Intelligent Implementation and Big Data Analysis of Strategies for Incorporating Civic and Political Elements in Physical Education Instruction
Data publikacji: 19 mar 2025
Otrzymano: 27 paź 2024
Przyjęty: 18 lut 2025
DOI: https://doi.org/10.2478/amns-2025-0469
Słowa kluczowe
© 2025 Ze Xu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
To do a good job of ideological and political work in colleges and universities, all other courses should guard a section of the canal and plant a good field of responsibility, so that all kinds of courses and ideological and political theory courses are traveling in the same direction, forming a synergistic effect. Colleges and universities are an important part of China’s education system, and carrying out ideological and political education is the essential need to implement the principle of cultivating a moral character and cultivating a high-quality talent team [1–4]. The relevant documents issued by the Ministry of Education pointed out that the construction of the Civic and Political construction of the college curriculum should be regarded as an important task to comprehensively improve the quality of talents, and to cultivate socialist builders and successors who are all-rounded in morality, intelligence, physical fitness, aesthetics, and labor, which further provides an important guidance for the development of the Civic and Political education in colleges and universities [5–8]. How to integrate the elements of Civics and Politics into the teaching of sports courses in colleges and universities, so that students in sports courses, based on the improvement of physical quality, to further develop the spirit of collectivism, the sense of competition and cooperation, as well as a sense of identification with the country and the nation, is an important issue in the development of sports in colleges and universities at present [9–11]. With the current rapid development of modern information technology, artificial intelligence has covered all industries, which has brought great influence to people’s work, life, and learning, and the way and method of education has also changed dramatically [12–14]. The construction of college sports course ideology and politics should also think about how to use good artificial intelligence technology to change the traditional teaching methods of sports courses, and intelligently integrate the elements of ideology and politics into the teaching process, with a view to cultivating college talents with firm ideals and beliefs, moral sentiments, cultural qualities and disciplinary concepts, and to achieve win-win effects of physical exercise and ideological and political education [15–18].
The article proposes four strategies for integrating the elements of Civics and Politics into physical education teaching, and analyzes the results of the realization of this strategy in the process of physical education teaching through big data. First, the basic process of data mining is introduced, and the principles of SOM clustering algorithm and improved FP-growth algorithm are explained. Secondly, the SOM clustering method is used to analyze the performance of male and female students in sports programs after clustering in physical education teaching data to provide data support for the evaluation or improvement of physical education teaching. The FP-growth algorithm is used to mine and analyze the different patterns of college students’ physical fitness and health, and the corresponding suggestions are given at the same time. Thirdly, teaching experiments were designed and paired t-tests were conducted using SPSS software to verify the teaching effect of integrating Civics elements into physical education teaching.
With the emergence of big data, the ideological concepts of college students are also constantly updated, which puts forward higher requirements for the work of political thinking and education in college sports courses, and requires that the political thinking and education in college sports courses must keep abreast of the times. For this reason, this paper proposes four strategies for integrating the elements of ideology and politics into sports education.
Teachers can make full use of big data technology to conduct a comprehensive and accurate analysis and assessment of students’ value orientation, behavioral preferences, mental health and other aspects, providing a scientific basis for the teaching design of Civics in physical education courses. Teachers can also make use of the Internet, mobile devices and other technical means to realize the organic integration of online and offline teaching, and through virtual experiments, simulation exercises and other teaching means, create situations to help students better understand and experience the elements of Civics and Politics in the physical education curriculum. At the same time, teachers should also use big data technology to realize comprehensive monitoring and evaluation of the teaching quality of physical education courses, students’ learning situation, psychological changes and other aspects, and build a perfect implementation system of the ideology and politics of the curriculum.
First of all, it is necessary to improve the data analysis abilities of teachers. Physical education teachers in colleges and universities need to master the basic methods and skills of data analysis, and be able to analyze and process a large amount of data, so as to derive valuable information, better understand the learning situation and ideological dynamics of students, and then carry out targeted teaching of course ideology and politics. Secondly, it is necessary to strengthen the ability of teachers to use information technology. Physical education teachers in colleges and universities can only improve their teaching of course ideology and politics if they can skillfully apply various information technology tools, such as data mining and data visualization. Finally, it is essential to cultivate the innovative consciousness and practical ability of teachers. Only by constantly exploring and trying new teaching methods and means, and constantly innovating the content and form of curriculum Civics teaching, can teachers transform theoretical knowledge into practical action, better meet the needs of students, and implement curriculum Civics teaching into physical education teaching.
First of all, we should focus on the collection of daily physical education data, change the one-sided and accidental evaluation that relies only on the final examination, increase the course-embedded evaluation, real-time detection and tracking of students’ skill mastery and psychological state, and obtain continuous and accompanying student evaluation data. Secondly, the “big value” of evaluation data resources should be deeply explored to expand the diversified applications of big data, such as providing early warning functions, so that students can carry out self-supervision and management through real-time data, and teachers can grasp the changes in students’ thinking through stage-by-stage feedback to provide data support for further integration of course civic politics. Finally, it is necessary to strengthen the management of the big data evaluation system, macroscopically analyze and grasp the ideological situation of the group, build a firm ideological position, and supervise the effectiveness of the implementation of course ideology and politics.
Teachers should collect and integrate teaching resources of different disciplines and fields with the help of the big data teaching platform, and classify, screen and integrate these resources to constantly optimize and improve the teaching resources of course civics and politics. First, it is necessary to integrate the network education platform, introduce high-quality online open course resources, expand the carrier of the implementation of the sports course ideology and politics, guide teachers to rely on the network teaching platform, carry out online and offline hybrid teaching, and extend the spatial and temporal scope of the implementation of the sports course ideology and politics. Secondly, we should vigorously promote the construction and application of online course resources to provide a platform for blended teaching, while at the same time promoting the sharing of online open courses on a wider scale. Thirdly, it is necessary to build a smart sports place based on big data, encourage teachers to adopt teaching methods such as blended teaching, flipped classroom, inquiry teaching, and promote the reform of sports classroom teaching in colleges and universities, and at the same time, try to build a live teaching platform to broadcast real-time webcasts of the teachers’ teaching, so as to realize the seamless connection between on-line and off-line, and both inside and outside the classroom.
Cluster analysis and association rule method are two effective methods of big data mining, which can reveal the relationship between data. In this paper, these two methods are used to analyze big data on the realization effect of integrating the elements of ideology and politics into physical education teaching, and the following is the basic principle of the method.
Data mining requires a large amount of data to be able to extract potential information that is valid and meaningful. Processing data in Excel can only be called data analysis. Considering from the data itself, data mining usually has six steps: defining the problem, establishing a data mining library, analyzing the data, preparing the data, building a model, and evaluating the model, as follows: Define the problem. Understanding the target data and task requirements in advance is a prerequisite for the process of “knowledge discovery”, and there is a clear definition of the target. For example, mining association rules, may be used to analyze customer shopping information, may also be used to improve the warehouse stocking program. Establishment of data mining repository. The data required for data mining is generally analyzed from data warehouses and data lakes, and then extracted to form a dataset that is relevant to the task. The dataset includes three types of data collections: training set, validation set, and test set. Data Analysis. The target data needs to be analyzed to identify the data fields that have the most impact on the output results, and then to determine whether to output or redefine them. Therefore, it is important to look for a data processing tool with a simple interface, friendly operation, and powerful functions to assist in this part of data analysis. Data preparation. Some processing of data is required before mathematical modeling. These processing tasks include: picking variables, picking records, creating new variables, and converting variables. Modeling. Building a mathematical model is an iterative process. It is first necessary to determine which model works best for the target problem. The model is trained by the training set and tested by the test set. Since the training set and the test set come from the same source, an independent data set is needed to test the accuracy of the model, which is why a validation set is also needed. Evaluate the model. After building the model, it is necessary to conclude the output results, evaluate the obtained results and explain the model value. The test set has an impact on the model evaluation due to the limitations of the data characteristics, because of the limitations of the test set source, the test set accuracy can only have a reference value for the data used to build the model. After validation of the model, there are two uses of the model, one for reference by the researcher and the other for use on different data sets.
Self-organizing mapping (SOM) clustering algorithm is an unorganized neural network learning algorithm. Its basic principle is as follows [19]:
Among them:
Let the output plane M consist of k nodes (called prototype vectors), each of which has the same dimensions as the gene sequence, i.e:
Among them:
During the training process, a gene sequence x is randomly selected from the input dataset every time an iteration is done, and then the distance between
Namely:
After determining the best matching unit (BMU), the best matching unit (BMU) as well as the weights of the nodes within the neighbor radius
In the above equation,
In practical applications, clustering effectiveness needs to be measured, i.e., the clustering results are measured.
The validity measure Silhouette index is a very effective index of clustering validity. Its calculation process and principle are as follows [20]:
For a given cluster
If the value of
The global Silhouette value (GS) is defined for any of the divisions
Apriori algorithm is the classic data mining algorithm for mining frequent itemsets and association rules to find implicit relationships between items from large-scale datasets. The core idea is to generate candidate items and their support by concatenation, and then generate frequent itemsets by pruning [21]. If an item set is frequent, then its child item sets must also be frequent. If an item set is not frequent, then its parent item sets must also not be frequent.
To discover frequent sets using Apriori algorithm, first we need to find frequent itemsets and then calculate the confidence level of association rules based on the support of frequent itemsets to obtain association rules.
Item set: a set containing zero or more items is called an item set. In the shopping basket transaction, each item is an item. A purchase behavior contains multiple items, the combination of which constitutes an itemet.
Support count: the number of times the itemset appears in the transaction.
Frequent itemset: a collection of items that often appear in one piece.
Association rule: implies that there may be a strong relationship between two items.
Support: indicates the proportion of transactions that contain both A and B to all transactions. If P(A) is used to denote the proportion of transactions that contain A, the formula is expressed as:
Confidence: indicates the proportion of transactions containing A that also contain B, i.e., the proportion of transactions containing both A and B to those containing A.
Formula expression:
Two important laws of Apriori algorithm:
Law 1: If a set is a frequent itemset, all its subsets are frequent itemsets.
Lemma 2: If a set is not a frequent itemset, then all its supersets are not frequent itemsets.
While the FP-growth algorithm is built on Apriori, it uses an advanced data structure to reduce the number of scans and significantly speed up the algorithm. The FP-growth algorithm scans the database, whereas the Apriori algorithm scans each potentially frequent itemset to determine whether a given pattern is too frequent, and thus the FP-growth algorithm is usually much faster than the Apriori algorithm [22].
Advantages of the FP-growth algorithm include its speed, which is much faster than Apriori. However, it is also difficult to implement. Applicable data types: discrete data FP-growth optimizes the Apriori algorithm and improves its efficiency. During the execution of FP-growth algorithm, frequent patterns can be found by traversing the dataset only a less number of times, and its process is divided into (1) Constructing the FP tree. (2) Mining Fast Mining Frequent Item Sets from FP Trees. The FP-growth algorithm stores the data in such a compact data structure called FP-tree, and then the FP-tree connects other more similar elements through links. The FP tree stores the frequency of occurrence of items, and each item is stored in this tree as a path. The links between similar items are called node links, which serve to discover the location of similar items more accurately and quickly.
Another metric is involved here: the degree of lift, which represents the ratio of the proportion of transactions containing A that also contain B to the proportion of transactions containing B. The ratio of the proportion of transactions containing A to the proportion of transactions containing B is the ratio of the proportion of transactions containing B to the proportion of transactions containing B. The formula is:
The degree of elevation reflects the correlation between A and B in the association rule, the degree of elevation is greater than 1 and the higher indicates the higher positive correlation, the degree of elevation is less than 1 and the lower indicates the higher negative correlation, the degree of elevation is equal to 1 indicates no correlation.
Administrative 231 (N=40) and 232 classes (N=40) in the L university were selected as the objects of analysis, and after implementing the strategy of integrating the Civic and Political elements in physical education teaching in this university, the SOM algorithm and FP-growth algorithm were used to mine and analyze the data on students’ physical fitness and to verify the effect of integrating the Civic and Political elements in physical education teaching through the design of the teaching experiments.
Figure 1 shows the percentage of each individual physical quality of the boys in the four categories A, B, C and D after SOM clustering. As shown in Figure 1, the values of each variable for boys in category A were the lowest among the four categories. In the 1000m, 50m, standing long jump, and pull-up events, it was the category C students who performed the best and the category A students who performed the worst. In the seated forward bending test program to assess lung capacity, D students performed the best while A students performed the worst. C students had excellent performance in the 1000m and 50m test events, while A and D students performed poorly in these two events. Comparing the pull-up program for students in categories A, C, and D, the same situation exists: students in category C perform well while students in categories A and D perform poorly. Comparison of seated forward bends with 1000m and 50m events tested for A, B, and C students showed that all C students performed the best, B students the second best, and A students the worst. Comparison of the testing of the pull-up program and the seated forward bend test program for students in categories A, B, C, and D revealed a negative correlation. The testing of the seated forward bending program as well as the spirometry program revealed that the performance of all four categories of students in both programs was the best for the D students, the second best for the C students, and the worst for the A students.
Students’ single-subject physical quality ratio(male)
Figure 2 shows the percentage of each single physical quality of girls in categories A, B, C and D after SOM clustering. Compared with the boys’ clustering results, the distribution of the values of each variable in different categories in the girls’ clustering results is more complicated. In the 50m and 800m test events, both category A students performed the best and category C students performed the worst. Students in categories A and B, who performed better in the sit-up test, showed a significant decline in the sit-up program compared with students in categories C and D, while students in categories C and D showed an increase. In the standing long jump, the test was similar to the sit-up program, with students in category D performing the best and students in category A performing the worst. Overall, in the female group, students who performed well in the seated forward bend test event also performed well in the 800m event. Similar to the case of male students, the percentage of variables was negatively correlated between the two items, seated body flexion and sit-ups, for all four categories of female students.
Students’ single-subject physical quality ratio (female)
Setting min_Sup=8%, Confidence=50%, sports behavior stage as output, the maximum number of entries of rule results is 6, and a total of 25 pieces of knowledge are found. After screening, a total of 9 knowledge items with decision-making significance were found. The association rule knowledge of male college students is shown in Table 1.
Rule knowledge discovery in different physical exercise behaviors of male
| Number | Rule | Support (%) | Confidence (%) | |
|---|---|---|---|---|
| Consequent | Antecedent | |||
| 1 | Anticipation | Left back hook=excellent, body fat rate=normal, reaction=medium, VO2max=bad | 9.023 | 51.104 |
| 2 | Anticipation | Left back hook=excellent, reaction=medium, VO2max=bad | 9.951 | 51.336 |
| 3 | Anticipation | Left back hook=excellent, reaction=medium, VO2max=bad, right back hook=excellent | 9.139 | 50.242 |
| 4 | Preparation | Reaction=passed, grip strength=bad | 8.791 | 67.44 |
| 5 | Preparation | Reaction=passed, VO2max=bad | 8.726 | 61.313 |
| 6 | Preparation | Reaction=bad | 8.968 | 91.579 |
| 7 | Action | Reaction=excellent | 9 | 100 |
| 8 | Action | Reaction=good, VO2max=bad, right back hook=excellent | 8.388 | 55.414 |
| 9 | Action | Reaction=good, grip strength=bad | 8.379 | 59.245 |
In the anticipation stage of male college students’ sports behavior, there were three meaningful knowledge items (No. 1, 2, and 3). Summarizing the three pieces of strongly correlated knowledge, we found that the flexibility (two-handed back hook) of male college students in the anticipation stage was “excellent”, the reaction time was “moderate”, and the cardiorespiratory fitness (VO2max) was “poor”. The support degree of the three rules is 9.023%~9.951%, and the confidence level is 50.242%~51.336%, which shows that the main performance affecting the physical health of male college students at the expected stage is low cardiorespiratory fitness, which may be the reason why sudden deaths are often seen in the college physical fitness test for male students who run 1,000 m. In addition, it is worth paying attention to the fact that the reaction time is only “medium” because the nervous system flexibility of students should be at the most sensitive stage in college. In addition, the fact that male college students’ reaction time is only “medium” is also worthy of attention, because at the university stage, the flexibility of students’ nervous system should be at the most sensitive stage.
There are 3 rules (No. 4, 5, and 6) that are strongly associated with physical health knowledge among male college students in the preparation stage. Serial number 6, “poor” in the response of male college students in the preparation stage, which has a support level of 8.968% and a confidence level of 91.579%, deserves sufficient attention. No. 4, “pass” on reaction time and “poor” grip strength, 8.791% support, 67.44% confidence. No. 5, “pass” at reaction time and “poor” VO2max has a support level of 8.726% and a confidence level of 61.313%. Collectively, male college students in the preparation phase had poor reaction times, poor strength qualities, and poor cardiorespiratory fitness. The analysis suggests that the “readiness stage” may be due to the fact that this group of students already perceive their physical health as poor and have some motivation to play sports in order to improve their physical condition. Students at this stage should be actively guided to strengthen their attitudes toward sports, enhance their learning of sports skills, and allow them to have a positive emotional experience in the process of sports, so as to promote the transformation of their sports to the action stage.
The association rules of male college students in the action phase unearthed three meaningful knowledge (serial numbers 7, 8, and 9). Knowledge number 7, with a response time of “excellent”, has a support level of 9% and a confidence level of 100%, which, when compared to knowledge number 6, suggests that physical education can positively and effectively enhance the neurological flexibility of male college students. Knowledge of numbers 8 and 9 indicates that VO2max and grip strength remain poor even after physical education. The reason for this is that the teaching is not well targeted, and it is also possible that the intensity of the exercise will give the exerciser a “painful” emotional experience and thus produce resistance behavior, which leads to the reluctance of the students to perform strength exercises, and thus their grip strength is poor.
Therefore, for male college students who actively participate in sports, they should be guided to exercise scientifically and intensively with sports prescription, mainly develop their cardiorespiratory fitness and strength exercises, and cultivate their hard-working and enduring spirit.
Setting min_Sup=10%, Confidence=60%, sports behavior stage as output, the maximum number of entries of rule results is 6, and a total of 65 pieces of knowledge are found. After screening, a total of 7 knowledge items with decision-making significance were found. The association rule knowledge of female college students is shown in Table 2.
Rule knowledge discovery in different physical exercise behaviors of female
| Number | Rule | Support (%) | Confidence (%) | |
|---|---|---|---|---|
| Consequent | Antecedent | |||
| 1 | Pre-anticipation | Grip strength=bad, right back hook=excellent | 11.398 | 65.448 |
| 2 | Anticipation | Grip strength=passed, reaction=medium, right back hook=excellent | 12.484 | 74.709 |
| 3 | Anticipation | Body fat rate=overweight, grip strength=passed, BMI=normal | 13.207 | 66.804 |
| 4 | Anticipation | VO2max=medium, grip strength=passed, right back hook=excellent | 10.028 | 63.674 |
| 5 | Action | Grip strength=bad, reaction=medium, right back hook=excellent | 10.612 | 70.578 |
| 6 | Action | Grip strength=bad, body fat rate=overweight, BMI=normal | 11.403 | 67.737 |
| 7 | Action | Grip strength=bad, VO2max=bad, reaction=medium, BMI=normal | 11.964 | 66.372 |
Knowledge 1 is the only knowledge found in the pre-expectation stage of female college students’ sports behavior, poor strength and good flexibility are their physical characteristics, and their lack of sports ideas may be due to their “softness”, which can be seen that female college students pay more attention to flexibility and ignore the characteristics of strength qualities. This knowledge suggests that it is important to emphasize the importance of strength for the physical health of female students who do not regularly exercise.
There are three meaningful knowledge findings (No. 2, 3 and 4) for female college students in the anticipatory stage of physical activity behavior, with passable grip strength, excellent back hook, medium reaction time and medium VO2max as the characteristics of their physical fitness, but the knowledge of No. 3 reveals the contradictory phenomenon of overweight body fat percentage and normal BMI, and body shape evaluation. In this regard, more scientific methods of body composition evaluation, such as the relatively inexpensive and highly accurate bioelectrical impedance test, should be used for decision support. In addition, the physical health scores of female college students in the expected stage are moderate or passing, which need to be improved. Therefore, they should be actively encouraged to participate in sports and develop strength and cardiorespiratory endurance qualities in a comprehensive manner.
There are three knowledge findings available for decision support for female college students in the action stage (serial numbers 5, 6, and 7), and the knowledge finding of serial number 6 is the same as that of serial number 3, which shows that the physical fitness of female college students in this stage is characterized by poor strength, moderate reaction time, and excellent flexibility. Poor strength, medium reaction time, overweight body fat percentage, and low cardiorespiratory fitness may be the reasons for female university students’ participation in sports, and decision support may continue to encourage their participation in sports, form a sports habit, and increase their confidence in sports through regular physical fitness tests.
Taking the administrative class 231 (N=40) as the experimental class and class 232 (N=40) as the control class, the former integrated Civics elements into physical education teaching, while the latter used general physical education teaching methods, and the effect of the realization of the integration strategy was tested through the pre- and post-test changes of the Civics quality level test.
Before the beginning of the experiment, students in the experimental class and the control class were tested on the level of Civic and Political Quality, and the data obtained were analyzed. The results show that the Civic and political quality of the two groups of students in various dimensions of the data does not have a significant difference (p>0.05), indicating that the Civic and political quality of the two groups of students before the start of the experiment is at the same level, and has the conditions for the experiment, and can be carried out in the next step of the experiment.
Through three months, 24 hours of physical education class Civics teaching design practice, for the two groups of students for Civics quality of the questionnaire scale post-test statistics, and through the SPSS26.0 statistical software for independent samples T-test, the results obtained as shown in Table 3.
Post-test result comparison between experimental and control group
| Dimension | Control group | Experimental group | T | P |
|---|---|---|---|---|
| Patriotism | 2.612±0.706 | 3.625±0.546 | 9.610 | 0.000 |
| Collectivism | 2.869±0.389 | 4.157±0.952 | 7.660 | 0.000 |
| Team spirit | 2.591±0.514 | 3.514±0.519 | 8.045 | 0.000 |
| Rule consciousness | 3.006±0.581 | 3.915±0.522 | 8.172 | 0.000 |
| Fairness and justice | 3.116±0.304 | 4.031±0.478 | 8.946 | 0.000 |
| Sense of responsibility | 3.050±0.109 | 4.056±0.860 | 8.696 | 0.000 |
| The correct view of victory and defeat | 3.206±0.609 | 4.094±0.381 | 8.266 | 0.000 |
| The spirit of striving and enterprising | 2.943±0.436 | 3.813±0.728 | 8.780 | 0.000 |
| Will quality | 2.858±0.636 | 3.951±0.425 | 7.795 | 0.000 |
From the table, it can be intuitively seen that the P-value of nine dimensions such as patriotism is less than 0.05, which is a significant difference, and the mean value of the experimental class students are higher than the control class students, indicating that compared with the traditional sports teaching design, the curriculum Civic and political teaching design can make the level of students’ Civic and political qualities be significantly improved. To analyze the reasons, the high school physical education course Civics teaching design through mining and integration of Civics elements, clear Civics goals, optimize the teaching content, and at the same time the use of verbal incentives, situational teaching methods, group teaching methods, games, competitions, and other teaching methods Civics education penetration of the entire teaching process of physical education classes, so that the students are subjected to the positive impact of the subtle influence, and gradually cultivate students’ love for the community, teamwork, The students gradually cultivate good ideological and political qualities such as love for the collective, teamwork, observing rules, striving and enterprising, tenacious will, fair competition, and the courage to take responsibility.
Table 4 shows the comparison between the pre-test and post-test data on the level of Civic and Political Quality in the control class. As can be seen from the table, after the end of the teaching experiment on the control class of students’ civic and political quality of the pre-test and post-test analysis, through the paired samples t-test found that the data of the two groups are not significantly different (P>0.05), in the spirit of patriotism and other nine dimensions of the average value of a slight fluctuation, but not much different from the results of the pre-test. This indicates that traditional physical education teaching without integrating civics education has less impact on the level of students’ civics education.
Post-test and pre-test comparison of control group
| Dimension | Pre-test | Post-test | T | P |
|---|---|---|---|---|
| Patriotism | 2.450±0.557 | 2.612±0.706 | -0.912 | 0.323 |
| Collectivism | 2.752±0.333 | 2.869±0.389 | -0.055 | 0.261 |
| Team spirit | 3.037±0.602 | 2.691±0.514 | 1.507 | 0.068 |
| Rule consciousness | 2.941±0.315 | 3.006±0.581 | -0.353 | 0.112 |
| Fairness and justice | 2.966±0.640 | 3.116±0.304 | -0.051 | 0.703 |
| Sense of responsibility | 3.189±0.595 | 3.050±0.109 | 0.655 | 0.616 |
| The correct view of victory and defeat | 2.887±0.513 | 3.206±0.609 | -0.749 | 0.630 |
| The spirit of striving and enterprising | 2.455±0.021 | 2.943±0.436 | -1.309 | 0.198 |
| Will quality | 2.666±0.388 | 2.858±0.636 | -0.912 | 0.201 |
Table 5 shows the difference between the pre-test and post-test levels of the quality of Civics and Politics in the experimental class group. After 24 hours of practice in the design of the Civics and Politics teaching design in physical education courses, the paired-sample t-test of the data of the students in the experimental class in the nine aspects of patriotism and other aspects with the pre-test found that P < 0.05, with significant differences, and that the mean values of the post-test were higher than those of the pre-test, which were respectively improved by 0.673, 1.237, 0.791, 1.033, 1.355, 1.517, 1.528, 1.293, 0.939. It shows that the students’ quality of thinking and politics has been significantly improved after the teaching of thinking and politics in physical education courses.
Post-test and pre-test comparison of experimental group
| Dimension | Pre-test | Post-test | T | P |
|---|---|---|---|---|
| Patriotism | 2.952±0.384 | 3.625±0.546 | -10.003 | 0.000 |
| Collectivism | 2.920±0.342 | 4.157±0.952 | -8.625 | 0.000 |
| Team spirit | 2.723±0.341 | 3.514±0.519 | -8.880 | 0.000 |
| Rule consciousness | 2.882±0.046 | 3.915±0.522 | -9.655 | 0.000 |
| Fairness and justice | 2.676±0.610 | 4.031±0.478 | -9.166 | 0.000 |
| Sense of responsibility | 2.539±0.756 | 4.056±0.860 | -10.124 | 0.000 |
| The correct view of victory and defeat | 2.566±0.605 | 4.094±0.381 | -9.369 | 0.000 |
| The spirit of striving and enterprising | 2.520±0.395 | 3.813±0.728 | -11.745 | 0.000 |
| Will quality | 3.012±0.881 | 3.951±0.425 | -11.746 | 0.000 |
The study proposes an intelligent strategy for integrating civic and political elements into physical education, and analyzes its implementation effect using SOM clustering and FP-growth association rule algorithms with big data. It was found that in the anticipation stage of physical education and sports, the main manifestation affecting the physical health of male college students was low cardiorespiratory fitness, and the reaction time, strength quality, and cardiorespiratory fitness of male college students also needed to be improved. Female college students had a lower strength and better flexibility during the pre-anticipation phase. The physical quality of female college students in the action stage was characterized by poor strength, moderate reaction time, and excellent flexibility. Further encouragement is needed to encourage them to participate more in sports. Comparing the differences between the pre- and post-tests of the nine dimensions, including the spirit of patriotism, it was found that the difference between the pre- and post-tests of the control group’s level of civic and political qualities was not significant (p>0.05), while the post-test scores of the control group were significantly higher than those of the control group and their own pre-test levels (p<0.05). Therefore, it is believed that the use of SOM clustering and FP-growth association rule algorithms can fully excavate and analyze the potential associations of students’ physical fitness data, and fully reflect the real value of integrating Civic and Political elements in physical education.