Research on the construction and application of an evaluation index system for vocational school teachers’ professional competency

This study focuses on the evaluation of vocational school teachers’ professional competencies. By addressing current limitations such as narrow evaluation dimensions and single-subject assessment, it integrates diverse theoretical frameworks and policies—including teacher efficacy theory—to construct an evaluation system. Through qualitative interviews and the Delphi method, the system comprises 7 primary indicators, 25 secondary indicators, and 89 tertiary indicators. These encompass: Professional awareness and ethics, Foundational competencies, Theoretical instruction, Practical teaching, Program development, Educational Research and Reform, and Professional development capabilities. A multi-subject collaborative evaluation model was established by integrating subjective and objective weighting methods using the Analytic Hierarchy Process (AHP) and entropy method, and its empirical application was conducted at a vocational school in Hubei Province. Research findings indicate significant disparities in teachers’ professional competencies, with higher scores in the dimension of professional ethics, while competencies in teaching research, practical teaching, and other areas remain relatively weak. Regression analysis reveals that factors such as years of teaching experience and corporate training programs exert differential impacts on competency development. Finally, we propose countermeasures such as tiered training, school-enterprise collaboration, and institutional optimization to provide practical references and support for evaluating vocational school teachers’ competencies and advancing their professional development.

1 Introduction

The status of vocational education within the national strategic framework continues to rise. As the primary platform for cultivating skilled professionals, the capacity building and evaluation of the teaching faculty in vocational schools have garnered increasing attention. Establishing a scientific and effective evaluation mechanism has become an intrinsic requirement for enhancing the quality of vocational education and promoting the professional development of teachers. However, many vocational schools currently face issues such as narrow evaluation dimensions, single-subject assessment, and arbitrary weighting in teacher evaluations, which hinder the diagnostic and guiding functions of the evaluation process. Therefore, establishing a comprehensive, systematic, and practical evaluation framework tailored to the unique characteristics of vocational education not only accurately reflects the current state of teachers’ competencies but also provides crucial guidance for their professional development and school management decisions.

This study aims to construct evaluation indicators through qualitative interviews and the Delphi method, combined with AHP and entropy weighting for composite weighting. Ultimately, it designs and applies a multi-stakeholder collaborative assessment system to provide empirical evidence and technical support for the scientific evaluation and continuous development of vocational school teachers’ professional competencies.

2 Literature review

Regarding the composition of teachers’ professional competencies, existing research indicates that these competencies fall within the realm of multidimensional, multilevel integrated abilities, encompassing multiple aspects such as teaching and education, scientific research and innovation, practical operations, and management. To be systematic and comprehensive, teachers’ professional competencies should encompass multiple dimensions including instructional design, character education and life guidance, mental health literacy education, logical thinking, empirical research, and philosophical reflection (Yang and Guan, 2012). From the perspective of modern educational technology, the professional competencies required of teachers in the information age encompass 23 aspects, including instructional planning proficiency, information processing skills, teaching research capabilities, and learning abilities (Qi and Gao, 2005). Teachers in the 21st century should possess professional competencies such as emotional stability, positive self-esteem, systematic personalized lesson plans, a collaborative approach, and flexible teaching methods (Kobalia and Garakanidze, 2010). Teachers’ professional competencies can also be categorized into general competencies and specialized competencies—that is, competencies applicable to every teacher and those specific to particular subject areas (Zulfizar, 2025). Based on modern teaching theories, teaching competencies should encompass seven dimensions, including teaching organization and implementation, selection of teaching methods, instructional design, and reflection (Li, 2023). In the field of vocational education, researchers have developed a competency model comprising five key elements: teaching fundamentals, professional practice, vocational skills, research capabilities, and comprehensive competencies (Zhan, 2015). These research perspectives collectively underscore the multifaceted and comprehensive nature of teachers’ professional competencies. From multifaceted, high-level requirements to specific competency demands across various domains, they reveal the complexity that teachers’ professional capabilities must embody within the contemporary societal context.

Regarding the factors influencing the professional development of teachers, numerous scholars have conducted research and investigations from three perspectives: individual factors, organizational factors, and institutional factors, yielding substantial results. Regarding individual factors, Personality traits influence teachers’ positive regulation of their own emotions and promote the development of cognitive abilities (Qu and Wang, 2024). Teachers’ attitudes toward their profession, continuous self-development, and job satisfaction influence the enhancement of their teaching competencies (Murwaningsih, 2024). In terms of organizational factors, school culture subtly shapes teachers’ professional identity and behavioral norms, fostering a positive environment for their professional growth. Resources and support provided by school administrators enhance teachers’ teaching confidence and innovative capabilities (Liu et al., 2023). The teaching autonomy granted to teachers by schools can, to a certain extent, shift the burden of work pressure onto them and constrain the development of their professional capabilities (Pearson and Moomaw, 2005). Conversely, if schools require teachers to shoulder multiple roles or responsibilities alongside their teaching duties—including administrative functions and external academic services—this overwhelming workload makes it difficult for them to plan the dimensions and direction of their professional development (Sengsoulintha, 2025). Participating in teaching competitions, research projects, and social service activities also serves as an important pathway for promoting the professional development of faculty members (Jiang and Zhu, 2016). At the institutional level, policy support can provide essential safeguards for teachers’ professional development through normative documents, resource allocation, and environmental shaping (Zhu et al., 2021). Regularly organizing training sessions for teachers on continuing education legislation, policies, and educational reforms can enhance their teaching practices and pedagogical skills (Tara, 2025). Individual, organizational, and institutional factors complement each other, creating synergistic effects that promote the professional development of teachers.

Regarding pathways for enhancing teachers’ professional competencies, a phased model based on competency accumulation divides the process into three stages: production, reproduction, and innovation. These correspond to five levels: cognitive, personal understanding, personal application, full mastery, and practical innovation (Wang, 2022). Additionally, it emphasizes the use of technology in the educational process and encourages teachers to create personalized learning pathways, such as modifying curriculum plans and teaching strategies to meet individual needs (Kalyani, 2024) suggests inviting highly skilled professionals from enterprises to participate in school teaching, as this integrates practical work experience with specialized knowledge. This initiative helps educators stay abreast of the latest industry developments and incorporate them into classroom instruction, thereby enhancing the teaching capabilities of highly skilled professionals (Zeng, 2011). It is essential to enhance teachers’ technological acceptance in education by cultivating their digital competencies (Antonietti et al., 2022). Finally, a diversified evaluation mechanism should be established and a practical platform built to enhance the vocational competency framework. This involves improving both the hardware and software infrastructure for vocational competency development and advancing the progression of vocational competency levels (Li and Dai, 2021).

Through an analysis of the current state of research on teacher professional competency evaluation internationally, we have made some discoveries. Firstly, the evaluation of teachers’ professional competence has shifted from a single-subject approach to a multi-subject collaborative model. Student evaluations of teaching serve as a crucial component, focusing on dimensions such as teaching methods, content, and interaction, and can effectively reflect the immediate impact of classroom instruction (Liu, 2020). Formative peer assessment provides an in-depth analysis of teaching strategies and subject content from a professional perspective, enabling a comparative evaluation of developing teachers (Zlabkova et al., 2024). Teachers’ self-assessment plays a crucial role in cultivating reflective thinking and self-awareness, and in promoting positive and transformative aspects throughout their careers (Masuwai et al., 2024). Subsequently, multi-agent collaboration optimized through weight allocation and guidance mechanisms can effectively overcome the limitations of a single perspective (Shauli Mukherjee et al., 2022). It is worth noting that using AI assessment can deepen our understanding of the competencies required of teachers and improve the quality of AI training in teacher education (Zhou et al., 2025). Secondly, self-efficacy significantly influences the enhancement of teachers’ professional competence. A case study of Jakarta’s Takana Tipri School revealed that teachers’ self-efficacy contributed 31.6% to their professional commitment, with those lacking self-efficacy exhibiting lower levels of professional commitment (Cahyaningrum et al., 2023). High teacher efficacy can boost student achievement by up to 20% and enhance overall educational quality by strengthening school cohesion (Zhou, 2019). In collectivist societies, enhancing teacher leadership through improved collective efficacy and teamwork fosters the development of teachers’ professional competencies (Luo et al., 2024). Thirdly, teachers’ digital literacy serves as a key indicator for evaluating their professional competence. The European Digital Competence Framework emphasizes that modern teachers must master skills such as information management, digital collaboration, and security awareness (Chrásková and Chráska, 2021). Incorporating digital tool practices into teacher training programs can significantly enhance teachers’ technology integration capabilities (García-Vandewalle García et al., 2023). It is noteworthy that peer influence and institutional support are crucial for the development of digital literacy. For instance, educational institutions can significantly enhance students’ digital self-efficacy by providing technological resources and fostering collaborative learning environments, which also applies to the teaching community (Sari et al., 2024). Teachers must enhance their digital expertise in applied fields and cultivate multifaceted skills to effectively utilize artificial intelligence within educational settings (Ghodrati et al., 2025) argues that leveraging generative AI in the era of artificial intelligence to can equip teachers with future-oriented professional development skills and enhance their higher-order thinking abilities (Lu et al., 2024). Fourthly, the ability to engage in lifelong learning is fundamental to enhancing teachers’ professional development capabilities. The lifelong learning tendencies formed during early education can positively predict career development aspirations (Lasser and Fite, 2011). Turkey’s National Lifelong Learning Strategy enhances teachers’ information literacy through systematic training programs, effectively promoting professional growth by integrating technical standards with self-efficacy (Arslan, 2019). Transforming schools into learning communities provides teachers with ongoing learning opportunities and continuously expands their professional capabilities (Admiraal et al., 2021).

3 Research design

This paper integrates teacher efficacy theory, professional competence theory, and lifelong learning theory with diverse academic and policy frameworks such as dual-qualified teachers and industry-education integration. It distills seven primary indicators—professional awareness and ethics, professional foundation, theoretical instruction, practical teaching, program development, teaching research and reform, and professional development capabilities. Specific observation points embody holistic requirements for teachers’ theoretical literacy, practical skills, and growth motivation. This framework highlights the dual characteristics of “practice + theory” within vocational education while laying the groundwork for systematic management of subsequent evaluation implementation and improvement pathways.

3.1 The process of constructing an evaluation indicator system

This study divides the construction of professional competency evaluation standards for vocational school teachers into two phases. The first phase employs qualitative research methods to analyze teachers’ professional development capabilities and constructs an evaluation indicator system based on interview data. Through semi-structured interviews, we delve into core areas such as professional awareness, teaching competencies, and career development to gain a deep understanding of the key elements of teacher capability, thereby establishing a scientifically sound and reasonable assessment indicator framework. The second phase employs the Delphi method, beginning with establishing the research topic and clarifying the objectives and scope of teacher professional competency evaluation. This was followed by assembling a specialized and diverse expert team for developing evaluation indicators. Subsequently, an expert questionnaire was meticulously designed to encompass the core elements of professional competency. Two rounds of expert opinion collection and feedback were conducted to ensure thorough exchange and scientific integration of all perspectives. Finally, statistical analysis of the results was performed to construct the evaluation framework and structural dimensions.

3.1.1 Interview design

The study interviewed 15 teachers and educational administrators from diverse disciplines to explore the core components of vocational school teachers’ professional competency development. To ensure diversity of perspectives, the study included faculty members from multiple disciplines, encompassing different academic ranks and administrative roles. Preliminary evaluation indicators derived from interview analysis (Table 1).

Table 1

Table 1. Interviewees.

The interview questions include the following 10 core inquiries: (1) How do you understand the professional role and responsibilities of a vocational school teacher? (2) In your educational work, what motivates you to maintain enthusiasm and commitment to your profession? (3) How do you ensure clear and easily understandable language expression in your teaching? (4) Have you utilized digital technology to support teaching? Can you provide examples of its effectiveness? (5) When developing lesson plans, how do you analyze student needs and set instructional objectives? (6) How do you handle unexpected situations in class and flexibly adjust teaching strategies? (7) When organizing practical teaching projects, how do you ensure they align with real-world demands and help students enhance their skills? (8) What has been your greatest challenge in practical teaching, and how did you overcome it? (9) Have you established personal career development goals? How do you plan to achieve them? (10) Which avenues—such as training, workshops, or self-directed learning—do you prefer for enhancing your professional capabilities?

3.1.2 Data processing

Conceptual refinement was conducted using manual coding and the NVivo 12.0 Plus analysis tool. The coding process comprised three stages: open coding, axial coding, and selective coding.

First, during the open coding phase, the research progressively explores and uncovers various situations and concepts within the textual materials. Through the organization and analysis of 15 interview transcripts, 98 open codes were ultimately generated. The table below provides open coding examples (Table 2).

Table 2

Table 2. Open coding examples.

Second, during the main axial coding phase, use the identified concept categories as the core for coding, compare them with other themes, and delve deeper into the relationships among various concepts. Further categorization of open codes is required to construct higher-level themes. For instance, regarding the “Professional Awareness and Ethics” coding, “Professional Ideals” and “Professional Mission” would be grouped under the theme “Professional Ideals and Moral Cultivation”; while “Dedication to Work” and “Spirit of Devotion” would be categorized under the theme “Professional Ethos.” The table below provides axial encoding examples (Table 3).

Table 3

Table 3. Axial encoding examples.

Thirdly, during the selective coding phase, core categories are extracted from the categorized categories, and connections are established between core and non-core categories. Through a three-tier coding analysis and synthesis, core concepts were identified and distilled, then consolidated into seven major categories: professional awareness and ethics, foundational competencies, theoretical instruction, practical training, program development, teaching research and reform, and career development capabilities.

3.1.3 Questionnaire survey and expert panel selection

The “Expert Consultation Questionnaire on Vocational School Teacher Professional Competency Evaluation Standards” used in this study was developed based on the “three-tier theory” of professional competency, drawing on international scholars’ classifications of professional competency domains, and incorporating insights from informal interviews with selected teachers. This expert consultation questionnaire comprises four sections: Introduction, Expert Basic Information, Questionnaire Survey, and Questionnaire Feedback. It employs a survey format combining subjective questions with open-ended objective questions. Using a non-probability “judgmental sampling” approach, 16 consulting experts were invited from comprehensive universities, teacher education organizations and institutions, and secondary vocational schools based on their representativeness, diversity, and expertise (Table 4).

Table 4

Table 4. Delphi method expert sources summary.

The authority coefficient (Cr) is determined through expert self-assessment, primarily based on the expert’s judgment basis (Ca) for evaluating the proposal and the expert’s familiarity with the issue (Cs). The expert’s familiarity with the issue is quantified as follows: highly familiar = 1, familiar = 0.8, generally familiar = 0.5, not very familiar = 0.2, and not familiar at all = 0. The specific calculation method for Cr is given by Equation 1.

$\begin{array}{ll}{\mathrm{C}}_{\mathrm{r}}=\frac{{\mathrm{C}}_{\mathrm{a}}+{\mathrm{C}}_{\mathrm{s}}}{2}& (1)\end{array}$

The calculation results indicate that the expert authority coefficient Cr reaches 0.7 or higher, signifying that the research findings are reliable (Table 5).

Table 5

Table 5. Expert authority level.

3.1.4 Two-round revision using the Delphi method

The study designed an “Assessment Index System for Vocational School Teachers’ Professional Development Capabilities,” incorporating Likert scales in its key sections. Experts were invited to rate the appropriateness and importance of primary, secondary, and tertiary indicators. Each indicator also included open-ended questions to facilitate specific revision suggestions from the experts.

(1) Revision process for the first-round survey indicators of the Delphi method

The data from 16 valid questionnaires were entered into SPSS 26.0 software to examine reliability, thereby assessing the internal consistency, reliability, and stability of the measurement results. Cronbach’s alpha coefficient is employed as an indicator for measuring the internal consistency of the questionnaire. The results indicate that the reliability coefficients for all indicators reached or exceeded 0.7, meeting general reliability standards and satisfying the research requirements. The following table presents the specific reliability test results (Table 6).

Table 6

Table 6. Reliability analysis results of the survey questionnaire (N = 16).

Data analysis was performed using SPSS software, primarily examining metrics such as mean, median, coefficient of variation, interquartile range (IQR), and consensus rate. The magnitude of the coefficient of variation reflects the degree of agreement among experts; a smaller coefficient indicates higher agreement. When the coefficient of variation is below 0.25, it generally indicates that experts have reached a high level of consensus. This study employed the interquartile range (IQR) as the criterion for assessing consistency in the Delphi method research, while adopting 80% as the threshold for consensus. If more than 80% of surveyed experts rated an indicator as 4 (important) or 5 (very important), that indicator was considered to have achieved consensus; otherwise, it would be considered for modification or deletion. The results of organizing and analyzing the expert feedback from the first round of the Delphi method are presented in the table below (Table 7).

Table 7

Table 7. Statistical results of first-round Delphi expert feedback (N = 16) with A = professional awareness and ethics, B = professional foundation, C = theoretical instruction, D = practical teaching, E = program development, F = teaching research and reform, G = professional development capabilities.

The research findings indicate that the median values and mean scores for all seven primary indicators (A–G) and 25 secondary indicators (A2–G2) exceed 4, with coefficient of variation values consistently below 0.25. This demonstrates that experts have reached a strong consensus regarding the secondary indicators. Among the 89 tertiary indicators (A1.1.1–G1.2.5), A1.3.1 achieved a consensus rate of 75%, failing to meet the standard; C1.2.3 recorded an IQR of 2 with a consensus rate of 70%, falling below the threshold. C1.3.1, C1.3.2, and D1.4.2 all have a consensus rate of 80%, which is at the threshold and requires a second round of revisions; E1.1.1 has a mean below 4 and a consensus rate of 68%, while F1.2.1 has an IQR of 1.5 and a consensus rate of 75%, both falling below the standard threshold.

Based on the measurement results, certain indicators need to be adjusted, reduced, and optimized. The term “professional competence” in A1.3.1 should be revised to “professional spirit.” Under C1.3 “Teaching Evaluation and Improvement Capabilities,” add three tertiary indicators “diagnostic evaluation,” “formative evaluation,” and “summative evaluation”—to emphasize the holistic nature of teacher assessment. D1.4.2 “Practical Teaching Guidance” has been supplemented with specific content, namely “Practical Teaching Guidance,” primarily covering three third tertiary indicators: “Practical Teaching and Various Competitions,” “Practical Teaching and Social Activities,” and “Practical Teaching and Internships/Employment.” E1.1.1 expands the “Program Positioning” indicator into “Program Positioning” and “Relationship Positioning”; F1.2.1 is adjusted to a secondary indicator and refined into “Teaching Reform” and “Teaching Achievement Awards”; G1.2 incorporates two tertiary indicators: “Disciplinary Frontiers” and “Industrial Frontiers.”

(2) Revision process for the second round of survey indicators in the Delphi method

Based on the results of the first round of revisions, the indicator system was recompiled, and a second round of expert consultation was conducted. The questionnaire content incorporates feedback from the initial survey and revised indicator details. This consultation process employs a Likert scale to evaluate newly added and modified indicators. The purpose of this consultation is to gather expert opinions and suggestions regarding the revised indicator system, thereby further refining it to enhance its scientific rigor and rationality. The questionnaire results continue to rely primarily on median, standard deviation, mean, coefficient of variation, interquartile range (IQR), and consensus level as key reference metrics. The following table shows the specific statistical results (Table 8).

Table 8

Table 8. Statistical results of second-round Delphi expert feedback (N = 16).

For the 14 newly revised secondary indicators, expert consultation results indicate that the median exceeds 4, the coefficient of variation ranges between 0.07 and 0.18 (below 0.25), the interquartile range falls between 0 and 1, and consensus exceeds 80%. This demonstrates that expert panel members have reached a high level of agreement on the revised indicators.

The indicator system has been successfully established after following two rounds of expert consultation using the Delphi method and the application of weighting techniques such as composite weighting to determine the weights of each indicator. This system comprises 7 primary indicators, 25 secondary indicators, and 89 tertiary indicators.

3.2 Weight determination and calculation process for vocational school teacher professional competency evaluation indicators

First, the structure and content of the evaluation indicator system were determined through two rounds of Delphi expert consultations. Second, 16 experts were invited to construct an AHP judgment matrix and calculate subjective weights. Third, basic data from 132 teachers at Vocational School D in Hubei Province, China, were collected, and objective weights were calculated using the entropy method. Finally, the geometric mean method was employed to combine subjective and objective weights, yielding the final composite weights. It should be noted that, in accordance with the principles of a multistage study design, the subsequent two distinct sample sizes (117 and 154) were both drawn from the same vocational school to maintain population consistency. These sizes were strategically calibrated based on the unique methodological requirements, data quality standards, and feasibility constraints of each analytical stage.

3.2.1 Application of the analytic hierarchy process (AHP)

Firstly, a hierarchical model is established, which comprises three levels: the objective level is “Professional Competency Evaluation for Career Teachers,” the criterion level includes seven primary indicators, and the scheme level encompasses 25 secondary indicators and 89 tertiary indicators. Using a judgment matrix, experts’ qualitative assessments are converted into quantitative data through pairwise comparisons. To conduct pairwise comparisons of the same-level indicators among 16 experts, a 1–9 scale is used to represent importance levels, thereby constructing a judgment matrix. 1 indicates equal importance between two indicators, 3 indicates one indicator is slightly more important than the other, 5 indicates significantly more important, 7 indicates strongly more important, and 9 indicates extremely more important. Values 2, 4, 6, and 8 represent intermediate judgments between adjacent categories. For example, using “practical teaching ability” as a primary indicator, it encompasses four secondary indicators, thereby forming a hierarchical observation system from macro-level dimensions to micro-level behaviors. Then construct an expert judgment matrix and perform data normalization. For indicators at each level, organize 16 domain experts to participate in two rounds of feedback-based questionnaire surveys, employing a 1–9 importance scale to conduct pairwise importance comparisons among indicators at the same level. Based on the results of the two-round expert consultation questionnaire survey, a pairwise judgment matrix was formed after experts scored the importance of each indicator. Below is the first-level indicator judgment matrix for Expert 1.

$A_{\mathit{\text{Expert}}1}=\left[\begin{array}{cccccccc}A& B_1& B_2& B_3& B_4& B_5& B_6& B_7\\ B_1& 1& 4& 7& 5& 2& 3& 1/6\\ B_2& 1/4& 1& 6& 2& 1/3& 1/2& 1/7\\ B_3& 1/7& 1/6& 1& 1/2& 1/5& 1/4& 1/7\\ B_4& 1/5& 1/2& 2& 1& 1/5& 1/3& 1/7\\ B_5& 1/2& 3& 5& 5& 1& 3& 1/4\\ B_6& 1/3& 2& 4& 3& 1/3& 1& 1/7\\ B_7& 6& 7& 7& 7& 4& 7& 1\end{array}\right]$

Secondly, weight calculation and consistency verification. By solving for the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, the weights for each indicator are obtained. Subsequently, the eigenvector method is used to calculate the weights for indicators at each level. Solve for the maximum eigenvalue ${\lambda }_{\max }$ and its corresponding normalized eigenvector based on the judgment matrix; this vector constitutes the set of indicator weights. To ensure the consistency of judgment logic, consistency verification is conducted concurrently by calculating the consistency ratio CR = CI/RI, where $\mathit{CI}=\frac{{\lambda }_{\max }-n}{n-1}$ and RI denotes the average random consistency index. Through calculation, the maximum eigenvalue of the matrix is determined to be 7.616. To ensure consistency, a consistency test is required. The consistency index CI is calculated using Equation 2.

$\begin{array}{ll}\mathrm{CI}=\frac{{\lambda }_{\max }-\mathrm{n}}{\mathrm{n}-1}=\frac{7.616-7}{7-1}=0.103& (2)\end{array}$

The average random consistency index (RI) is 1.341, and the random consistency ratio (CR) is given by Equation 3:

$\begin{array}{ll}\mathrm{CR}=\frac{\mathrm{CI}}{\mathrm{RI}}=\frac{0.103}{1.36}=0.076<0.10& (3)\end{array}$

Since CR is less than 0.1, the judgment matrix is deemed reasonably constructed. After constructing the judgment matrix based on the scores assigned by 16 experts to the primary indicators, matrix aggregation is performed. Using the geometric mean method, the scoring matrices formed by m experts (m = 1,2, $\dots$k) are multiplied row-by-row. Then, each row of matrix A is raised to the nth power to obtain the unique integrated matrix as follows:

$\begin{array}{c}{\overline{\mathrm{A}}}_{\mathit{\text{Aggregated}}}=\left[\begin{array}{cccccccc}A& B_1& B_2& B_3& B_4& B_5& B_6& B_7\\ B_1& 1& 1/5& 1/3& 1/7& 1/3& 1/6& 1\\ B_2& 5& 1& 3& 2+1/2& 2+1/3& 2+1/4& 2+1/3\\ B_3& 3& 1/3& 1& 4& 4+2/9& 5+3/7& 5+1/9\\ B_4& 7& 2/5& 1/4& 1& 4+1/5& 3+1/6& 4+4/7\\ B_5& 3& 3/7& 9/38& 5/21& 1& 1+1/3& 2/9\\ B_6& 6& 4/9& 7/36& 6/19& 3/4& 1& 1/6\\ B_7& 1& 3/7& 9/46& 7/32& 9/2& 6& 1\end{array}\right]\end{array}$

By organizing the weighting of primary indicators for the professional competency assessment of vocational school teachers, the statistical results in the table below were obtained (Table 9).

Table 9

Table 9. Statistical analysis of weighted primary indicators for occupational competency assessment.

Thirdly, hierarchical synthesis of subjective weights. Adopting a “stepwise weighting and composite ranking” strategy, the weights at each level are integrated through multiplication operations to ultimately determine the final weight.

3.2.2 Entropy-based weighting method

First, construct the primary evaluation matrix. This study collected evaluation data from 117 teachers at Vocational School D in Hubei Province, China, across 89 tertiary indicators. To ensure data authenticity and reliability, stringent quality control measures were implemented. Let the sample data from the expert questionnaire be denoted as m, and let the number of experts in each evaluation data sample be denoted as n, thereby forming the original evaluation matrix: $\mathrm{X}={\left({\mathrm{X}}_{\mathrm{ij}}\right)}_{\mathrm{nm}}=(\mathrm{i}=1,2,\dots \dots \mathrm{m};\mathrm{j}=1,2,\dots \mathrm{n})$

Second, data standardization processing. The raw data undergoes normalization using the extreme value method, followed by dimensionless processing to map all indicator values to the interval [0, 1]. The calculation formula is given by Equation 4.

$\begin{array}{ll}{\mathrm{Y}}_{\mathrm{ij}}=\frac{{\mathrm{X}}_{\mathrm{ij}}-{\left({\mathrm{X}}_{\mathrm{ij}}\right)}_{\min }}{{\left({\mathrm{X}}_{\mathrm{j}}\right)}_{\max }-{\left({\mathrm{X}}_{\mathrm{j}}\right)}_{\min }}& (4)\end{array}$

In Equation 4, $\mathrm{i}$ denotes the expert index and $\mathrm{j}$ the criterion index; ${\mathrm{Y}}_{\mathrm{ij}}$ is the normalized value assigned by expert $\mathrm{i}$ to criterion $\mathrm{j}$ , where as ${\mathrm{X}}_{\mathrm{ij}}$ denotes the raw value assigned by expert $\mathrm{i}$ to criterion $\mathrm{j}$ in the original dataset; ${\left({\mathrm{X}}_{\mathrm{ij}}\right)}_{\min }$ and ${\left({\mathrm{X}}_{\mathrm{j}}\right)}_{\max }$ represent, respectively, the minimum and maximum observed values of criterion $\mathrm{j}$ across all experts. ${\left({\mathrm{X}}_{\mathrm{j}}\right)}_{\min }$ denotes the minimum value observed for the $\mathrm{j}$ evaluation indicator.

Third, Calculation of Indicator Information Entropy and Weights. we need to compute the share of every evaluated object under each indicator. Let $P_{\mathit{ij}}$ be the share of object $i$ under indicator $j$ , calculated according to Equation 5.

$\begin{array}{ll}{\mathrm{P}}_{\mathrm{ij}}=\frac{{\mathrm{Y}}_{\mathrm{ij}}}{\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{Y}}_{\mathrm{ij}}}& (5)\end{array}$

Subsequently, the entropy value for each indicator is computed using Equation 6.

$\begin{array}{ll}{\mathrm{e}}_{\mathrm{j}}=\frac{1}{\mathrm{lnn}}(\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{P}}_{\mathrm{ij}}ln{\mathrm{P}}_{\mathrm{ij}}),0\le {\mathrm{e}}_{\mathrm{j}}\le 1& (6)\end{array}$

If any ${\mathrm{P}}_{\mathrm{ij}}$  = 0, replace it with ${\mathrm{P}}_{\mathrm{ij}}$  + 1 before taking the logarithm.

Finally, the entropy weight of each indicator is obtained via Equation 7.

$\begin{array}{ll}{\mathrm{W}}_{\mathrm{j}}=\frac{1-{\mathrm{e}}_{\mathrm{j}}}{\mathrm{m}-\sum _{\mathrm{j}=1}^{\mathrm{m}}{\mathrm{e}}_{\mathrm{j}}}& (7)\end{array}$

Through the above calculations, entropy-based weights were obtained for 89 tertiary indicators. These weights were then propagated upward through the hierarchical structure to derive entropy-based weights for 25 secondary indicators and 7 primary indicators.

3.2.3 Combination weighting method

Combination weighting can simultaneously consider expert judgment and actual data distribution, balancing subjectivity and objectivity to provide a more comprehensive basis for determining weights. The study employs the geometric mean method, commonly used in composite weighting approaches, to combine the subjective weights derived from the AHP method with the objective weights obtained from the entropy method. The calculation formula is given by Equation 8.

$\begin{array}{ll}\overline{\mathrm{W}}=\frac{\sqrt{{\mathrm{W}}_{\mathrm{i}}{\mathrm{W}}_{\mathrm{j}}}}{\sum _{\mathrm{j}=1}^{\mathrm{n}}\sqrt{{\mathrm{W}}_{\mathrm{i}}{\mathrm{W}}_{\mathrm{j}}}}& (8)\end{array}$

$W_i$ represents the weight derived from the Analytic Hierarchy Process (AHP), $W_j$ denotes the weight obtained via the entropy weight method, and $\overline{W}$ is the resulting composite weight after adjustment. The final combined weight for the vocational school teacher professional competency evaluation index system is determined using the geometric mean approach.

4 Application of the vocational school teacher professional competency evaluation index system

4.1 Weight distribution for evaluation criteria of vocational school teachers’ professional competency

This study employs a multi-dimensional evaluation model to integrate multi-perspective data from teacher self-assessment, peer evaluation, student feedback, and leadership appraisal. By utilizing the AHP-entropy value combination weighting method, it quantifies expert experience through the construction of a judgment matrix. Combined with information entropy theory to analyze the objective importance of indicators, this approach ultimately achieves a dynamic equilibrium between subjective and objective weighting. After consistency verification, entropy-based adjustment, and final composite weighting, the evaluation weights were ultimately determined (Table 10)

Table 10

Table 10. Weight distribution of evaluation subjects for vocational school teachers’ professional competency assessment.

4.1.1 Business process design for the evaluation of vocational school teachers’ professional competency

After establishing the organizational structure of the evaluation system, an analysis of its business processes was conducted, resulting in the specific workflow described below. As shown in the diagram, administrators log in to input role-specific information. Based on the requirements of different roles, they select questions to compose questionnaires and click to publish them. Each role receives the questionnaire they need to complete. Upon submission, the results are returned to the administrator, generating an assessment report (Figure 1)

Figure 1

Figure 1. Business process diagram.

4.2 Application of the vocational school teacher professional competency assessment system

Focusing on the professional competency data of 154 teachers at a vocational school in Hubei Province, China, a comprehensive analysis is conducted. Employing a multi-dimensional evaluation model to quantitatively analyze teachers’ professional competency, this study uses SPSS 26.0 to conduct descriptive statistics, t-tests, analysis of variance (ANOVA), and principal component factor analysis. Through multidimensional statistical methods, it reveals the current state of teachers’ competency structure, its internal relationships, and pathways for optimization. The total scores for teachers’ professional competencies and the distribution of scores across each dimension are shown in the table below (Table 11)

Table 11

Table 11. Distribution of teachers’ overall professional competency scores and scores across dimensions (maximum score: 100).

The average score for teachers’ professional competency was 73.68 points, indicating an overall moderate level. The standard deviation of 7.45 points suggests that the degree of variation within the group is relatively concentrated. However, significant disparities exist across dimensions: The Professional Awareness and Teacher Ethics dimension scored as high as 88.42 points, with 85.1% of teachers falling into the high-level category. The minimum score remained at 69 points, far exceeding the lowest scores in other dimensions. This outcome reflects that the education system has effectively established unified norms through institutionalized professional ethics standards, but it may obscure the deep-seated difference between passive compliance and the internalization of values. In comparison, the mean difference between theoretical and practical instruction was 5.85 points, indicating a pronounced divergence: the former scored 76.18 points while the latter scored only 70.33 points. The higher stability reflected by the former’s SD = 9.23 suggests a greater degree of standardization in traditional classroom lectures, whereas the latter’s SD = 11.82, with its lower mean and greater dispersion, implies that skill-based practical training is constrained by resource limitations.

The weak correlation between professional awareness and teaching-research activities reflects deep-seated contradictions in institutional design—the high-intensity requirements of teacher ethics codes crowd out teachers’ research time, creating tension between professional ethics and creative freedom that further stifles research momentum. The regression analysis of factors influencing professional competence is shown in the table below (Table 12)

Table 12

Table 12. Regression analysis of factors influencing professional competence (7-dimensional correlation).

The regression model indicates that the positive effect of teaching experience exhibits significant selectivity and limitations. The beta coefficient for years of teaching experience was 0.32 (p < 0.01), covering five dimensions including professional cognition and teaching competence. However, it did not significantly contribute to career development. This indicates that accumulated experience enhances normative capabilities but fails to foster breakthrough capabilities. Taking practical teaching ability as an example, each additional year of teaching experience increases the score by 0.82 points (p < 0.001). However, a clear ceiling effect emerges: the increase drops sharply by 30% after 15 years of teaching experience. Although corporate training programs demonstrated statistical significance in weaker dimensions—such as professional development (β = 0.18) and teaching research (β = 0.12)—the funnel effect in resource coverage resulted in insufficient actual benefits: participating teachers averaged 7.2 training days annually, yet only 24% reached the 15-day effectiveness threshold. Moreover, training content emphasized procedural observation over practical intervention. The distribution of competency gaps among teachers of different ranks is shown in the table below (Table 13)

Table 13

Table 13. Distribution of competency gaps among teachers of different titles.

The professional development capacity of entry-level teachers showed a non-compliance rate of 78.3% (M = 58.7), with their competency gap directly linked to passive role positioning: only 8% of entry-level teachers were included in professional development committees, and their participation rate in core curriculum design fell below 15%. This resulted in their competency cultivation lacking practical scenario support. The average score for intermediate-level teachers was 60.2, with a non-compliance rate of 63.1%. Their teaching and research challenges reveal a disconnect between evaluation and support: promotion standards require an increase in annual publications from 0.2 to 0.8 articles, yet research guidance coverage remains at only 34%. Consequently, 68% of research outputs are published in non-core journals, and the achievement conversion rate falls below 5%. The average professional development competency score for senior teachers stands at 65.8, seemingly meeting standards. However, significant disparities exist within this group: teachers on secondment to enterprises and engaged in industry-sponsored projects achieved an average score of 75.3, while those in administrative roles detached from frontline industry practice scored only 55.7. This reflects rigid career pathways and monopolization of resources.

5 Research findings and discussion recommendations

5.1 Research findings

First, Research has revealed that the professional competency of teachers at this school exhibits a pronounced “polarization” pattern. Professional awareness and ethical competence ranked first with a significant lead of 88.42 points, with 85.1% of teachers achieving high-level proficiency. In contrast, dimensions such as teaching research capability (62.15 points), professional development capability (59.82 points), and practical teaching capability (70.33 points) lagged significantly behind. This disparity in competency fundamentally stems from imbalances in institutional design. In contrast, the weakness in teaching research and practical teaching capabilities reflects structural deficiencies in resource allocation and incentive mechanisms. Teaching research capabilities not only scored low on average but also exhibited significant dispersion, with a standard deviation of 13.56, indicating severe disparities in capability development among faculty members.

Second, regression analysis of factors influencing professional competence reveals multidimensional paradoxes within the pathways of teacher competency development. Although teaching experience generally exerts a significant positive influence on professional competence, this gain exhibits a pronounced trend of phased decline. Teachers in the early stages of their careers can continuously enhance their cognitive abilities through accumulated experience. However, as their years of teaching increase, the pace of skill development gradually slows, and even a phenomenon of skill stagnation may emerge. This trend reflects a decline in adaptability among long-term educators regarding technological updates and pedagogical innovations, leading to a path dependency where experience replaces renewal. This limits the continuous optimization of their skill structure.

Third, there is a significant disconnect between the current teacher evaluation system and the actual needs for professional development. Novice teachers are generally marginalized within institutional frameworks, possessing extremely limited influence in professional development. Their involvement primarily consists of administrative tasks, lacking genuine opportunities to lead projects or engage in substantive construction. This perpetuates a cycle of “low participation—low advancement” in their professional capabilities. Mid-career faculty at critical junctures in their professional development receive growth support resources that fall far short of their competency requirements. While research workloads have increased significantly, the absence of robust mentoring systems and quality assurance mechanisms has led to outputs predominantly focused on non-core, low-utility research types, hindering the advancement of knowledge production. Senior teachers, despite holding a dominant position in resource allocation, have seen their functional roles increasingly bureaucratized. Their daily work is dominated by a heavy load of procedural tasks, severely limiting their capacity for innovation. Overall, the existing professional title system places greater emphasis on evaluating static, quantifiable achievements while neglecting the dynamic assessment of core competencies such as teachers’ comprehensive abilities, practical innovation, and cross-disciplinary integration.

5.2 Discussion recommendations

First of all, it is necessary to reconstruct the institutional framework from two aspects: teacher ethics evaluation and school-enterprise cooperation, to promote the coordinated development of professional competence and ethical standards. By integrating corporate safety production regulations into instructional design and inviting industry experts to jointly certify industry-education integration projects, the intrinsic nature of professional ethics can be strengthened. Second, within the tiered development pathway, novice teachers must engage deeply in corporate production practices. For instance, they should participate in at least one corporate technical task per semester, with outcomes jointly evaluated by corporate mentors and directly impacting professional title advancement. For senior teachers, a technical certification elimination mechanism should be established, while simultaneously mandating their responsibility to pass on skills. Finally, society, schools, and individuals should collaborate to enhance teachers’ professional capabilities. At the societal level, policy deregulation is needed to reduce compliance burdens, such as decreasing the frequency of teacher ethics inspections while increasing the weight of joint evaluations by enterprises and schools. At the institutional level, breaking resource monopolies and path dependencies is essential. This includes redesigning practical training curricula based on technology update cycles and requiring senior faculty to complete at least one new technology certification every two years. At the individual level, value awakening should be driven by technological feedback loops. For instance, after participating in corporate technology projects, faculty should convert case studies into teaching resources for campus-wide sharing.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

MZ: Writing – original draft. LW: Writing – review & editing. CZ: Writing – original draft. TS: Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the National Education Science Planning Project for the research project “Optimizing the Institutional Environment for the Professional Development of Dual-Qualified Teachers in Higher Vocational Colleges (DJA23035).”

Acknowledgments

We are deeply grateful to Dongxihu Vocational School in Hubei Province for granting us permission to conduct interviews and distribute questionnaires to hundreds of teachers and students on campus, and for providing us with interview accommodations.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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