Research on the Construction of the Evaluation System of Occupational Competence from Vocational Colleges in PRC-based on Big Data Technology
Bibliographic record
Abstract
This study constructs an Occupational Competency Assessment System (OCAS) for vocational colleges through a mixed-methods approach, combining quantitative analysis (Analytic Hierarchy Process, AHP) and qualitative techniques (literature analysis, expert interviews, and Delphi method). Quantitative results indicate that specialist knowledge (32.63%) and teamwork skills (13.23%) are the highest-weighted indicators, while practical skills (5.65%) and critical thinking (1.72%) show relatively lower contributions. Qualitative data collection involved four rounds of Delphi consultations with 30 participants (experts, students, graduates, and industry professionals), followed by grounded theory coding to identify 15 core competency categories. Findings reveal that the proposed system effectively integrates vocational methodological and social competencies, with a strong correlation (r > 0.7) among practical skills, teamwork, and workplace adaptability.However, limitations of traditional OCAS persist, including overreliance on subjective evaluations, fragmented data sources between schools and industries, and insufficient feedback mechanisms for personalized student development. To address these gaps, the study recommends strengthening practice-oriented teaching models, enhancing industry-education collaboration for real-time data integration, and incorporating dynamic AI-driven adjustments to improve assessment accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".