Construction of Teaching Innovation Team Building System for Higher Vocational Teachers in the Context of Skill-based Society in Information Age

Informatization teaching has become the trend of modern education development, which provides teachers with more teaching resources and teaching tools, and also puts forward higher requirements for teachers’ teaching methods and teaching teams [1-3].

Higher vocational colleges and universities are important places to cultivate professional talents for the society and are also an important part in the development of vocational education in China. The construction of high-quality and high-caliber teacher innovation team is the key to cultivate high-quality and high-skilled talents in higher vocational colleges and universities [4-7]. However, the existing teacher team has outstanding problems such as irrational structure, insufficient comprehensive practice ability, insufficient collaborative innovation, and imperfect ability enhancement and improvement mechanism, which seriously affect the construction and development of teacher teaching creation team in higher vocational institutions [8-11].

Therefore, the construction of teacher teaching innovation team has a positive role in promoting the teaching reform of higher vocational institutions. The construction of teacher teaching innovation team in higher vocational colleges not only concerns the quality of education and teaching, but also involves the overall development of the school [12-15]. The construction of team can promote the communication and cooperation among teachers, break the barriers of individual teaching, and form collective wisdom. At the same time, teams can provide more resources and support for teachers’ teaching innovation [16-18]. And in the context of skill-based society in the information age, the construction of teams is of great significance to improve teachers’ information-based teaching ability and realize the modernization of education [19-21]. By cultivating teachers with innovative spirit, teaching enthusiasm and informatization skills, we can provide students with more valuable and practical education and contribute to the cultivation of more high-quality technical talents for the society [22-25].

This paper first clarifies the construction principle of a teaching innovation team and highlights the practical significance of classroom teaching quality assessment for evaluating a teaching innovation team. Secondly, based on the hierarchical analysis method, it establishes hierarchical relationships and evaluation indexes for evaluating the teaching quality of higher vocational teachers. Then, the questionnaire survey method is used to collect experts’ opinions, and the Delphi method and hierarchical analysis method are used to determine the weights of the first-level and second-level constituent elements of the higher vocational classroom teaching quality assessment system. Finally, a higher vocational college is selected as the research object, and the teaching quality assessment model proposed in this paper is applied to evaluate the quality of teaching. Using the technique of association rules, the student evaluation and peer teacher evaluation in the model are associated with the evaluation grade respectively, and the connection between the two aspects of student evaluation and peer teacher and teaching quality is analyzed.

2

Research on the construction of the assessment system for the quality of higher vocational classroom teaching

2.1

Construction of Teaching Innovation Teams in the Context of Informationization

Teaching innovation team building, teachers are required to change the attitude of education, improve the effectiveness of education to continue to focus on the problem, change the “teacher as the main body” of the traditional concept of education, learning and practicing the “students as the main body, the teacher as the guide” of the new concept of education, student-centered, student development as the main body, the real realization of the fundamental task of moral education. In order to achieve the fundamental task of moral education, we should consider students as the main source of development. Reform one-sided grafting, didactic, duck-type teaching mode, the teacher’s “teaching” and student “learning” organically combined, give full play to their enthusiasm and creativity, make full use of the results of the teaching work, to achieve the best teaching results, innovative teaching methods, the establishment of an efficient classroom. Teaching methods, the establishment of efficient classroom.

In order to overcome the difficulties of simplified teaching methods in the past, we encourage the wide application of modern information technology such as big data, artificial intelligence, VR/AR, 5G, etc., make extensive use of special educational resources, offer information technology courses on high-quality networks, and set up a golden hybrid training course for higher vocational colleges and universities. In addition to this, it is also necessary to improve students’ ideological and political awareness, and to study the ideological and political factors of socio-economic hotspots in conjunction with the content of the curriculum and incorporate them into the educational process. Strengthen the knowledge and awareness and ideological and political education, highlighting practice, linking theory to practice, adhering to the new development concept of “deep integration of production and culture, school and enterprise co-construction”, widely distributed in and out of the school, theory and practice. According to the students’ professional characteristics, according to the production process, according to the enterprise’s actual production of the whole process, through the adoption of appropriate local education methods, students can learn and practice in the real or similar “production site”, and gradually master the skills to complete the future tasks.

2.2

The Relevance of Classroom Teaching Quality Assessment for the Evaluation of Teaching Innovation Teams

1)

Clarify the connotation of teaching innovation team building and the core essence of teaching evaluation reform

The improvement of classroom teaching quality is the key to the sustainable development of higher vocational colleges and universities. The evaluation of teachers’ classroom teaching quality needs to incorporate the construction requirements of teaching innovation team, and the evaluation results should be widely used in a number of activities, such as teaching quality excellence assessment, team performance evaluation, and teachers’ teaching improvement. However, the first batch of projected teams still have cognitive and practical misunderstandings or deviations in the theoretical basis, implementation strategy, operation management, and acceptance criteria of team building. The Overall Plan for Deepening Educational Evaluation Reform in the New Era specifies the need to further strengthen the tasks of teaching quality assessment and teaching innovation team building, emphasizing that the setting of classroom teaching quality evaluation indicators should pay more attention to the process development of the team. This enables us to gain a deeper understanding of the intrinsic qualities of teaching innovation teams and the core essence of education evaluation reform.

2)

Provide effective support for the construction of teaching quality evaluation system for teachers in higher vocational colleges and universities

Classroom teaching quality evaluation for teachers is the most direct way to test their teaching level, and it is also an important measure to promote education reform and improve the overall teaching level. At present, there is a problem of absolutization in the evaluation of classroom teaching quality of teachers in higher vocational colleges and universities, and the existing evaluation system does not pay enough attention to assessing the enterprise practice ability of school teachers and the classroom teaching ability of part-time teachers in enterprises. Excellent evaluation standards for classroom teaching quality should pay more attention to the comprehensive and professional quality of students, thus better promoting the achievement of teaching goals. At the same time, the practicality and innovativeness of the higher vocational classroom should be continuously improved, and the teachers’ teaching innovation team should be encouraged to cooperate with enterprises and develop teaching resources according to the actual needs of enterprises, so that the teachers’ teaching focus can be shifted to cultivating the comprehensive quality of the students, and a higher vocational teaching quality evaluation system can be constructed on this basis.

3)

Promote the collaborative community of teachers’ teaching innovation team to improve the teaching quality

Teachers’ teaching innovation team is a community of relationship or community of practice jointly formed by excellent teachers and industrial instructors with different professional and practical backgrounds. This community is established on the basis of collaborative mechanism, through the reform of teaching content and methods, the development of teaching resources, and the promotion of teaching seminars and experience exchange, in order to realize the purpose of improving teaching quality. The formation of teachers’ teaching innovation teams is conducive to breaking down the organizational barriers between higher vocational colleges and enterprises, and promoting collaborative innovation between the two sides in the areas of curriculum construction, teaching reform and vocational skills level certificate examination and training.

3

Research on the construction of quality assessment model based on hierarchical analysis method

3.1

Construction of Teaching Quality Evaluation Indicators for Higher Education Teachers

3.1.1

Rationale for the construction of the model’s indicator system

The aim of higher vocational education is to produce high-quality skilled specialists with an innovative spirit, practical abilities, creativity, employability, and entrepreneurship. In order to closely follow the requirements of the talent cultivation goal, emphasize the practical, open and vocational nature of the talent cultivation process, pay attention to absorbing the new achievements of professional development and teaching reform, strengthen the practical teaching, highlight the students’ vocational ability cultivation and the improvement of comprehensive quality, we should take the talent cultivation goal as the basis for the evaluation of the quality of teaching of the teachers of the higher vocational education so as to cultivate the high-quality skilled specialists.

The model of this paper is based on the basic starting point of higher vocational training objectives, based on the teaching quality evaluation method of teachers in a domestic higher vocational college, in order to ensure that the teaching evaluation of teachers is carried out more comprehensively, the teaching evaluation is carried out from the students’ evaluation of teaching and peer listening to the class in two aspects.

To determine the index system, the students and teachers from each college and major of the university were used as survey objects, and data was collected using the self-administered questionnaire method. The questionnaires were designed to be composed of evaluation indexes related to the evaluation of teachers’ teaching quality from the aspects of students’ evaluation of teaching and peers’ listening to lectures, and then the questionnaires were randomly distributed to the respondents by using the stratified sampling method so that they could complete the questionnaires independently, and the validity of each questionnaire was examined. A total of 250 questionnaires were sent out and 238 were recovered, with a recovery rate of 95.2%, and 225 valid questionnaires, with a validity rate of 94.5%. The questionnaire students came from different majors and grades, and the questionnaire teachers came from different colleges with different majors, different age stages, and different titles. With the help of sample survey data, the evaluation indexes of each level were determined.

This paper analyzes the data collected from the questionnaire based on the training objectives of higher vocational education, and the following will establish the hierarchical relationship and evaluation indexes for the evaluation of teaching quality of higher vocational teachers respectively.

3.1.2

Evaluation indicators for student assessment of teaching and learning

As the main body of the teacher’s teaching process, students have the most say in how good or bad the teacher’s teaching is. The evaluation indexes of student evaluation are shown in Table 1. There are four first-level indexes in student evaluation of teaching: teaching attitude, teaching method, teaching ability, and teaching effect. Each first-level indicator contains several second-level indicators. Since the indicators are both independent of each other and subject to each other, the evaluation indicators in the table follow the principle of relative independence and the principle of comprehensive wholeness.

Table 1.

Evaluation indicators of students’ evaluation of teaching

Evaluation subject	Primary index	Secondary index
Student evaluation of teachingA	Teaching attitudeA1	Love their work, be a teacher, teach peopleA1-1
		Carefully prepare lessons, teach, organize classroom discussion, tutoring, practice and other teaching linksA1-2
		Care and strict requirements for students, humbly listen to all opinions, and constantly improve the level of teachingA1-3
	Teaching methodA2	Teaching effective practice, clear organization, able to understand the simple, the focus is prominent, the difficult problems to talk deeplyA2-1
		The blackboard writing is clear, the arrangement is reasonable, the detail is appropriate, the lecture is clear, the language is vivid, the speed is moderateA2-2
		The theoretical contact practice, for example, is appropriate and appliedA2-3
	Teaching abilityA3	Constantly enrich and update the teaching content, absorb the new research results in the professional field, reflect the new trend of teaching reform, and have a sense of innovationA3-1
		Teach students according to their aptitude, take the trouble to answer students’ questions, and help students solve difficult problemsA3-2
		Pay attention to the combination of imparting knowledge and cultivating ability, appropriate encouragement and guidance, and consciously strengthen the cultivation of students’ vocational skillsA3-3
	Teaching effectA4	After learning this course, students have a good grasp of the book knowledge and the basic requirements of the curriculum standardsA4-1
		After learning this course, it will help students to improve their ability to analyze and solve problemsA4-2
		After learning this course, students can improve their self-learning abilityA4-3

3.1.3

Evaluation metrics for peer-heard lectures

Different teachers use different teaching methods, in order to enhance mutual learning between peers and complement each other’s strengths and weaknesses, it can be used to continuously improve teachers’ teaching ability, teaching level and teaching methods by listening to lessons between teachers. Most of the higher vocational colleges and universities in China have implemented the teacher-student listening system. In addition, the evaluation results are more accurate due to the close proximity of specialties between peers. The evaluation indexes of peer lectures are shown in Table 2. There are four first-level indexes in peer lectures: teaching attitude, teaching method, teaching ability, and teaching effect. The indicators in the table follow the principles of comprehensive wholeness and relative independence.

Table 2.

Evaluation indicators of peer attendance

Evaluation subject	Primary index	Secondary index
Peer lectureB	Teaching attitudeB1	Academic rigorous, as a teacher, strict attendance, teaching and educating peopleB1-1
		Familiar with the curriculum standards, proficient in the content, fully prepared for the lessonB1-2
		Warm-hearted counseling students, take the trouble to help students solve difficult problemsB1-3
	Teaching methodB2	The language expression is clear and concise, and the blackboard writing is neat and standardizedB2-1
		According to the characteristics of teaching content, appropriate teaching AIDS and a variety of teaching techniques are adopted to teach, and attention is paid to guiding students to transform knowledge into abilityB2-2
		For the whole body, can better maintain the class order, pay attention to feedback adjustment, astutely deal with accidental eventsB2-3
	Teaching abilityB3	The teaching objectives are in line with the actual situation of students, the content is full, no knowledge mistakes, and the required teaching skills are skilledB3-1
		The regulations are clear, the focus is prominent, the difficult problems are seized and solved, and the requirements of “necessary and sufficient” are paid attention toB3-2
		Pay attention to absorb the new information of the development of the professional field (discipline), update the teaching content, and provide valuable information in the classroomB3-3
	Teaching effectB4	The teaching process is compact and time allocation is appropriateB4-1
		The correct rate of oral answering, classroom exercises and written assignments is higherB4-2
		To complete the teaching task on time and achieve the teaching purpose, students at all levels have gained something, and the teaching reflects wellB4-3

3.2

Extending the model based on the data mining hierarchical analysis method

AHP hierarchical analysis is one of the widely used scientific decision-making techniques, and all the behavioral choices and judgments made in people’s lives can be called decision-making. Any decision-making is the result of comprehensive judgment made under the framework of certain systematic theories of the actors, and it is also the process of repeated comparison, judgment and selection of many factors that will have an impact on the results. However, in this many factors, only a small part of the factors can be quantitative indicators to indicate that most of the factors can only be qualitative can not be quantified, so the analysis and treatment of these factors can not be quantified mainly depends on the subjective choice of people. Only scientific decision-making can better ensure the smooth development of society, economy, education and other aspects, so a scientific and effective decision-making tool is particularly important for everyone.

In this paper, in the process of establishing the teaching quality evaluation system of higher vocational colleges and universities, the basic principles and process of hierarchical analysis are studied in depth, and the problem of assigning the weights of multiple indicators is solved by combining qualitative and quantitative methods. Meanwhile, association rules and other related technologies are applied in the analysis process to make the evaluation system more perfect and practical. 1)

Constructing a recursive hierarchical model using split-level clustering method

In general, people understand and analyze complex problems mostly by the method of division, so for a larger system we can also decompose it and then study it. Decomposition is generally represented by an inverted tree hierarchy: (1)

High level (goal level): it is used to represent the predetermined goal and usually consists of one element;

(2)

Middle level (guideline level): consists of a number of levels mainly set to achieve the predetermined goal;

(3)

The bottom layer (program layer): decision-making programs, various measures, etc. provided to accomplish the predetermined goals.

Split-level clustering method is a method of splitting levels from top to bottom, which is often referred to as the method of totaling first and then dividing. The principle of the method is very simple, that is, first all the data objects are placed in a cluster, and then gradually subdivided into smaller clusters, (there is like drawing the form of an inverted tree, first draw the roots, then draw the branches, and finally draw the leaves.) Until the smaller clusters are able to complete a prescribed function relatively simply, even if the split is complete. As in the teaching quality evaluation system, the constructed hierarchical model is the evaluation index system. The evaluation index system’s highest level is about to split the 0th step, and then divide it into two steps to create a two-level indicator structure for the progressive hierarchical model.

2)

Construction of judgment matrix

If the upper factor target layer is set to U and the lower factor criterion layer is set to U₁, U₂, ⋯U_n, we can set the corresponding weights based on the dependence of each lower factor on the upper factor W₁, W₂, ⋯W_n. If the dependence of each lower factor on the upper factor can be expressed quantitatively, the weights of each lower factor can be set directly, but for the lower factors that are qualitative and with subjective decision-making, it is difficult to set their corresponding weights directly, so we can only use other calculation methods to get the weights. However, for those qualitative and subjective decision-making factors, it is difficult to directly set their corresponding weights, so the weights of the lower factors can only be obtained by using other calculation methods. The main purpose here is to compare all the factors involved with each other, i.e., to take the ratio of the influence of the two lower factors U_i and U_j on the target factor U, and the result is denoted as U_ij. Combining all the results of the comparison results results in the judgment matrix, which is expressed as follows: (1) $U = {(U_{i j})}_{n * n} = (\begin{matrix} U_{11} & U_{12} & \dots & U_{1 n} \\ \dots & \dots & \dots & \dots \\ U_{n 1} & U_{n 2} & \dots & U_{n n} \end{matrix})$

The judgment matrix has the following characteristics: (1)

$U = {(U_{i j})}_{n * n}$ , U_ij > 0, 2) U_ij*U_ji = 1, 3) U_ii = 1

So call the judgment matrix U as positive and negative matrix.

The properties of this matrix are as follows:

(2)

All eigenvalues of U are 0 except the largest eigenvalue λmax = n;

(3)

All rows of U are positive multiples of any row, so R(U) = 1;

(4)

The transpose matrix U^T of U is also consistent;

Summarizing the above properties it follows that if U is the consistency matrix, λ_max = n, then the eigenvector corresponding to λ_max is normalized and recorded as $W = {(W_{1}, W_{2}, \dots W_{n})}^{T}$ , where $\sum_{i = 1}^{n} W_{i} = 1$ , W is called the weight vector, which represents the weight of U₁, U₂, ⋯U_n in the objective U. Within this system, the weights of each factor can be determined by comparing the factors two by two.

3)

Calculation of weights

The common methods for calculating the weights are: square root method, power multiplication method and sum-product method, etc. These methods are used to calculate the specific eigenvectors in the judgment matrix that we have constructed W. In this paper, we use the sum-product method to calculate the weights and verify whether the consistency effect of the construction of the judgment matrix is in order according to the maximum eigenvalue in the eigenvectors of the judgment matrix. The specific calculation process is as follows:

First normalize the columns (2) ${\bar{U}}_{i j} = \frac{U_{i j}}{\sum_{k = 1}^{n} U_{k j}}$

Then average the canonical columns to determine the final weights (3) ${\hat{W}}_{1} = \frac{1}{n} \sum_{j = 1}^{n} {\bar{U}}_{i j}$

The vector $\hat{W} = {({\hat{W}}_{1}, {\hat{W}}_{2}, \dots {\hat{W}}_{n})}^{T}$ , which is the eigenvector sought, is also the weights of the factors. The eigenvectors are then further solved and computed to give the maximum eigenvalue λmax and this λmax is utilized to check the consistency of the data. The maximum eigenvalue of the judgment matrix U is calculated using the following formula: (4) $λ \max = \frac{1}{n} \sum_{i = 1}^{n} \frac{{(U \hat{W})}_{i}}{W_{i}}$

In Eq. ${(U \hat{W})}_{i}$ is the i rd element of vector $U \hat{W}$ . 4)

Verification of consistency

In the process of constructing the judgment matrix, there will certainly be some subjective factors, so in the process of comparing the two factors will inevitably appear inconsistent, only whether this inconsistency is within the permissible range. In order to solve this problem, so a way to calculate the consistency index is proposed. If the consistency index test does not meet the requirements, it is necessary to re-examine the judgment matrix and make appropriate changes; on the contrary, it shows that the set judgment matrix comparison is indeed feasible.

Assuming that CI is the consistency index of the judgment matrix, the formula for calculating the consistency index CI is as follows: (5) $C I = \frac{λ \max - n}{n - 1}$

The value of CI is to determine how much the constructed judgment matrix is different procedure from the perfectly consistent matrix, the larger the value, the greater the degree of difference, and vice versa indicates that the two matrices are essentially consistent and the constructed judgment matrix is very good. In turn, the consistency ratio CR = CI/RI. RI denotes the stochastic consistency index and for n = 1 ~ 9, Saaty et al. determined the value of RI through their study.

When the consistency ratio is CR < 0.10, it indicates that the constructed judgment matrix U has good consistency, and its corresponding eigenvector W can be fully used as the weights. When the consistency ratio is CR > 0.10, it is necessary to readjust U to reduce the consistency ratio until the condition is satisfied. At this point, the calculated eigenvectors are processed by normalization and other techniques, and the weights of each level are very reasonable. 5)

Association rule test

In order to improve the correctness and rationality of the evaluation system indicator levels and weights, we will mine the association rules from the collected evaluation data. The association rules are utilized to check whether the evaluation system indicator levels and weights are in line with reality. If they are not in line with the reality, the parts that are more different from the reality will be used as feedback information according to the mining results, so that adjustment and optimization can be made. Valuable knowledge obtained from the association rules of data mining can also be added to the knowledge base to guide the construction and adjustment of the evaluation system in the next step.

The extended model of AHP based on data mining is shown in Figure 1:

4

The design and application of assessment model of senior classroom teaching quality

4.1

Determination of weights of teaching quality assessment indicators

This study invites 19 experts to score the weights of the classroom teaching quality assessment system of higher vocational colleges and universities through an expert consultation questionnaire, to this end, the study constructs an expert weight consultation questionnaire for the comparison of the constituent elements of all levels between two by two according to the hierarchical structure of the previous two evaluation indexes, and the experts score the importance of the first-level and second-level constituent elements of the evaluation system between two by two respectively, and then according to the obtained importance scores The judgment matrix between these evaluation elements is constructed.

4.1.1

Constructing judgment matrices

The scoring scale used is the Satty1-9 scale method, and the correlation between the degree of importance and the scale value. After the feedback forms of 19 consulting experts were recovered, the judgment matrices of these experts for all levels of constituent elements were collated, and then based on the completeness of matrix filling, the validity of these 19 experts’ judgment matrices was preliminarily judged, so as to carry out random consistency test, and then finally, the calculation and analysis of the matrices were carried out. In this study, the geometric mean method will be used to calculate the weight values of the first-level constituent elements, taking the assignment of one expert to the first-level constituent elements as an example:

According to the expert’s assignment of the first-level constituent elements, there are four elements of the first-level constituent elements: teaching attitude A, teaching method B, teaching ability C, and teaching effect D. Table 3 displays the judgment matrix of the expert on the first-level constituent elements, which will be converted into a judgment matrix based on the expert’s assignment results.

Table 3.

Expert judgment matrix of first-level components

	Teaching attitudeA	Teaching methodB	Teaching abilityC	Teaching effectD
Teaching attitudeA	1	1/5	1/3	1/5
Teaching methodB	5	1	1/2	3
Teaching abilityC	3	2	1	3
Teaching effectD	5	1/3	1/3	1

4.1.2

Deriving component weights

Following the calculation of the weight coefficients of the indicators for expert a, the ratings of the remaining 18 experts were calculated. The study used the Delphi method, and the results of the weights of the components at all levels of the student evaluation system are shown in Table 4. As a result of the calculation, four decimals are retained, and A1~A4 are used to indicate the weights of the first-level constituent elements, and B1~B12 are used to indicate the weights of the second-level constituent elements.

Table 4.

Weight of constituent elements at all levels of student evaluation system

First-order component weight	Secondary component weight
A1 0.1455	B1 0.0783
	B2 0.0378
	B3 0.0294
A2 0.1533	B4 0.0759
	B5 0.0408
	B6 0.0366
A3 0.3715	B7 0.1415
	B8 0.1304
	B9 0.0996
A4 0.3297	B10 0.1207
	B11 0.1066
	B12 0.1024

The results of the weights of the components at all levels of the teacher evaluation system are shown in Table 5:

Table 5.

Weight of constituent elements at all levels of student evaluation system

First-order component weight	Secondary component weight
A1 0.1455	B1 0.0826
	B2 0.0372
	B3 0.0257
A2 0.1533	B4 0.0893
	B5 0.0401
	B6 0.0239
A3 0.3715	B7 0.1399
	B8 0.1182
	B9 0.1134
A4 0.3297	B10 0.1203
	B11 0.1105
	B12 0.0989

4.2

Validation and analysis of the teaching quality assessment model

In the previous section, the weights of the first-level and second-level components of the senior classroom teaching quality assessment system were determined through the Delphi method and hierarchical analysis, in order to verify the rationality of the senior classroom teaching quality assessment system, as well as to understand the status quo of the classroom teaching quality of the senior colleges and universities at this stage. In this paper, a higher vocational college is selected as the research object, and a questionnaire is prepared according to the higher vocational classroom teaching quality assessment system, which is divided into a student evaluation questionnaire and a teacher evaluation questionnaire, and carries out a quality evaluation survey on the most important evaluation subjects of higher vocational colleges and universities at two different levels, namely, the students’ evaluation of teaching and the peers’ listening to lectures, respectively. The questionnaire sets five evaluation levels, and the collection of comments is as follows: V={very satisfied, relatively satisfied, average, relatively dissatisfied, very dissatisfied}. A total of 540 questionnaires were distributed in this survey, and a total of 488 valid questionnaires were recovered, with a valid questionnaire recovery rate of 90.3%.

4.2.1

Reliability analysis of the teaching quality assessment system

Reliability analysis, also known as reliability analysis, is used to measure whether the sample response results are reliable, i.e., whether the sample has truthfully answered the scale-type items. In order to verify the reliability of the components of the teaching quality assessment system proposed in this paper, this paper uses the questionnaire analysis software SPSS to calculate the reliability of the student evaluation questionnaire and the teacher evaluation questionnaire, and derive the Cronbacha reliability coefficient.

The results of the reliability of the components of the teaching quality evaluation system are shown in Table 6. The ɑ coefficient of the teaching attitude evaluation dimension of the student evaluation questionnaire is 0.942, the ɑ coefficient of the teaching method evaluation dimension is 0.979, the ɑ coefficient of the teaching ability evaluation dimension is 0.932, the ɑ coefficient of the teaching effectiveness evaluation dimension is 0.957, and the ɑ coefficient of the total scale of the student evaluation questionnaire is 0.985. The ɑ coefficient of the teaching attitude evaluation dimension of the teacher evaluation questionnaire is 0.951, the The ɑ-coefficient of teaching method evaluation dimension is 0.968, the ɑ-coefficient of teaching ability evaluation dimension is 0.978, the ɑ-coefficient of teaching effectiveness evaluation dimension is 0.954, and the ɑ-coefficient of teacher evaluation questionnaire total scale is 0.988, and the above ɑ-coefficient values are in line with the values of Prof. Minglong Wu’s “Questionnaire Statistical Analysis Practice- SPSS Operation and Application” by Prof. Ming-Lung Wu, the reliability requirement of the scale is 0.9 and above, which means that the reliability of the components and the total scale is very good.

Table 6.

Reliability of components of teaching quality evaluation system

Constituent element	Cronbacha ɑ		Number of terms
Constituent element	Student evaluation	Teacher evaluation	Student evaluation	Teacher evaluation
Teaching attitude evaluation	0.942	0.951	3	3
Teaching method evaluation	0.979	0.968	3	3
Teaching ability evaluation	0.932	0.978	3	3
Teaching effect evaluation	0.957	0.954	3	3
Total	0.985	0.988	12	12

4.2.2

Analysis of the components of the evaluation of teaching attitudes

The previous section analyzed students’ and teachers’ ratings of teaching quality evaluation from two different levels, namely, students’ evaluation of teaching and peer listening; the main content of this section is based on the four evaluation elements of the CIPP evaluation theory, which analyzes the different components of teaching evaluation, so as to be able to propose improvement strategies for the improvement of teaching quality from the different components. The topics involving the evaluation of teaching attitudes from both student evaluation of teaching and peer listening are specifically: 1)

Student Evaluation of Teaching

Q1: Teachers love their jobs, serve as teachers and educate people.

Q2: Teachers prepare and teach seriously, organize classroom discussions, tutorials, practice and other teaching sessions.

Q3: Teachers care about and strictly require students, listen to opinions from all aspects, and constantly improve their teaching level.

2)

Listening to lectures by peers

Q4: Teachers are rigorous in their teaching, serve as role models, take strict attendance, and teach and educate people.

Q5: Teachers are familiar with the curriculum standards, proficient in the content, and well-prepared for the lessons.

Q6: Teachers are enthusiastic in counseling students and take great pains to help students solve difficult problems

The descriptive analysis of the components of teaching attitude evaluation is shown in Table 7. According to the students’ evaluation questionnaire in Table 7, the mean value of the three questions of teaching attitude evaluation is above 4 points, and the standard deviation is less than 1, which means that the students’ evaluation of these questions is in the degree of “relatively satisfactory”, and most of the students’ ratings are more concentrated, and the data fluctuation is small.

Table 7.

Descriptive analysis of elements of teaching attitude evaluation

Question	Sample size	Minimum value	Maximum value	Mean value	Standard deviation
Q1	278	1	5	4.15	0.96
Q2	278	1	5	4.32	0.88
Q3	278	1	5	4.09	0.97
Q4	210	1	5	4.09	0.78
Q5	210	1	5	3.84	0.72
Q6	210	1	5	3.92	0.81

According to the questionnaire of peer listening in Table 7, the mean value of two questions in the evaluation of teaching attitude is lower than 4, which are the teacher’s preparation for the lesson and the teacher’s evaluation of assisting the students’ motivation, which to a certain extent indicates that the teachers still need to improve their understanding of the curriculum standards and the consideration of the students in the curriculum.

Similarly, the results of the descriptive analysis of the other components of the teaching quality assessment system can be derived.

4.3

Analysis of the correlation between indicators and evaluation levels within the model

In this section, this paper utilizes the technique of association rules to associate student evaluations of teaching and peer teacher evaluations with evaluation ratings in the model, respectively, and the sources of evaluation ratings are analyzed with clustering of the evaluation data. There are a total of 24 evaluation items in the teaching evaluation form, and clustering for all 24 data items is unnecessary. Therefore, this paper reorganizes the data in the evaluation form from 2 evaluation dimensions.

The values of the 2 evaluation dimensions are equivalent to the sum of the values of each question of the four evaluation elements. After the transformation, we have converted the 24 evaluation items to the following two dimensions: “Student Evaluation” and “Teacher Evaluation”. We can use these two aspects as representative indicators to complete data clustering on the four attributes of the 488 data samples that were collected and organized.

4.3.1

Data acquisition

The new data evaluation table needs to be followed by an evaluation rating accordingly, and the initial table to be correlated is shown in Table 8.

Table 8.

Initial table to be associated

Student evaluation	Teacher evaluation	Evaluation level
48	49	Relatively satisfactory
47.7	48.3	Relatively satisfactory
23.7	24.1	Less satisfied
36.5	36.9	general
13	15	Very dissatisfied
……	……

4.3.2

Data pre-processing

Now, this paper converts the data of “student evaluation” and “teacher evaluation” in Table 8 by data generalization. For example, we convert the two aspects of “student evaluation” and “teacher evaluation” in the above table into “excellent”, “good”, “medium”, “poor”, etc. Table 9 displays the precise conversion criteria for the evaluation dimension.

Table 9.

Conversion standards of Evaluation dimension

Evaluation dimension	After generalization grade
Score≥54	Optimal
48≤Score<54	Good
42≤Score<48	Intermediate
Score<42	Poor

The evaluation table after generalizing the data in Table 7 according to the conversion criteria in the above table is shown in Table 10.

Table 10.

Evaluation table after generalization of data

Student evaluation	Teacher evaluation	Evaluation level
Good	Good	Relatively satisfactory
Intermediate	Good	Relatively satisfactory
Poor	Poor	Less satisfied
Poor	Poor	general
Poor	Poor	Very dissatisfied
……	……

4.3.3

Linkage between quality of teaching and evaluation ratings

Synthesizing the data given in Table 10, the data is computed by the modified APriori algorithm, setting 0.2 as the minimum support, and the resulting frequent item set and support results are shown in Table 11.

Table 11.

Frequent item set and support Number of supported items

Student evaluation			Teacher evaluation
Frequent item set	Support degree	Support number	Frequent item set	Support degree	Support number
Student evaluation=Optimal	0.4	160	Student evaluation=Optimal	0.3	120
Student evaluation=Good	0.6	240	Student evaluation=Good	0.6	240
Evaluation level=Highly satisfied	0.5	200	Evaluation level=Highly satisfied	0.4	160
Evaluation level=Relatively satisfactory	0.7	280	Evaluation level=Relatively satisfactory	0.6	240
Student evaluation=Optimal, Evaluation level=Highly satisfied	0.4	120	Student evaluation=Optimal, Evaluation level=Highly satisfied	0.3	100
Student evaluation=Good, Evaluation level=Highly satisfied	0.6	160	Student evaluation=Good, Evaluation level=Highly satisfied	0.3	100
Student evaluation=Good, Evaluation level=Relatively satisfactory	0.6	160	Student evaluation=Good, Evaluation level=Relatively satisfactory	0.5	140

Combining the data in Table 11 and setting the minimum confidence level at 50%, the confidence level for a student evaluation of excellent and a grade rating of very satisfied is 0.8, and when the student evaluation is good, the confidence level is 0.6 regardless of whether the grade rating is very satisfied or more satisfied.The comparison of these two data shows that one of the major key factors in improving the quality of teaching and learning is to take the students as the main body. Therefore, we need to strictly require teachers to do this.

Similarly, with a minimum confidence level of 50%, the confidence level for a teacher evaluation of excellent and a rating of very satisfactory is 0.8, which is the same as the confidence level for a rating of more satisfactory when the teaching methodology is good, suggesting that part of the results of the evaluation ratings are derived from peer teacher evaluations and that the advice of the peer teachers is of indispensable value in improving the quality of teaching and learning.

5

Conclusion

This paper constructs a teaching quality assessment model for the teaching innovation team of higher vocational teachers using hierarchical analysis methods, and uses the model to evaluate the team.

Using the questionnaire analysis software SPSS to calculate the reliability of the student evaluation questionnaire and the teacher evaluation questionnaire, the ɑ coefficient of the total scale of the student evaluation questionnaire is 0.985, and the ɑ coefficient of the total scale of the teacher evaluation questionnaire is 0.988, and the reliability of the scales is 0.9 and above, which indicates that the scales have good reliability.

Using the technique of association rules, student evaluations and peer teacher evaluations in the model are associated with evaluation ratings respectively, and when the minimum confidence level is 50%, the confidence level for a student evaluation of excellent and a rating of very satisfactory is 0.8, and when the student evaluation is good, the confidence level is 0.6 regardless of whether the rating is very satisfactory or more satisfactory. The confidence level for a teacher evaluation of excellent and a rating assessment of very satisfactory is 0.8, which is the same as the confidence level for a rating assessment of more satisfactory when the teaching method is good.

The results indicate that a major key factor in improving the quality of teaching and learning by the Teaching Innovation Team is student-centeredness, as well as the indispensable value of advice from peer teachers.

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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Construction of Teaching Innovation Team Building System for Higher Vocational Teachers in the Context of Skill-based Society in Information Age

Zheng Ran

Online veröffentlicht: 24. März 2025

Eingereicht: 14. Nov. 2024

Akzeptiert: 18. Feb. 2025

DOI: https://doi.org/10.2478/amns-2025-0761

SchlüsselwörterTeaching innovation team, Teaching quality assessment model, Hierarchical analysis, Association rule

© 2025 Zheng Ran, published by Sciendo

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

Schlüsselwörter
Teaching innovation team, Teaching quality assessment model, Hierarchical analysis, Association rule