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Analysis of the Teaching Quality of Physical Education Class by Using the Method of Gradient Difference

Publié en ligne: 15 Jul 2022
Volume & Edition: AHEAD OF PRINT
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Reçu: 12 Apr 2022
Accepté: 15 Jun 2022
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Magazine
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2444-8656
Première parution
01 Jan 2016
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2 fois par an
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Anglais
Introduction

The assessment of the quality of education of teachers is an important part and content of managing the quality of education of higher education institutions. Scientific and rational evaluation methods are an important measure to guarantee the quality of education, correctly evaluating a teacher's teaching effect can guide teachers to improve their teaching level, improving teaching methods has a positive effect, and scientifically and effectively evaluating teaching quality is of great significance to school teaching [1]. Over the last few years, universities have placed great importance on assessing the quality of teachers' education, establishing appropriate rule systems and assessment methods, and constantly reforming and improving them in practice. At present, the most widely used teaching quality evaluation methods mainly include opinion polling method, peer evaluation method and so on. These methods provide a reference for evaluating teaching quality, but there are also some shortcomings, mainly in that the qualitative indicators of teaching quality evaluation need to be determined subjectively by the evaluators, and there are uncertainties and ambiguity. This has led to the current evaluation of teaching quality, and has not yet found a more objective and appropriate method [2]. The graded difference value method is a tool with strong self-learning ability, robustness and fault tolerance, good at association, generalization, analogy and reasoning, the treatment of the uncertainty of teaching quality evaluation has a very good effect. Physical education is a public basic course in the university, which undertakes the important task of exercising and improving students' physical quality. Improve physical education, it is the most sought goal of colleges and universities to make physical education classes play their due role better. Therefore, evaluate PE lessons, look for factors that affect the quality of PE lessons, improve PE lessons, and improve the level of PE lessons [3]. In response to this, we will use the academic ability difference evaluation method to dig up related information that affects the quality of physical education classes, and provide scientific and guiding advice for future physical education class reforms. I made it. Patjas M et al. stated that in recent years, higher education has developed rapidly, and the number of students in many colleges and universities has expanded rapidly, but the number and quality of teachers have not kept pace with [4]. Mcneill K L et al. stated that someone at a conference presented: One for attention, two for improvement. The so-called “one emphasis” means that in the very long term that follows, high schools place great importance on the important role that theological evaluation plays in higher education, thereby fully fulfilling the role of theological evaluation. Two improvements must be achieved. “Two improvements” are the improvement of the quality of education and the improvement of the quality of education, both of which are dependent on each other and promote each other. Teaching evaluation is the key to promote the better improvement of the two [5]. Maulana R and others believe that the quality of teaching is the lifeline of the school's sustainable development, and it is also one of the important indicators for the evaluation of the work effectiveness of the majority of front-line teachers [6]. Llinares S et al. In a long-standing assessment of the quality of physical education classes, focus on whether the practice density and exercise load of the whole class meet the standards stipulated in the sports theory textbooks, and it is difficult to evaluate other aspects of the content because there are no standards [7]. In recent years, a lot of evaluation programs have been launched, but most of the evaluation process is complicated, data processing is difficult, and it is often impossible to evaluate a class in time. In the past two years, we have used the principles of statistics and fuzzy mathematics on the basis of previous research, and used the graded difference method to determine the quality of the class, trying to provide a more objective basis for scientifically formulating the quality evaluation standard of physical education.

Methods
Analysis of the teaching quality of physical education
Contents of teaching quality analysis

The quality of physical education classes must be evaluated in three major ways: class attitude, class level, and class effect.

The specific contents are as follows:

Teaching attitude. Including the quality of writing lesson plans; preparation before class (site, equipment, safety measures).

Teaching level. Including teachers' organizational management ability; The application of teachers' teaching methods and teaching skills; The leading role of teachers, the implementation of teaching principles, whether students can play the main role and fully mobilize students' enthusiasm for learning; Whether teachers pay attention to strengthening students' ideological and moral education in the classroom.

Teaching effect. Including the application and adjustment of reasonable and appropriate exercise load, and the evaluation of exercise load test results; Analysis of the completion of the “three basics” teaching tasks (objectives); the effect of ideological and moral education, the implementation of classroom routines, and the organizational discipline of students. As well as the brave and tenacious learning spirit, the collective spirit of solidarity and mutual assistance, the good teacher-student relationship and the initiative and enthusiasm for maintaining a high enthusiasm for learning and exercising [8].

The reference standard for the quality analysis of physical education teaching
Reference standards for theoretical courses

The teaching tasks should be clear, according to the syllabus, the teaching plan for the whole semester, and the teaching requirements at different stages, starting from the reality of the textbook and the reality of the students in the class, the specific requirements of each class are planned and scientifically arranged.

Classroom teaching is well organized and does not waste time and student energy.

The teaching method should be in line with the basics of the student's knowledge and should be able to progress little by little to elicit and think about the student's learning positivity. The classroom atmosphere should be serious, active and lively.

Emphasize emphasis and break through difficulties.

The teaching effect is good, and it is basically done in the classroom and digested in the classroom.

Reference standards for practical lessons

Can determine the teaching task more appropriately, and complete the teaching task well.

Can make students acquire certain basic knowledge, basic skills, and basic skills; to develop students' physical fitness in an all-round way and enhance their physical fitness.

The course structure is rigorous, the measures taken are reasonable, and the time is accurately grasped.

The amount of exercise and the density of the course are appropriate. The general reference standard is that the average heart rate of students is between 130 and 170 beats per minute.

Teachers are proficient in teaching materials, explain concisely, demonstrate accurate, scientific and effective teaching methods, and combine comprehensive care with key guidance.

Moral education can be carried out in combination with the characteristics of the textbook itself, and the effect is obvious.

Teachers can play a leading role. The teacher's appearance and teaching attitude give students a good influence and education. Temporary accidents should be handled promptly, decisively and correctly [9].

The establishment of the quality analysis framework of PE classroom teaching
Determine the network architecture

Figure 1 is a diagram showing a network structure of a model, which is composed of an input layer, a concealment layer, and an output layer, the 3-layer structure can solve most nonlinear problems as long as the appropriate number of neurons is taken, and the data curve can be simulated. The input layer uses 5 neurons, corresponding to the 5 dimensions of teaching quality evaluation; The output layer uses 1 neuron, corresponding to the evaluation results; Empirically, the number of neurons in the hidden layer is half the number of neurons in the losing layer and the output layer, that is, three [10]. Once the training sample is determined, the number of neurons in the input and output layers is determined, but the number of secret layers and neurons is an important and difficult problem: There are too few points to have the learning and information processing capabilities required for the network; If the score is too high, the complexity of the network structure increases, and it is easy to fall into a local local minimum value during learning, and the learning speed becomes slow. In order to meet the accuracy, it is a principle to adopt the structure as compact as possible. You can also try the following methods for primary selection:

Method 1: The relationship between the number of hidden neurons s and the number of patterns N: s = log 2 N;

Method 2: Kolmogorov's theorem is that the knot of the hidden layer s = 2m + 1 (m is the knot of the input layer);

Method 3: s=sqrt (0.39mn + 0.14nn + 2.45m +0.78n +0.34)+0.52 (m is the number of input layers and n is the number of output layers).

Figure 1

Differential network structure

Symbol regulations

A large number of symbols are involved in the model, and the following provisions are made: training sample set Xp; training sample Xi = [X1, X2, X3, X4, X5] (1×5 matrix); Hidden layer weight W(2)ji (5×3 matrix), The weight of the connection from neuron Xi in the input layer to neuron Hj in the hidden layer; Hidden layer input Hinj = X * W(2)ji; hidden layer output Houtj = sigmoid (Hinj); Hidden layer weight W(3)ji (3×1 matrix), that is, the connection weight of hidden layer neuron Hj to output layer neuron Y; Output layer input Yin = Houtj * W(3)ji output layer output Yout sigmoid(Ym); expected value Tp.

Activation function

In the level difference value method, the input value of each neuron is the accumulation of the output value of all neurons in the previous layer multiplied by the weight, and the activation function processes the input value to generate the output of the neuron [11]. The step function is an ideal activation function, but it is discontinuous, non-derivative, and not smooth, therefore, the sigmoid function shown in Figure 2 is often used as the activation function in the level difference method. Whether the input value tends to + ∞ or − ∞, the output has a certain convergence limit to keep the model stable; At the same time, the derivative of the sigmoid function is simple and easy to calculate, as shown in equations 1 and 2. f(x)=11+expx f\left( x \right) = {1 \over {1 + {{\exp }^{ - x}}}} df(x)dx=expx(1+expx)2=11+expx(111+expx)=f(x)(1f(x)) \matrix{ {{{df\left( x \right)} \over {dx}}} \hfill & = \hfill & {{{{{\exp }^{ - x}}} \over {{{\left( {1 + {{\exp }^{ - x}}} \right)}^2}}} = {1 \over {1 + {{\exp }^{ - x}}}}\left( {1 - {1 \over {1 + {{\exp }^{ - x}}}}} \right)} \hfill \cr {} \hfill & = \hfill & {f\left( x \right)\left( {1 - f\left( x \right)} \right)} \hfill \cr }

Figure 2

Sigrmoid function

It can be seen from the sigmoid function diagram that the input is outside the [−5,5] interval, and the change in the function value is small, brings the gradient closer to zero This causes the gradient disappearance phenomenon in the step value calculation method. The five dimensions of evaluation are percentile, so the input X data is mapped to the range of [0,1] by formula (3). Use the “read delimited spreadsheet control” in labview to read the training sample set from the Excel table [12]. X=XiXminXmaxXmin X = {{{X_i} - {X_{\min }}} \over {{X_{\max }} - {X_{\min }}}}

Extract a sample matrix [X1, X2, X3, X4, X5, X6] from the normalized sample set and multiply it with the weight matrix W(2)ji, the Hinj input matrix of the three neurons in the hidden layer is obtained, and the Houtj output matrix of the three neurons in the hidden layer is calculated through the activation function sigmoid. In the same way, the output value Yout of the output layer neuron Y can be obtained. Select 30 samples for training, the program adopts the While loop structure, the loop body calculates the output value of 1 sample every time it is executed, uses the shift register to execute 30 times and ends the loop, and obtains the output value array of 30 samples [13].

Results and Analysis
Network Design
Determining network floor

The recognition model is shown in Figure 3.

Figure 3

Model diagram of teaching quality evaluation based on graded difference method

Theoretically proven: A network with bias and at least one S-type implicit layer and one linear output layer can be approximated to any rational number. Increasing the number of layers can further reduce the error and improve the accuracy, but at the same time complicate the network and increase the training time of the weight value of the network. Since the improvement in error accuracy can actually be obtained by increasing the number of neurons, it is easier to observe and adjust than increasing the number of layers. Therefore, increasing the number of neurons in the implicit layer is a priority. In this experiment, the number of implicit layers is set to 1, and one input layer and one output layer are added to form a three-layer hierarchical network[14].

Determining the number of implicit layer neurons

The accuracy of network training can be improved by adopting one implicit layer to increase the number of neurons. So how many implicit layer nodes should we choose? This is not clearly defined in theory. When designing concretely, it is realistic to compare and train the number of each neuron and add a little margin. In this experiment, we compared several cases and set the number of neurons in the implicit layer to 6.

Determination of initial weights

Since the system is non-linear, the initial values are largely related to whether learning is locally minimized, whether it can converge, and how long the training time is. Empirically, it generally takes a random number with an initial weight between (−1,1).

Determination of learning step size

The learning step determines the amount of change in weight that occurs in each cycle training. Large learning steps make the system unstable; Small learning steps result in long training times and may be slow to converge, but ensure that the error values in the network do not extend beyond the valleys of the error surface and eventually approach the minimum error values. Therefore, in general, smaller learning rates tend to be chosen to ensure the stability of the system. The learning rate is selected in the range of 0.01–0.8. According to the above, the author chooses a 3-layer differential network. The number of neurons in the input layer is determined by the number of variables in the training sample, the number of neurons in the implicit layer is 6, and the number of neurons in the output layer is 1. The transformation function of the node selects the sigmoid function. The initial weight is between (−1, 1), which is randomly generated [15]. The learning step size is determined to be 0.1, Figure 4 is the learning result, and Figure 5 is the error curve.

Figure 4

Learning Results

Figure 5

Error Curve

Determining the correspondence between the network output value and the evaluation level

The author divides the teaching quality evaluation results into five grades, namely: Excellent, good, moderate, pass, and fail. The corresponding relationship between the network output value and the evaluation level is shown in Table 1.

Correspondence table of network output value and evaluation level

grade network output value s
excellent 0.9 ≤ S ≤ 1.0
good 0.8 ≤ S < 0.9
medium 0.7 ≤ S < 0.8
Pass 0.6 ≤ S < 0.7
failed S < 0.6
Experimental results

According to the conclusion of the analysis in Section 3.1, the experiment was carried out according to the evaluation model flow in Figure 3. The experiment selects 100 samples, each sample contains 10 evaluation index values and the final evaluation score. We take 50 samples as training samples and the remaining 50 samples as test samples. The target error is 0.02. After 21 times of training, the target error of the differential network meets the requirements. The trained graded network is tested against the original training samples, and the match rate between the network output grade results and the actual evaluation results reaches 100%; The network is tested on the test samples, and the coincidence rate between the network output grade results and the actual evaluation results reaches 96%. It shows that the model in this paper can more accurately evaluate the teaching quality according to each evaluation index.

Conclusion

The developed physical education class quality evaluation score table overcomes the shortcomings of traditional subjective evaluation, and the evaluation effect is more realistic; And this table is simple to use, easy to master, and can evaluate the difference in time after a physical education class is over. Therefore, through the appraisal of experts and administrators, this table has a wide range of feasibility and is suitable for the evaluation of the quality of physical education classes in various schools. The target error was obtained through 21 experiments, and the trained graded network was tested against the original training sample, and the match rate between the network output grade results and the actual evaluation results reached 100%; The network is tested on the test samples, and the coincidence rate between the network output grade results and the actual evaluation results reaches 96%. In order to further improve school sports management, promote the reform of sports education, and improve the quality of teaching, relevant educational administration and scientific research departments should strengthen further discussion and research in this area, and strive to establish a unified evaluation project, standard and method.

Figure 1

Differential network structure
Differential network structure

Figure 2

Sigrmoid function
Sigrmoid function

Figure 3

Model diagram of teaching quality evaluation based on graded difference method
Model diagram of teaching quality evaluation based on graded difference method

Figure 4

Learning Results
Learning Results

Figure 5

Error Curve
Error Curve

Correspondence table of network output value and evaluation level

grade network output value s
excellent 0.9 ≤ S ≤ 1.0
good 0.8 ≤ S < 0.9
medium 0.7 ≤ S < 0.8
Pass 0.6 ≤ S < 0.7
failed S < 0.6

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