Research on Interdisciplinary Integration Path of Core Literacy in Physical Education and Health Curriculum under the Perspective of OBE

In the 21st century, the competition of education in the international arena has evolved into the competition of talents, and the strategy of strengthening the country with talents is the common tacit strategy of all the countries in the world, and the cultivation of talents, as an international consensus, is the key guide for the change of education in today’s world, which is to adapt to the needs of the country and society.

Cultivating talents to meet the needs of the country and society is the key guide to educational change in today’s world [1-3]. Under the historical flood of great changes in education at home and abroad, in order to further implement the macro talent cultivation system, the development of students to adapt to the society’s necessary abilities and good quality. It is necessary to do in-depth exploration and research for the core literacy of students.

OBE results-oriented concept, education results-oriented, talent training results of a more advanced education concept [4-6]. It has been widely used in many educational scientific research, combining the OBE education concept with the development of students’ core literacy, and redefining the structure of students’ talent cultivation and the evaluation index system of core literacy through the reverse thinking of students’ learning outcomes. Students improve their social adaptability along with their ability development, and finally become practical talents to meet future work needs and social development. The introduction of the OBE education concept in the study of the core literacy evaluation index system of physical education students is conducive to the innovation of China’s physical education profession [7-8]. From the perspective of the long-term development of national sports teaching in the future, the research on the evaluation index system of core literacy of physical education majors is a natural requirement for the implementation of national policy guidelines, and it is an important way to implement the fundamental task of Lidu Shuren education.

As an important specialty in the development of physical education, the physical education major is the backbone of the development of school physical education disciplines, and it is also the inheritor of sports culture. Under the concept of OBE, the construction of the evaluation index system of core literacy of physical education majors is necessary for the construction of physical education disciplines and the development of physical education majors, and it is also an important measure to enhance the international competitiveness and international status of China’s physical education majors. Starting from the OBE concept to assess the core literacy of physical education majors, comprehensively summarize and study the social adaptability required for the future employment of physical education students, aiming to improve the core literacy competitiveness development level of students [9-10]. This can not only enhance the students’ necessary ability to integrate into society and adapt to social development, but also is an inevitable need to improve the development ability of physical education majors.

Literature [11] applied intergroup contact theory to a sport for development program focusing on acculturation. Five components of intergroup contact theory were built into the program design and then evaluated through qualitative analysis to test the applicability of the theory in sport for development research. Literature [12] explored the effects of augmented training on vertical jump performance in amateur, collegiate, and elite female athletes through a systematic review and meta-analysis. It was concluded that augmented training is an effective form of training to improve the performance of female athletes. The benefits of augmented training on performance are greater for longer duration interventions. Literature [13] used a whole group randomized controlled trial to investigate the effects of a school physical education intervention on students’ MVPA. The primary outcome of the trial was students’ MVPA in physical education classes. Secondary outcomes included students’ leisure time MVPA, teachers’ perceived need for support, need for satisfaction, motivation for physical education autonomy, willingness to participate in physical activity, mental health, and health-related fitness (cardiorespiratory and muscular fitness). Literature [14] provides a review of literature related to caring in physical education and physical activity environments to identify the current status, gaps, and future trends in research on the ethics of caring in physical education and physical activity environments. Physical education teachers value the importance of caring. Perceived caring climate or perceived caring behaviors in physical education or physical activity settings are positively correlated with many motivational, cognitive, and affective variables. Literature [15] In this paper, through the investigation and analysis of Chinese adolescent physical health data, we propose that the results of health intervention training as part of the empirical study of constructing a big data management service system for adolescent health can effectively improve the hypothesis of the relationship between adolescent physical health. Literature [16] used a mixed method of survey distribution to conduct a cross-sectional study. Online educational environments had statistically significant benefits for students in terms of instructor feedback and responsiveness, understanding and interest in course content, and perceived positive health changes during the course.

In this paper, we firstly design an OBE interdisciplinary teaching model for physical education and health from the perspective of top-level design, theme selection and coordination.Secondly, it points out the limitations and problems of this teaching model, in view of which such problems can be solved and optimized by constructing a recommendation model for interdisciplinary teaching of physical education and health courses. Then the data sources are identified, the data are preprocessed based on the statistical data segmentation algorithm, then the TF-IDF algorithm is used to obtain the textual features of the interdisciplinary teaching of physical education and health courses, and the similarity value of the interdisciplinary teaching of physical education and health courses is solved through the data vectorization conversion and similarity computation formula, and the construction of the recommendation model of interdisciplinary teaching of physical education and health courses is finally completed. Finally, the interdisciplinary teaching of physical education and health courses in colleges and universities is analyzed using the controlled experiment before and after side method and the simulation experiment comparison method, respectively.

2

Interdisciplinary Research in Physical Education and Health Programs Incorporating OBE

2.1

Infiltrate the OBE Physical Education and Health Interdisciplinary Teaching Model

2.1.1

Top-level design

Top-level design involves systematically sorting out the interdisciplinary teaching system. The basic elements related to interdisciplinary teaching, such as teaching objectives, teaching content, teaching methods, teaching evaluation, and academic quality description, need to be brainstormed by experts and scholars to form an authoritative and guiding national curriculum and unified teaching materials, guide front-line physical education teachers to carry out interdisciplinary teaching design, curriculum integration, and educational evaluation, and solve key problems such as "what to teach", "how to teach" and "how to evaluate" in the process of interdisciplinary teaching. Only through top-level design, systematic combing, and further clarifying the teaching elements such as methods, methods, and objectives of interdisciplinary physical education can interdisciplinary teaching activities be implemented more effectively, scientifically and healthily.

2.1.2

Selected themes

Selected themes to create interdisciplinary teaching and learning in multiple contexts. First, the creation of adapted to the students to learn the selected parts, are in line with the cognitive level of the students, such as can not appear in the level of a student to learn the parabola such a hypercognitive situation. Again, in the primary stage of track and field teaching, the introduction of tangent mathematical concepts to explain the way the arc starts is obviously inappropriate.Secondly, create situations that are adaptable to the facilities on the school grounds. According to the actual conditions of school sports teaching, create regional, localized and school-based interdisciplinary teaching contexts according to local conditions, or share the corresponding contextual resources with neighboring schools, communities or social organizations. For example, if the school is in the old red revolutionary area, it can combine the interdisciplinary teaching context of physical education with red stories, hero stories and revolutionary stories, which can enhance students’ knowledge and ability in physical education and strengthen their sense of national pride, social responsibility and national identity at the same time. Third, create diversified problem situations. The purpose of interdisciplinary teaching is to stimulate students to synthesize the use of knowledge of various disciplines in order to solve practical problems in complex situations, so the design of the teaching situation should be closely linked with life, and life problems are closely linked.

2.1.3

Integration and coordination

Coordinate and establish a community that promotes interdisciplinary teaching and learning. First, build a community among physical education teachers. Invite administrators of educational administration, administrators of physical education teaching in schools, physical education teachers and researchers, physical education teachers, etc., to teach and research together, explore together, and give feedback together, so as to really solve the pain point problems in interdisciplinary teaching practice. Secondly, establish a community among interdisciplinary disciplines. Physical education teachers should take the initiative to break the disciplinary barriers, take the initiative to communicate with teachers of other disciplines, such as when encountering technical actions such as volleyball serve, dunking, passing, etc., they can take the initiative to discuss with the physics teacher about how to introduce physical concepts such as the concept of the parabola, force analysis, and the direction of the force into the practice of teaching, so that the students can better generate knowledge links and more naturally understand the disciplinary knowledge and technology. Third, build a community across academic disciplines. Interdisciplinary teaching should also pay attention to the articulation of school segments, such as junior high school physical education interdisciplinary teaching content should be connected with the school segments, senior high school physical education interdisciplinary teaching content is also connected with the junior high school segments, to realize the development of large, medium and small integrated teaching, to ensure that the interdisciplinary teaching content design adapted to the law of cognitive development of the students.

2.2

Recommended Model for Interdisciplinary Teaching in Physical Education and Health Courses

2.2.1

Data sources

The source of the original data is the OBE-integrated Physical Education and Health Curriculum Interdisciplinary Research Outcomes dataset within the university, which consists of 5,700 records, each of which corresponds to a research finding, as well as its associated author information, published journal information, etc. Each record has 80 attribute values, most of which are blank values. The items with relatively complete and meaningful data include outcome number, discipline content, integration pathway, and personalized recommendation.

2.2.2

Statistical-based algorithms for data segmentation

Statistical based participle algorithm has a corpus of successfully trained participles, through which the probability of different ways of word formation is calculated and finally the way with the highest possible probability of word formation is chosen. For a sentence S, it has m ways of word formation as follows: (1) $\begin{matrix} A_{11} A_{12} \dots \dots A_{1 n_{1}} \\ A_{21} A_{22} \dots \dots A_{2 n_{2}} \\ \dots \dots \dots \dots \dots \dots \\ A_{m 1} A_{m 2} \dots \dots A_{m n_{m}} \end{matrix}$

Where, A_{mn_m} represents the n_mrd word in the mnd participle way, and its optimal participle way is the participle way with the highest probability of statistical distribution among the m participle ways, and if the participle way of the rth one is optimal, then: (2) $r = \underset{i}{\underset{︸}{arg max}} P (A_{i 1}, A_{i 2}, \dots, A_{i n_{t}})$

Where P(A_i1,A_i2,…,A_in_t) denotes the probability distribution of the ind partitioning method, i.e: (3) $P (A_{i 1}, A_{i 2}, \dots, A_{i n_{t}}) = P (A_{i 1}) P (A_{i 2} | A_{i 1}) P (A_{i 3} | A_{i 2}) \dots P (A_{i n_{t}} | A_{i n_{t - 1}})$

JIEBA text participle method combines the fast speed of dictionary participle method and the elimination of ambiguity of statistical participle method.JIEBA text participle includes three types of participle modes, which can be used for different participle method tasks to select the more suitable participle mode. Dynamic planning to find the path shown in Figure 1, JIEBA text participle is mainly based on the dynamic planning method, according to the word frequency of words, to find out the participle method that has the highest probability of word formation in various word formation methods.

2.2.3

Feature extraction based on TF-IDF algorithm

Feature extraction of interdisciplinary texts of physical education and health courses integrating OBE concepts refers to extracting feature word information from them that can represent interdisciplinary texts of physical education and health courses integrating OBE concepts. The feature words can represent the text, which is used to calculate the similarity between the texts and reduce the amount of calculations. The main purpose of feature extraction of interdisciplinary text of physical education and health curriculum integrating OBE concepts is to minimize the number of words for computational processing without losing the information of the original text of interdisciplinary physical education and health curriculum integrating OBE concepts, to reduce the storage space of the text as well as the words of interdisciplinary text of interdisciplinary physical education and health curriculum integrating OBE concepts, and to improve the interdisciplinary physical education and health curriculum integrating OBE concepts efficiency of text processing.

The TF-IDF text feature extraction algorithm is based on counting the word frequencies of the text. After feature extraction, the feature words representing the text are characterized by higher word frequencies in this text, but lower word frequencies in other texts. Therefore, combining the concepts of word frequency as well as inverse document frequency, the formula of TF-IDF is shown below: (4) $T F_I D F_{W_{h}} = T F_{W_{h}} * I D F_{W_{h}}$

Where TF_{W_k} denotes the word frequency of word W_h and IDFW_k denotes the inverse document frequency of word W_h. As shown in Eqs. (5) and (6), the word frequency and inverse document frequency can be expressed as: (5) $T F_{W_{h}} = \frac{c o u n t (W_{h})}{| D_{i} |}$ (6) $I D F_{W_{h}} = \log \frac{N}{\sum_{i = 1}^{N} I (W_{h}, D_{i})}$

where count(W_h) indicates the number of times word W_h appears in the set of words after data preprocessing, D_i indicates the number of all words in the set of summary words, and N is the total number of words in the corpus. I(W_h,D_i) indicates whether the corpus contains word W_h, if it contains word W_h, the value is 1, if it does not contain word W_h, the value is 0. Assuming that word W_h has never appeared in the corpus, $\sum_{i = 1}^{N} I (W_{h}, D) = 0$ , therefore, it is necessary to do smoothing on the inverse document frequency IDFW_k of word W_h, and the processing process is shown in Eq. (7).

(7)

I D {F^{'}}_{W_{h}} = \log \frac{N}{1 + \sum_{i = 1}^{N} I (W_{h}, D_{i})}

2.2.4

Data conversion to quantification

Machine learning algorithms can not directly deal with text data, this paper indicates that the model is the conversion of text data into numerical values, a probabilistic model by calculating the probability of the textual utterance, a textual representation based on the modeling of the contextual relationship of the words, which can be expressed according to the Bayesian formula: (8) $p (W) = p (w_{1}^{T}) = p (w) \cdot p (w_{2} | w_{1}) \cdot p (w_{3} | w_{1}^{2}) \dots p (w_{T} | w_{1}^{T - 1})$

Where S_q denotes the qnd utterance in the text and W_{q,m_k} denotes the m_nth word of the qth text utterance. Currently, the common word vectorization models are Word2Vec model, N-gram model.

1)

Word2Vec model

The structure of neural network training word vectors in Word2Vec model is shown in Fig. 2, Word2Vec, is a way of constructing word vectors using neural networks.The Word2Vec model uses a three-layer neural network architecture, and through the textual training of the neural network, all the words are mapped to the corresponding K-dimensional distributed vectors, i.e., the textual words are transformed into an arithmetic form.

The structure diagrams of CBOW model and Skip-gram model are shown in Fig. 3, respectively.Word2Vec’s method of constructing word vectors contains two models, CBOW model and Skip-gram model.CBOW model estimates the chances of intermediate words by using the before and after words, while Skip-gram model is exactly opposite to the CBOW model, i.e. Skip-gram uses intermediate words to estimate the chances of their before and after words.

The basic parameters in the Word2Vec model are shown in Table 1, for the parameters in the Word2Vec model, changing the parameters can be a certain basis for subsequent algorithmic experiments.

Table 1.

Word2vec model parameters

Parameter name	Parameter content
Sg	Set training model
Size	Training feature vector dimension
Window	Window size
Workers	Parallel quantity of training

2)

N-gram model

The basic idea of the N-gram model is to estimate the Nth word of the sentence based on the first N-1 words, and calculate the co-occurrence probability of the N-element words to predict the probability of occurrence of the center word. It can be expressed as: (9) $p (w_{h} | w_{1}^{h - 1}) \approx p (w_{h} | w_{h - n + 1}^{h - 1}) = \frac{p (w_{h - n + 1}^{h})}{p (w_{h - n + 1}^{h - 1})}$

When the corpus is large enough, Eq. (10) can be simplified to: (10) $p (w_{h} | w_{1}^{h - 1}) \approx \frac{c o u n t (w_{h - n + 1}^{h})}{c o u n t (w_{h - n + 1}^{h - 1})}$

Where $w_{h - n + 1}^{h}$ is the set of components of the h–n+1 nd to hrd words of the sentence, and $c o u n t (w_{h - n + 1}^{h})$ and $c o u n t (w_{h - n + 1}^{h - 1})$ are the number of occurrences of set $w_{h - n + 1}^{h}$ and set $w_{h - n + 1}^{h - 1}$ , respectively. Usually N-gram models are binary and ternary models, i.e., N=2 and N=3.

2.2.5

Vector similarity

In the vector space model, the topic is represented by a matrix consisting of a column of words and their weights, and the more closely related to the topic, the more frequently the words appear, and the more weight they take up in the whole matrix. Topic similarity calculation is actually the magnitude of the differences between different topic vector space models. The commonly used ones are cosine similarity, JS similarity, and KL similarity.

Since this paper needs to screen the associated themes in multiple time windows, a uniform similarity threshold needs to be set in the theme screening mechanism, but KL similarity and JS similarity are difficult to have a uniform standard in terms of threshold, and do not have good generalizability to experimental data. The result of cosine similarity calculation is between 0 and 1, the larger the value indicates that the angle between the vectors is smaller, i.e., the higher the similarity between the topics, so this paper adopts cosine similarity to calculate the correlation between the topics. Cosine similarity is used to assess the similarity between topics by calculating the cosine value of the angle between two topic vectors, based on the weight of the topic vectors in each lexical dimension, the difference of the angle between the spatial vectors representing the two topics is calculated.

The similarity calculation between vector space models can be derived using the Euclidean dot product formula as shown in Equation (11): (11) $T_{i} T_{j} = ‖ T_{i} ‖ ‖ T_{j} ‖ \cos θ$

In Eq. (11), T_i and T_j denote the ird and jth subject word vector space models, respectively, so the formula for cosine similarity is shown in Eq. (12): (12) $s i m i l a r i t y = \cos (θ) = \frac{T_{i} □ T_{j}}{‖ T_{i} ‖ ‖ I T_{j} ‖} = \frac{\sum_{k = 1}^{n} T_{k i} \times T_{k j}}{\sqrt{\sum_{k = 1}^{n} T_{k i}^{2} \times \sum_{k = 1}^{n} T_{k j}^{2}}}$

In Equation (12), T_ki denotes the weight occupied by the vocabulary k words in the i theme, i.e., the generation probability of vocabulary k, and T_kj denotes the weight occupied by the kth vocabulary word in the theme j. From the formula, it can be seen that the similarity between topics is proportional to the value of cosine similarity. The similarity threshold method can study the degree of association of multiple subjects on a time series and is suitable for the application of subject evolution analysis. The similarity of different subjects is calculated and the subject similarity matrix is output.

3

Analysis of Interdisciplinary Case Teaching in Physical Education and Health Courses in Colleges and Universities

3.1

Analysis of Learning Interests in the Interdisciplinary Model of Physical Education and Health Courses

With the deepening of the new curriculum reform and the promotion of quality education, the new type of teaching oriented to core literacy is also influencing physical education teaching, and the integrated teaching of learning, practicing and competing that allows students to learn in authentic contexts is becoming the main form of development of core literacy in physical education. For physical education, the effective integration of interdisciplinary learning and physical education learning is an important way for the current high school physical education class to develop students’ cooperation, practice, innovation and comprehensive learning ability. In physical education classes, the integration of motor skill learning and interdisciplinary learning is the main focus, and it is promoted by themes, so that students can comprehensively use interdisciplinary knowledge to analyze and solve the problems of actual motor learning in the classroom.

In the empirical study, students of a specific school sports and health course were randomly selected. They used random sampling to ensure that the sample was representative of the overall characteristics. In order to validate the interdisciplinary teaching mode of physical education and health course infiltrating OBE designed above, experimental and control groups were set up respectively, the experimental group adopts the interdisciplinary teaching mode of physical education and health course infiltrating the concept of OBE, and the control group adopts the traditional teaching mode, and the experimental period is 18 weeks, and the number of students in both groups is kept the same, with the value of 30 students.

3.1.1

Analysis of learning interest in the two classes before the experiment

The results of the analysis of students’ learning interest before the experiment are shown in Table 2, through the comparative analysis of data, the P-value of the two classes of students in physical education learning interest before the experiment is greater than 0.05, which indicates that there is no significant difference between the two groups of students in the interest of physical education learning, which meets the requirements of the experimental standards and makes the experiment carry out with significance.

Table 2.

Students study interest analysis results before experiment

Test index	N	Experimental group		Control group		T-Value	P-Value
Test index	N	Mean	SD	Mean	SD	T-Value	P-Value
Negative interest	30	15.25	3.55	15.86	3.75	0.558	0.567
Positive interest	30	21.46	5.57	20.86	5.16	-0.375	0.697
Sports participation	30	15.76	3.68	15.28	3.16	-0.457	0.638
Autonomy and inquiry	30	17.18	4.15	15.88	3.35	-1.148	0.247
Focus on sports	30	11.58	5.16	10.38	4.07	-0.838	0.394

3.1.2

Analysis of learning interest before and after the experiment in the experimental class

The experimental control analysis was used to analyze the students’ learning interests before and after the experiment on the interdisciplinary teaching mode of physical education and health curriculum permeated with the concept of OBE, and the results of the analysis of students’ learning interests before and after the experiment are shown in Table 3. The data show that the students in the experimental class showed significant differences in the three dimensions of negative interest (P=0.0001<0.05), positive interest (P=0.005<0.01), and autonomy and inquiry (P=0.006<0.01), and that the dimensions of sports participation and attention to sports did not show any significant differences, but they also had a certain improvement compared with the preexperiment.

Table 3.

The students study the analysis results before and after the experiment

Test index	N	Pretest		After testing		T-Value	P-Value
Test index	N	Mean	SD	Mean	SD	T-Value	P-Value
Negative interest	30	15.25	3.55	12.36	2.76	5.186	0.001
Positive interest	30	21.46	5.57	23.86	4.79	-3.438	0.005
Sports participation	30	15.76	3.68	17.18	4.38	-1.674	0.114
Autonomy and inquiry	30	17.18	4.15	21.18	4.38	-6.903	0.006
Focus on sports	30	11.58	5.16	12.08	4.45	-0.508	0.605

3.1.3

Analysis of students’ learning interest before and after the experiment in the control class

Next, the method of comparison before and after the experiment was used to analyze the students’ learning interest before and after the experiment of the control class students, and the results of the analysis of the students’ learning interest before and after the experiment of the control class students are shown in Table 4, and the data present that the students in the control class did not present significant differences in the four indicators. However, compared with the control class before the experiment, there was a certain degree of reduction in negative interest (0.7), but attention to sports also improved (2.06). The data show that regular physical education improves students’ negative interest and concern for sports, but the remaining three indicators do not show significant improvement.

Table 4.

The students study the results of the students’ interest in the study

Test index	N	Pretest		After testing		T-Value	P-Value
Test index	N	Mean	SD	Mean	SD	T-Value	P-Value
Negative interest	30	15.86	3.75	15.16	3.36	1.726	0.089
Positive interest	30	20.86	5.16	20.86	5.16	-2.27	0.058
Sports participation	30	15.28	3.16	15.27	3.16	-1.766	0.112
Autonomy and inquiry	30	15.88	3.35	15.88	3.35	-1.705	0.106
Focus on sports	30	10.38	4.07	13.07	4.78	-1.456	0.129

3.1.4

Analysis of learning interests of the two classes after the experiment

The results of the analysis of students’ learning interests after the experiment in the experimental class and the control class are shown in Fig. 4, which shows that the negative interest of the students in the experimental class and the control class in the physical education learning interest test after the experiment was significantly reduced in both classes, but a significant difference was presented in the two indicators of positive interest (21.46, 0.03) and autonomy and inquiry (21.38, 0.008), and the two qualities of students in the experimental class were improvement is significantly better than the control class.

3.2

Validation of the effectiveness of interdisciplinary recommendation models

3.2.1

Similarity calculation

Before confirming the validity of the recommendation model constructed above for interdisciplinary teaching of physical education and health courses, its similarity needs to be calculated and the corresponding similarity matrix needs to be constructed. When the subject of physical education is specified and the knowledge of disciplines with similar interests to it is calculated, there are many existing algorithms for similarity, such as Pearson’s coefficient, Jaccard’s similarity, cosine similarity, and modified cosine similarity, among other calculations. Using the similarity formula mentioned above, the similarity between the vectors is calculated by considering the interest rating data as multidimensional rating vectors. The interval of the similarity calculation result is [0,1], the larger the similarity value, the more similar the PE and health courses are, the interdisciplinary similarity matrix of PE and health courses is shown in Table 5, where 1~80 indicates the attribute values of the PE and health courses, and the size of its matrix is 80×80, which will be illustrated in the form of a matrix in the form of a table. It is found that the values about the interdisciplinary similarity matrix of physical education and health courses are kept between 0.5 and 0.7, which indicates that the interdisciplinary similarity of its physical education and health courses is in good condition, and its similarity can be used for the validation analysis of the effectiveness of the interdisciplinary recommendation model of the physical education and health courses that integrates the concept of OBE.

Table 5.

Interdisciplinary similarity matrix of physical and health courses

Disciplines	1	2	3	4	5	6	7	……	80
1	1	0.629	0.543	0.534	0.641	0.565	0.599	……	0.658
2	0.629	1	0.668	0.669	0.528	0.677	0.524	……	0.594
3	0.543	0.668	1	0.551	0.689	0.578	0.677	……	0.683
4	0.534	0.669	0.551	1	0.655	0.569	0.628	……	0.574
5	0.641	0.528	0.689	0.655	1	0.592	0.632	……	0.537
6	0.565	0.677	0.578	0.569	0.592	1	0.537	……	0.615
7	0.599	0.524	0.677	0.628	0.632	0.537	1	……	0.646
……	……	……	……	……	……	……	……	1	……
80	0.658	0.594	0.683	0.574	0.537	0.615	0.646	……	1

3.2.2

Validation results

By calculating the similarity, the students’ interest degree value for the interdisciplinary teaching model of a fusion OBE concept physical education and health course is given, and the data of the interest degree score value is shown in Table 6, and it is found that the number of students is from 71050015 to 71050149, and the number of the corresponding physical education and health course collection is from 3400025 to 3400280, and the interest degree value ranges from 0 to 5, and the prediction of the current students’ interest value for an interdisciplinary teaching of a PE and health course integrating OBE concepts, that is, the content recommendation for interdisciplinary teaching of a PE and health course integrating OBE concepts for current students. In this paper, the validity of the interest scores was validated using the model proposed above, and the MAE mean absolute error, which was earlier applied to evaluate the performance of recommendation prediction and is a commonly used measure of the effectiveness of recommendations, was used when making student interest degree-based recommendations for interdisciplinary instruction in physical education and health courses. If a student’s interest level rating set for recommending n instructional content is {P₁,P₂,⋯,P_n}, and the student’s actual rating set is {q₁,q₂,⋯,q_n}, then the mean error of the interdisciplinary instructional content recommendation model for integrating OBE concepts physical education and health courses can be expressed as: (13) $M A E = \frac{\sum_{i = 1}^{n} | p_{i} - q_{i} |}{n}$ Table 6.

Interest rating data

Disciplines	Student quantity	Set of sports and health courses	Interest value
1	71050025	3400107	5
2	71050015	3400124	1
3	71050082	3400177	4
4	71050137	3400123	1
5	71050047	3400177	1
6	71050035	3400169	4
7	71050078	3400025	3
……	……	……	……
80	71050149	3400280	5

The smaller the value of MAE, the smaller the bias, indicating that the predicted interest rating of the students is closer to the real situation, which can reflect the accuracy of the recommendation results to a certain extent.

In order to verify the effectiveness of the model in the students’ scoring of the interdisciplinary teaching content recommendation of the physical education and health course that integrates OBE concepts, the model approach proposed in this paper will be compared with the following two scoring approaches, the scoring of the loan time by incremental time scale (ITS) and the scoring based on the loan time scale (BTS) will be normalized by both other scoring treatments under different number of proximate neighbors, k for instructional content recommendation. In the sequence of instructional content recommendations, if the value of k is too small, the recommendation is not efficient, and if it is too large, it will increase the operation burden. The model proposed in this paper is compared horizontally with the other two scoring methods, and the MAE results of different student interest scoring methods are shown in Fig. 5, which shows that the recommended model proposed in this paper is more effective in evaluating students’ interest in teaching (the overall MAE average is 0.7606), and the average absolute error of its recommendation is smaller, indicating that the method can measure students’ interest in interdisciplinary teaching and provide a reference basis for the research of interdisciplinary teaching and learning of physical education and health courses in the view of OBE. It provides a reference basis for the research of interdisciplinary teaching in physical education and health courses under the OBE perspective, enriches the way students rate their teaching recommendations, and in addition provides integration paths and innovative strategies for the interdisciplinary physical education and health courses infiltrating the OBE concept.

4

Conclusion

Interdisciplinary integration emphasizes the attention to the interconnection between disciplines, and physical education and health teachers should update the traditional teaching ideas, improve their interdisciplinary literacy, and promote the effective integration of physical education and health and other disciplines by means of carrying out thematic learning activities. Based on this, this paper is based on the perspective of OBE teaching concept, constructed a recommended model for interdisciplinary teaching of physical education and health courses, and used the constructed model to analyze the case teaching of interdisciplinary physical education and health courses in colleges and universities. The experimental group and the control group showed significant differences in the indicators of active interest (21.46, 0.03) and autonomy and inquiry (21.38, 0.008), indicating that the teaching model of physical education and health courses infiltrated with the concept of OBE has a more significant effect than the traditional teaching model. Then the validity of the model constructed above was verified, and it was found that the model of this paper (0.7606) has a smaller mean absolute error of recommendation compared with ITS and BTS, which confirms that the model is able to measure the students’ interest in interdisciplinary teaching and provides a reference basis for the research on interdisciplinary teaching of physical education and health courses under the perspective of OBE. Ultimately, based on the results of the interdisciplinary case teaching analysis of physical education and health courses in colleges and universities, the interdisciplinary integration of physical education and health courses can be promoted in the four aspects of playing the leading role of the government, optimizing the learning environment of physical education courses, constructing the quality monitoring system of physical education teaching and expanding the ways of cooperation between schools and enterprises.

Further research can be conducted on the scientific nature of the data used in the model, as well as the challenge and optimization of the model in dealing with more complex data.It is hoped that the researchers will be able to continuously promote the idea of OBE in the field of sports and health.

Sprache:: Englisch

Zeitrahmen der Veröffentlichung:: 1 Hefte pro Jahr
Fachgebiete der Zeitschrift:: Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere

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Research on Interdisciplinary Integration Path of Core Literacy in Physical Education and Health Curriculum under the Perspective of OBE

Lin Xu

Online veröffentlicht: 17. März 2025

Eingereicht: 19. Nov. 2024

Akzeptiert: 18. Feb. 2025

DOI: https://doi.org/10.2478/amns-2025-0239

SchlüsselwörterTF-IDF, Disambiguation algorithm, Vector transformation, Recommendation model, Interdisciplinary

© 2025 Lin Xu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
TF-IDF, Disambiguation algorithm, Vector transformation, Recommendation model, Interdisciplinary