducts a controlled experiment with the traditional physical education teaching mode. The experimental group adopts the teaching strategy of this paper, while the control group adopts the traditional teaching mode, with 50 students in each group, and the teaching duration is 8 weeks (16 credit hours).
In order to exclude the influence of other factors on the experiment, first of all, the students in the experimental group and the control group should be tested with the Functional Movement Screening (FMS) test before the experiment, and the results of the comparative analysis of the FMS test are shown in Table 1.
FMS test comparison analysis results
| Evaluation index | Experimental group(X±S) | Control group(X±S) | T | P |
|---|---|---|---|---|
| Squat | 2.12±0.38 | 2.31±0.40 | -0.638 | 0.493 |
| Stepped over the rack | 1.94±0.51 | 2.05±0.43 | -0.516 | 0.615 |
| Straight arrow | 1.94±0.49 | 1.91±0.60 | 0.164 | 0.706 |
| Shoulder activity degree | 2.61±0.75 | 2.65±0.48 | -0.442 | 0.731 |
| Keep your legs on your back | 2.56±0.29 | 2.35±0.71 | 1.481 | 0.158 |
| Push-ups | 2.49±0.47 | 2.58±0.45 | -0.517 | 0.602 |
| General assessment | 1.71±0.67 | 1.63±0.54 | 0.611 | 0.547 |
According to the data analysis in Table 1, there is a maximum difference of 0.21 between the FMS functional movement assessment scores of the two groups of experimental subjects, with no significant difference. An independent samples t-test of the obtained data revealed that the minimum p-value of the single and total assessments of the functional movements of the two groups of students was 0.158, which was greater than 0.05, indicating that there was no significant difference between the FMS assessment scores of the students in the two groups before the experiment, so the selected experimental group and the control group students meet the requirements of the experiment.
At the end of the experiment, the movement skill scores, performance scores, and total scores of the two groups of students were compared and analyzed, respectively. The object of the motor skill assessment is to assess the prescribed routine movements for aerobics level 1. Performance assessment mainly includes form, quality, special techniques, psychological factors, personality and temperament, interest, artistic cultivation, and so on. The structure of the total score is 30% of the theoretical score, 40% of the skill score +30% of the usual score. Table 2 displays the post-experimental comparative analysis.
The comparison analysis results after the experiment
| Evaluation index | Group | Number | X±S | T | P |
|---|---|---|---|---|---|
| Action skill | Experimental group | 50 | 86.43±2.14 | 6.418 | 0.001 |
| Control group | 50 | 81.27±1.61 | |||
| Expressiveness | Experimental group | 50 | 80.74±2.17 | 4.816 | 0.002 |
| Control group | 50 | 76.35±2.96 | |||
| Total score | Experimental group | 50 | 90.34±2.32 | 8.225 | 0.000 |
| Control group | 50 | 81.87±14.62 |
As can be seen from Table 2, after 16 hours of experimental teaching, the average of the set movement assessment scores of the students in the experimental group and the control group were 86.43 and 81.27, respectively, with a p-value of 0.001, which is less than 0.05, and there is a significant difference. This indicates that the new technology-enabled aerobics teaching has a good effect on students’ mastery of movement skills, which stems from the fact that the new technology-enabled physical education teaching strategy has a strong relevance as well as the ability to provide students with real-time feedback and corrective suggestions on their movement practice. The aerobic performance scores of the experimental and control groups were 80.74 and 76.35, respectively, with a difference of 4.39 points. The performance score data of the two groups was subjected to an independent sample t-test, and the p-value was 0.002, which was less than 0.05 and markedly different. The use of new technology empowerment in the physical education teaching strategy has a better effect on the student’s performance scores in sets, as indicated. The mean score of the overall performance test of the experimental group was higher than that of the control group (90.34>81.87), and the T-value of the two groups was 8.225, with a p-value of 0.000, which is less than 0.05, and is significantly different. Therefore, compared to traditional teaching methods, new technology-enabled physical education teaching strategies can help students master their motor skills as soon as possible, enhance their performance, and achieve better academic performance.
In order to further explore the maximum advantages of the new technology-enabled physical education teaching strategy, this paper analyzes the changes produced by the experimental intervention by conducting stage assessments of the movements of students in the experimental group and the control group in the early, middle and late stages of the experiment, respectively, and the results of the comparative analysis of the staged assessment of the movements are shown in Table 3.
The comparison analysis results of the analysis of stage action appraisal
| Evaluation stage | Experimental group(X±S) | Control group(X±S) | T | P |
|---|---|---|---|---|
| Week 2 | 72.41±1.72 | 71.76±1.88 | 0.715 | 0.143 |
| Week 3 | 75.23±1.76 | 73.85±1.42 | 1.047 | 0.064 |
| Week 4 | 79.31±2.24 | 76.57±3.51 | 2.626 | 0.081 |
| Week 5 | 81.67±2.12 | 78.41±3.35 | 5.052 | 0.004 |
| Week 6 | 84.21±1.97 | 80.15±1.64 | 6.741 | 0.003 |
| Week 7 | 86.49±1.58 | 81.36±1.44 | 7.118 | 0.000 |
| Week 8 | 88.14±1.47 | 82.61±1.68 | 10.315 | 0.000 |
As can be seen from Table 3, the independent samples T-test was conducted on the stage movement assessment data of the experimental group and the control group students respectively, and the results showed that the P-values of the data of the 2nd, third and fourth weeks were 0.143, 0.064 and 0.081 respectively, which were all greater than 0.05, and there was no significant difference, which indicated that the gap between the two groups of students’ movement learning effectiveness was relatively small at the beginning stage of the formation of movement skills, but the gap gradually became larger with time. The gap gradually became larger. In the movement assessment of the fifth week, the mean and standard deviation of the movement assessment of the students in the experimental group was 81.67±2.12, and that of the control group was 78.41±3.35. The mean value of the experimental group was slightly higher than that of the control group, with a p-value of 0.004, p<0.05, and the two groups had a significant difference. The mastery of the movement skills of the experimental group was better than that of the control group students, which indicated that the experimental group had a more significant change in the mid-stage of learning. More significant changes. In the movement assessment of the sixth week, the p-value of the two groups was 0.003, showing a significant difference. In the movement assessment of the seventh week, the mean and standard deviation of the movement assessment of the students in the experimental group and the control group were 86.49±1.58 and 81.36±1.44, respectively, with a p-value of 0.000, which was less than 0.001, indicating that there was a very significant difference between the two groups. The P-value of the two groups in the movement assessment of the eighth week was 0.000, which represents a significant distinction. By analyzing the results of each week’s assessment, it can be seen that the classroom learning of the students in the experimental group and the control group has a large change, but the change is less significant in the early stage, but the change is more significant from the beginning of the middle stage to the late stage, and the significance of the difference is gradually increasing.
In order to assess learners’ acceptance of new technology-enabled physical education, this study adopted the Technology Acceptance Model (TAM), which has been widely used in the field of new technology applications, to construct a smart teaching environment based on new technology-enabled teaching strategies, and investigated the extent to which students need a smart teaching environment in physical education by means of a questionnaire.
The results of the survey on students’ need for a smart physical education environment obtained in this study are shown in Figure 4. The labels 0 to 5 in the figure indicate very little need, relatively little need, average, relatively need, and very much need, respectively.
Requirements survey results
As shown in Figure 4, 47.26% of the students need the smart physical education teaching environment relatively, and 20.46% of the students need the smart physical education teaching environment very much, which shows that most of the students have a high degree of need for the smart teaching environment constructed based on new technology-enabled teaching strategies.
The constructed smart sports teaching environment is divided into hardware and software environments, and the hardware environment includes hardware facilities (H1), smart sports classroom (H2), smart sports venues (H3), and smart sports teaching facilities (H4), and the software environment includes library resources (S1), smart teaching platform (S2), smart education model (S3), digital resources (S4), and digital services (S5). In this paper, the constituent elements of the smart sports teaching environment are surveyed again for the degree of demand, and the results of the survey on the degree of demand for the elements are shown in Figure 5.
Factor demand degree survey results
As can be seen in Figure 5, students have the highest proportion of demand for the software environment: the highest demand for library resources (18.91%) and the smart teaching platform (14.94%), followed by the hardware environment: smart physical education teaching facilities (14.69%) and smart sports venues (14.09%).
Through the questionnaire on the feasibility of the hardware environment and software environment of the students, summarized data found that more than 85% of the students believe that the hardware environment and software environment can improve the quality of teaching, help them improve their learning of physical education and other related knowledge, and scientific and effective physical exercise. The design of a new technology-enabled physical education strategy in this paper is feasible, as demonstrated by this.
In order to ensure the scientific validity of the study, this study applies Pearson correlation analysis to analyze the external variables of this questionnaire (including the hardware environment HE and the software environment SE), perceived usefulness (PU), perceived ease of use (PEU), attitude of use (AU) and behavioral intention (BI), and the results of the Pearson correlation analysis are shown in Table 4.
Pearson correlation analysis results
| HE | SE | PU | PEU | AU | BI | ||
|---|---|---|---|---|---|---|---|
| HE | Pearson correlation | 1 | 0.531** | 0.556** | 0.567** | 0.529** | 0.543** |
| Sig. (two-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| Case number | 438 | 438 | 438 | 438 | 438 | 438 | |
| SE | Pearson correlation | 0.531** | 1 | 0.545** | 0.582** | 0.526** | 0.557** |
| Sig. (two-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| Case number | 438 | 438 | 438 | 438 | 438 | 438 | |
| PU | Pearson correlation | 0.556** | 0.545** | 1 | 0.604** | 0.602** | 0.529** |
| Sig. (two-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| Case number | 438 | 438 | 438 | 438 | 438 | 438 | |
| PEU | Pearson correlation | 0.567** | 0.582** | 0.604** | 1 | 0.582** | 0.634** |
| Sig. (two-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| Case number | 438 | 438 | 438 | 438 | 438 | 438 | |
| AU | Pearson correlation | 0.529** | 0.526** | 0.602** | 0.582** | 1 | 0.657** |
| Sig. (two-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| Case number | 438 | 438 | 438 | 438 | 438 | 438 | |
| BI | Pearson correlation | 0.543** | 0.557** | 0.529** | 0.634** | 0.657** | 1 |
| Sig. (two-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
| Case number | 438 | 438 | 438 | 438 | 438 | 438 |
As shown in Table 4, the Pearson correlation coefficient between the hardware environment and the software environment is 0.531>0, with significance P<0.01, so the hardware environment and the software environment are significantly positively correlated. That is, the better the hardware environment develops, the better the software environment develops, which proves the scientific nature of the classification of smart sports teaching environments.
The Pearson correlation coefficients of hardware environment and perceived ease of use, perceived usefulness, attitude toward use, and willingness to act are 0.556, 0.567, 0.529, and 0.543 respectively, all of which are greater than 0, with significance P=0.000<0.01, so hardware environment is significantly positively correlated with perceived ease of use, perceived usefulness, attitude toward use, and willingness to use.
The Pearson correlation coefficients of software environment and perceived ease of use, perceived usefulness, attitude toward use, and willingness to act are 0.545, 0.582, 0.526, and 0.557, respectively, which are all greater than 0, and the significance P=0.000<0.01, so the software environment is significantly positively correlated with perceived ease of use, perceived usefulness, attitude toward use, and willingness to use.
It can be seen that the higher the degree of intelligent development of the hardware environment and software environment in the intelligent sports teaching environment, the students’ perceived ease of use, perceived usefulness, attitude toward use, and willingness to use will grow significantly. This confirms the practicality and effectiveness of the innovatively designed technology-enabled physical education teaching strategy in this paper.
To further prove the usefulness of new technology-enabled physical education teaching strategies, this study utilized a linear regression model with the independent variables perceived ease of use, perceived usefulness, attitude toward use, and willingness to use to describe the dependent variable smart physical education teaching environment (hardware environment and software environment), proving the necessity of building a smart physical education teaching environment based on innovative teaching strategies.
The adjusted R-square of the dependent variable of linear regression analysis is 0.584<1, which indicates that perceived ease of use, perceived usefulness, attitude toward use, and willingness to use can explain 58.4% of the intelligent physical education teaching environment, with a high degree of explanatory significance. The DW value (Durbin Watson) is 1.874, which is greater than 1.5 and less than 2.5, proving that the independent variable autocorrelation between them is not serious so that the model can be applied reasonably.
The coefficients of the independent variables of the linear regression analysis are shown in Table 5. As shown in Table 5, Perceived ease of use, Perceived usefulness, and Attitude to use have a significant effect on the smart sports teaching environment (hardware environment and software environment). Significance P=0.000<0.01. Behavioral willingness significance P=0.006<0.05, which also has a significant effect on the smart sports teaching environment. In general, a VIF value is less than 5 can be applied, and the maximum VIF value of the model in this study is 2.174, which is less than 5, so the model can be reasonably applied.
Independent coefficient
| Model | Unnormalized coefficient | Standardized coefficient Beta | t | Sig. | Common linear statistics | ||
|---|---|---|---|---|---|---|---|
| B | Standard error | Tolerance | VIF | ||||
| Constants | 0.932 | 0.146 | 6.046 | 0.000 | |||
| Perceived usefulness | 0.256 | 0.034 | 0.284 | 5.561 | 0.000 | 0.496 | 2.015 |
| Behavioral intention | 0.131 | 0.035 | 0.145 | 2.764 | 0.006 | 0.462 | 2.163 |
| Attitude toward using | 0.197 | 0.032 | 0.231 | 4.357 | 0.000 | 0.460 | 2.174 |
| Perceptual ease of use | 0.234 | 0.031 | 0.263 | 5.372 | 0.000 | 0.519 | 1.926 |
The histogram of the residuals of the linear regression analysis is shown in Figure 6. As can be seen from Fig. 6, the purple curve is the normal curve, the blue bar is slightly higher than the normal curve, and the histogram shows a left-right symmetrical pattern, which indicates that the residuals of the constructed model are approximately normally distributed.
Linear regression analysis of residual histogram
In summary, the results of the regression model are basically accurate, and there is a necessity for the construction of a smart physical education teaching environment, i.e., the new technology-enabled physical education teaching strategy designed in this paper has a high practical value.
This paper outlines the innovative design of the teaching strategy for new technology-enabled physical education and combines the technology acceptance model with the teaching practice to analyze the effects of its implementation.
The FMS test results for the students in the experimental group and the control group before the experiment show a p-value greater than 0.05, indicating that both groups of students are at the same level in terms of basic physical function. Additionally, the sample selection meets the requirements of the experiment.
The mean values of the movement skill scores of the students in the experimental group and the control group were 86.43 and 81.27; the mean values of the performance scores were 80.74 and 76.35; and the mean values of the total scores were 90.34 and 81.87, respectively. All the scores of the students in the experimental group were higher than those of the control group, and the p-values of the two groups were less than 0.05, which is a significant difference. Therefore, compared with the traditional teaching methods, the new technology-enabled physical education teaching strategies can help students master their motor skills as soon as possible, enhance their performance, and achieve better academic performance.
According to the stage movement assessment, the p-values for the 2nd, third, and fourth weeks were 0.143, 0.064, and 0.008, which were higher than 0.05 and did not have any significant difference. Whereas the p-values for weeks 5, 6, 7, and 8 were less than 0.05 with significant differences. Analyzing the weekly assessment results reveals a significant shift in the classroom learning of students in both the experimental and control groups. While the early stage difference is not statistically significant, it becomes more significant from the middle stage to the late stage, with the significance of the difference steadily increasing.
More than 85% of students hold the belief that the hardware and software environment can enhance the quality of teaching and learning, aiding them in improving their understanding of physical education and other related knowledge, as well as scientific and effective physical exercise. This paper determines the feasibility of the new technology-enabled physical education strategy.