Development of Core Competencies for University Students during the Pandemic, Crisis of Public Health
Bibliographic record
Abstract
Future competency is a necessary condition for securing the competency of college students who lead the changed era. Therefore, this study was conducted to derive core competencies reflecting future competencies and to develop diagnostic tools. To this end, the reliability and validity of the draft questionnaire prepared after reviewing previous studies and receiving reviews from experts were secured. The survey was conducted for 983 college students from April 12 to 16, 2021, and the final 75 questionnaires were confirmed through statistical verification. Through the collected data, potential profiles with heterogeneous characteristics based on core competencies were classified into three, and the characteristics of each potential group profile were confirmed. The main analysis results are as follows. First, the five core competencies (humanities competency, communication competency, globalization competency, creativity competency, professionalism competency) consists of 15 sub-competencies and 75 questionnaires. Second, the improved K-University core competency scale has secured validity after verifying the improvement plan through the expert meetings and surveys. Third, based on the improved K-University Core Competency Diagnosis scale, the overall average of core competencies was 3.85, communication competencies 3.99, creativity competency 3.96, humanities competencies 3.85, professionalism competencies 3.85, and globalization competencies 3.58. Furthermore, a total of three analyzing the latent profile through the core competency diagnosis result, a total of three latent profiles (upper group, middle group, and lower group) were identified. Through the analysis results, a new core competency diagnostic scale was developed by reflecting the educational goals, vision, and future capabilities of the university. Through the results of this study, other higher education institutions will also be able to raise their interest in the future competencies of university students and provide competency-based curriculum to enhance the quality and effectiveness of education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".