Research on the Construction Path of Physical Education Teaching Reform Innovation Guarantee System Driven by Digital Technology

Digital technology promotes intelligent and automated sports teaching, and the extensibility of data storage can automatically identify every basic state in public sports teaching. Digital technology can make use of data quantification and information interconnection thinking to accurately grasp the real-time status of sports training of public sports students in colleges and universities, which is conducive to teachers’ better adjustment of teaching plans and optimisation of resource allocation. Digital technology bridges the gap between students’ needs and the movement process, realising students’ movement level, movement form, movement mode, movement process, and the collection of movement data intelligently so as to accurately understand the dynamic needs of public physical education students, and adjust the plan in time to improve the efficiency of the allocation of teaching resources [28-29].

Digital technology enables data sharing and in-depth integration of information, which can break data silos and enhance work efficiency. Public sports students can choose sports and understand the basic situation of school sports resources through the digital platform. Schools can collect and analyse big data to accurately understand the changes in the needs of public sports students and the use of school sports resources, so as to adjust the curriculum arrangement in time and enhance the efficiency of the use of school sports resources.

Digital technology optimizes the structure of physical education teaching and promotes the efficient development of teaching quality. Firstly, digital technology rationalizes the structure of the teaching guarantee system. Secondly, digital sports have advanced the teaching quality guarantee system. The seniorization of the structure of the guarantee system is a process of qualitative change in the guarantee system. Digital technology stimulates innovation in traditional safeguard systems, promotes the optimization of each link in the safeguard system, and promotes intelligence in each link. At the same time, digital technology triggers the safeguard system to generate new demand, thus achieving the advanced safeguard system structure. Digital technology changes the demand side by promoting the change of students’ behavior, thus creating more demand for safeguards.

First, keep up with the digital era and optimise guarantee services. Establish a networked public physical education teaching quality assurance system using digital technology and with the help of big data, cloud computing, and other information technology.

Second, quality-oriented, data visualisation. Digital technology can automate the generation of various data in the teaching process and provide support for quality assurance in physical education teaching reform.

Third, innovative mechanisms and multiple coordination. The construction of the quality assurance system for physical education reform requires the joint participation of the school-level sports administration department, the college-level sports administration department, and the department in charge of public sports.

The framework of the quality assurance system of physical education teaching reform is shown in Figure 1. The quality assurance system of sports teaching reform is a systematic and complex project, which takes the decision-making system, the teaching resources guarantee system, the teaching quality process monitoring system, the evaluation and diagnostic system of teaching quality, the organisational guarantee system of teaching quality, and the teaching quality regulation and feedback system as the main framework, and builds up the data-sharing service platform with the support of big data, computers, Internet of Things and other digital technologies.

Figure 1.

The framework of the quality assurance system of sports teaching reform

The Physical Education Teaching Steering Committee unites the development of quality assurance systems, major policies, and measures for physical education teaching in schools. The school’s Academic Affairs Office and Physical Education Team specifically organise and implement the system, with management experts and physical education teaching experts checking and supervising against quality standards and providing feedback for analysis and improvement. The organisational system of physical education teaching and management can be run in a closed loop, from the headmaster in charge to the Academic Affairs Office, the physical education team, teachers and students, and then from teachers and students back to the physical education team, the Academic Affairs Office and the headmaster. Through the closed-loop operation, new problems in teaching are constantly summarized, researched, and solved to improve the teaching process.

The overall planning of the physical education curriculum, the unified development of the teaching plan for each grade, the schedule of hours, each teacher to develop a unified lesson plan, the vice principal in charge, the director of the Office of Academic Affairs, the head of the physical education teaching and research group stamped in order to implement the lesson plan. Teachers’ teaching process can be checked in multiple ways through multiple channels, such as student evaluation, peer listening, and supervisory listening, by supervisors from the Supervisory Office. Students’ extracurricular physical exercise is checked and supervised through mobile phone exercise apps, self-study records on the Pan-Asia teaching platform, etc., so as to cultivate their lifelong habit of physical exercise.

The nature of the scheduling problem is a non-linear, multi-objective combinatorial optimisation problem that satisfies multiple constraints, specifically the combinatorial optimisation problem that combines students, teachers, courses, time and classrooms.

The process of scheduling is the process of conflict resolution. Scheduling conflict refers to teachers, classes, courses, classrooms and time to send five elements at a certain time to compete for a teaching resource and the occurrence of contradictions that make the teaching work can not be carried out normally.

The following are the main conflicts in the scheduling process:

Teacher conflicts, where the same teacher can only teach one lesson in a classroom at a given time.

Classroom conflicts, where the number of students in a classroom at the same time must be less than the capacity of the classroom. The same classroom can only have one lesson scheduled at the same time. The number of classes scheduled at one time must not exceed the number of classrooms available.

Class conflicts, where students in the same class can only take one bite of a course in a fixed classroom at the same time.

The set definitions, variable representations and symbols for the scheduling problem in this paper are defined as follows:

The collection of courses is C={1,2,...,c,...,c¯} . Course c includes the following attributes. n_ò indicates the number of teaching hours scheduled for course c. a_c denotes the number of people taking course c. TSC denotes a special course that needs to be arranged in a special classroom, and TSC ⊂ C. U_c denotes the scheduling priority of course c, and the smaller U_c is, the higher the priority. nminò and nmaxò indicate the minimum and maximum number of teaching hours per day for course c, respectively.

The set of classrooms is R={1,2,...,r,...,r¯} , b_r denotes the capacity of classroom r, and the special classroom is specified as TSR ⊂ R.

The set of teachers is T={1,2,...,t,...,t¯} , C_i denotes the courses offered by teacher t, then C_i ⊂ C.

The set of students is S={1,2,...,s,...,s¯} , C_s denotes the courses chosen by students s, and C_s ⊂ C, CG_s denotes the number of courses for students s.

The set of teaching time periods is D = P × W × J, where P is the number of weeks the class meets per semester, P={1,2,...,p,...,p¯},p¯≤20 .

W is the number of days per week that classes meet, W={1,2,...,w,...,w¯},w¯≤7 .

J is the number of days the class meets per day, J={1,2,...,j,...,j¯},j¯≤5 .

Time_i: denotes the available time slots for teacher t, Time_i = {(p, w, j)}, p ∈ P, w ∈ W, j ∈ J.

R_rpwj: If classroom r is available for classes on day w, time slot j of week p then R_rpwj = 1, otherwise s. J_s: The time between two adjacent courses of student s is evenly spaced and Js=⌈w¯×j¯CGs⌉−1 . where ⌈w¯×j¯CGs⌉ is the result of the ratio of the number of weekly class slots w¯×j¯ to the number of courses CG_s offered by student s is taken to the nearest whole number.

a_sj: If the interval between session j and session j + 1 of the s nd student satisfies the uniform interval J. then a_sj = 1, else a_sj = 0.

p_cpwj: Indicates the level of satisfaction of the teacher that the course c is scheduled for the w day j time slot in the p th week, if (p, w, j) ∈ Time_i, then p_cpnj = 1, otherwise p_cpnj = 0.

x_cpwj: is the decision variable, x_cpwj = 1 if course c is scheduled to be taught in classroom r on day w, time slot j of week p, and x_cpwj = 0 otherwise.

The university scheduling problem is to satisfy per-course credit hour requirements by assigning the set of courses proposed in the teaching programme C, to the set of classroom resources R. and distributing them across the set of available time slots D. Each course c in course set C is taken by a number of students (belonging to set S) and taught by one or more teachers (belonging to set T), while a student can take more than one course and a teacher can teach more than one course, and each classroom r in classroom resource set R has its available time slots belonging to set D.

The curriculum is based on the meaningful scheduling of the elements of scheduling that satisfy the necessary conditions. For the scheduling problem in practical applications, the following constraints will be established:

Constraint 1: Student s ∈ S can take only one course at a time: (1)∑còCs∑ròRxopwj≤1s∈S,p∈P,w∈W,j∈J

Constraint 2: Teacher t ∈ T can only teach one course at a time: (2)∑ceCi∑reRxcpwj≤1t∈T,p∈P,w∈W,j∈J

Constraint 3: Only one course can take place in Classroom t ∈ R at any one time: (3)∑ceCxcpnj≤1r∈R p∈ P v∈ W ξ̇

Constraint 4: For each course t ∈ C a predetermined number of teaching hours n_c needs to be achieved: (4)∑reR∑peP∑weW∑jeJxcprij=ncc∈C

Constraint 5: Classroom capacity b, which is greater than or equal to the number of students attending that classroom: (5)xcrprij*ac≤brc∈C, r∈ R p∈ P ∞W∈J

Constraint 6: The total number of classrooms r¯ is greater than or equal to the total number of courses scheduled at the same time: (6)∑c∈C∑r∈Rxcpwj≤r¯p∈P,w∈W,j∈J

Constraint 7: Range of hours per day for course c ∈ C [nminc,nmaxc] : (7)nminc≤∑reR∑jeJxcrpwj≤nmaxcc∈C,p∈P,w∈W

Constraint 8: Special courses need to be arranged in special types of classrooms: (8)∑ceTSC∑reR−TSR∑peP∑weW∑jeJxcrpwj=0

Constraint 9: If classroom τ is not available for scheduling at time (p, w, j), i.e., R_rpnj = 0. then no course c can be scheduled in classroom r at time (p, w, j), R_rpwj = 0 at time x_crpwj = 0.

The problem of class scheduling is a process of choosing the best among many feasible options, and it is often difficult to judge the merits of class scheduling options by a single indicator. In this paper, taking into account the students’ learning ability and the teaching and research time of the teachers with the influence of factors, the objective function is reduced to the following: (9)max α×∑ceC∑reR∑peP∑weW∑jeJpcpel∑reRxcpej∑ceCnc+β×∑s=13∑j=1qj−1asjgs/s¯+γ×NDIS

That is, the sum of the homogeneity of students’ weekly number of class sessions, the satisfaction of teachers’ time in class, and the inverse of students’ walking distance between classes is the maximum.

Among them: (10)∑eC∑peP∑veW∑j∈JPcpmj∑reRxcrpmj∑ceCnc

Indicates teachers’ overall satisfaction with scheduling. (11)∑s=1s∑j=1si−1asjgs/s

denotes the uniformity of class spacing for all students.

DIS is the distance between classes in the academic building and has DIS=∑s=1s∑w=1w∑j=1p−1Room[s][w][j] . Room[s][w][j] is the distance of the campus planning route in metres between the buildings in which the classrooms of 1 member (student or teacher) S are located for the ith and i + 1th classes on day W, and it is agreed that the distance between classes on different floors of the same academic building is 0. N is a given constant, which is set artificially in order to avoid the inverse term to be too small due to the large distances in the arithmetic process The distance between the different floors of the same building and the classroom is 0.

α, β, γ(α + β + γ = 1) denote the weights of teacher satisfaction, course spacing uniformity, and building spacing in the objective function, respectively. If α = 1, then the objective function only considers maximising teacher satisfaction. If β = 1, then the objective function only considers maximising the uniformity of students’ course spacing. If γ = 1, then the objective function only considers minimising building spacing.

The steps of the genetic algorithm to solve the class scheduling problem are broadly divided into the following processes:

STEP1: As the scheduling problem is to be computerised, thus the objective of scheduling needs to be quantified first.

STEP2: The corresponding pairs of the scheduling problem are converted into factors of the genetic algorithm, i.e., coding, according to the actual situation of the scheduling problem, genes can represent the set of variables, and the data structure of the scheduling problem can be represented by chromosomes.

STEP3: After the encoding of the scheduling problem, it is necessary to design the operators involved in several important operational steps of the genetic algorithm, mainly the replication, crossover and mutation operators.

STEP4: Calculation of fitness value of the results after processing each operation operator of the genetic algorithm in order to obtain the optimal objective.

Once the session optimality values are available, it is simply a matter of adding up the session optimality values of the scheduling programme and comparing them to the total number of classes. The following formula is used: a_i for each time slot, n for the total number of classes, and l for the whole school: (12)l=∑i=1nai

In the formula, the magnitude of the value of l measures the strength of the course scheduling programme, with a larger value indicating a better programme and vice versa.

The degree of uniformity of the distribution of class daily hours is to homogenise the distribution of class courses on a daily basis to avoid the scheduling of class courses being overly concentrated on a single day, using h_d to indicate the number of courses taken by a class C_i in d working days, n to indicate the total number of class days in a week, and h_i to indicate the average number of class sessions to be taken in a single day by a class, the degree of distribution of the average daily hours in a single class can be expressed as follows: (13)Ci=1∑d=1n(hd−h1)2

Where h1=∑d=1nhdn , the total number of classes in the whole school is m, the average daily distribution of class hours in the whole school C can be expressed as the above formula, the value of c reflects the degree of uniformity of the curriculum arrangement in the whole school, and the larger the value of c, the more uniform the curriculum arrangement is, so as to avoid the over-concentration of the curriculum in a certain working day, and effectively improve the utilisation rate of the teaching resources.

This indicator is used to measure the degree of goodness of fit of a class’s course combination scheme, which can rationally optimise the arrangement of those classes that have the same course twice or more in a week, so as to avoid the concentration of this course, which will result in inefficiency of the class. Using b_i for the degree of excellence of the daily mix, n for the total number of non-two-credit hour courses, and m for the total number of classes in the school, the degree of excellence of the class mix can be expressed by the following formula: (14)D = 1m∑c=1m( 1n∑i=1n bic )

The fitness function is a measure of the degree of individual performance in the genetic algorithm, the algorithm iterates according to the value of individual fitness in the population, thus it will directly affect the convergence speed of the algorithm and the acquisition of the global optimal solution. According to the above analysis, the fitness function can be selected as: (15)F =13(p1l+ p2C + p3D)e

In the formula, p₁, p₂, p₃ indicates the degree of importance of the corresponding scheduling objectives. The larger the value means that the objective is more important, and the selection of their size has a subjective factor. Scheduling staff depending on the degree of importance of the value. The value of e indicates the feasibility of the combination, i.e., whether there is any conflict in the current scheduling, and the value of 1 indicates that there is no conflict, while the value of 0 indicates that the current scheduling programme is not feasible and that there is a conflict in the course.

Value judgements are made on issues such as class scheduling, selection of teaching materials, and extracurricular sports club competitions in accordance with the standards of the physical education curriculum. The primary focus of the teaching process is to evaluate the implementation of basic teaching documents, semester teaching schedules, and lesson time teaching plans by the physical education teaching and research group and teachers. Teachers’ teaching quality evaluation refers to whether the lesson plan is written in detail, whether the preparation before class is sufficient, whether the key points and difficulties are broken through in the class, whether the teaching method is appropriate, whether the teaching is targeted, whether the teaching attitude is good, whether the teaching is serious and responsible, and whether the importance is attached to the teaching information feedback and so on. Whether the exercise load in the class is moderate, and whether the time arrangement for the preparation, basic, and end parts is reasonable. Students are active and interested in the class, and the national students’ physical fitness test has a high rate of attainment and excellence. The students’ learning process adopts a combination of self-assessment, mutual assessment and teacher’s assessment and combines qualitative and quantitative in the way of evaluation, focusing on the evaluation of students’ progress and improvement of their abilities.

The purpose of the population size test is to observe the extent to which size affects the efficiency of programme execution. Three sets of data were used in the test to compare the final efficiency, and the population sizes tested were 30, 60, and 90.

The effect of population size on computing speed (unit: s) is shown in Figure 2, which records five sets of results from the test data, each set of data from a random sampling. It can be seen that with the same population size, the time for each run is basically the same, with little difference. As the population size becomes larger, the run time gradually increases. The average time for the three sets of data is 30 (1027s) for test group A/population size, 60 (2221s) for test group B/population size, and 90 (4758s) for test group C/population size, in that order.

Figure 2.

The impact of population size on operation speed (unit: second)

The test results show that the time required for the genetic algorithm to obtain the optimal solution increases as the population size becomes larger. Of course, the increase in population size causes an increase in the solution time. However, if you need high-quality class schedules, you may want to increase the population size appropriately to obtain a certain quality of class schedules, at the expense of speed. Through continuous adjustment of data and multiple tests, it has been determined that the optimal population size for efficiency is between 40 and 80.

The adaptive parameters K1, K2, K3, and K4 are adjusted to different values to test the near optimal solution and the number of iterations for the corresponding fitness parameter. The partial results of the fitness values of the near-optimal solution and the number of iterations to reach the value for different adaptive parameters are shown in Table 1. The average value of the fitness value of the near-optimal solution for the adaptive parameters K1, K2, K3 and K4 is 1165.2. The crossover, mutation probability and population evolution curve performed by the genetic algorithm used in the scheduling system show a monotonically increasing trend, and the test reveals that the difference in the values of the adaptive parameters affects the fitness to a certain extent but the overall trend of the population does not change significantly.

The computer scheduling will be affected by the size of the population, which has a greater degree of influence on the efficiency of the execution of the program. Here, the test set the population size of 30, 60, 90, 120, 150, and 180. The effect of the number of courses on the efficiency of the algorithm results is shown in Table 2. When the number of courses is in the interval of 30 to 90, the scheduling time can be controlled within 4 minutes. It is obvious that as the number of courses involved in scheduling rises, the time for the completion of scheduling shows a certain regular increase, so a higher number of university-wide scheduling courses will result in a slightly longer time for the formation of the class schedule. In order to quickly form the schedule, a chunking model can be implemented, where the overall course is divided into modules and then scheduled.

There is an original intelligent scheduling system in this sports college. This section is implementing a designed system to compare the operational results of the original system. The scheduling test will be carried out on the courses taken by the students of the School of Mechanics in the last semester of 2023, with a total of 13 involved classes, 30 courses, occupying 32 classrooms. The results of optimizing the intelligent scheduling system are shown in Table 3.

Analysing the data for this set of tests, it can be concluded:

The intelligent scheduling system designed this time considers the constraint rules more thoroughly, resulting in fewer conflicts in the course schedule. There is a greater degree of uniform distribution of courses. The running time of the automatic scheduling system is shorter. The intelligent scheduling system that was designed is more consistent with common sense.

To find out the satisfaction of the students of the experimental class with their teachers, the quality of the school physical education teachers investigated in this study includes the academic level of the teachers, teaching methods, teaching organisation, teaching attitude and teacher-student relationship. The results of the survey of the experimental class on the satisfaction of school physical education teachers are shown in Table 7, and the overall satisfaction is greater than 4.0. From the results of the survey, it can be seen that the student-teacher relationship with the teacher is still very cordial, and the satisfaction is 4.26. Secondly, the students also affirm the teacher’s attitude in the classroom and the organization of the classroom process.

Every student attaches the greatest importance to good or bad results, so the reform of physical education teaching needs to focus on the assessment system to keep pace with the times, not only to reflect intelligence but also to ensure fairness and justice. Assessment and evaluation are also the most important concerns of students. A reasonable, fair, and impartial teaching evaluation system not only can provide a fair evaluation of each student’s performance but also can act as an incentive for students to study hard.

Satisfaction ratings of the experimental class on the assessment of physical education teaching are shown in Table 8.

As can be seen from the table, the student’s overall response to the performance evaluation structure of school sports and the fairness of teachers’ grading is good. The reason for this is that the school has recently carried out different degrees of reform in the assessment and evaluation system, focusing on the attitude of students towards participating in physical exercise. Among them, students’ overall satisfaction with the format of the examination is 3.75, and the grading standard of 3.85 is low, but the satisfaction is greater than 3.5 points.