Mapping SDGs’ 4 and 8 through enhancing technological skills for students’ employability and establishing a software professional employability skills development program
Bibliographic record
Abstract
In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancemenThe purpose of this study is to: (1) evaluate the technological skills of the final-year undergraduate students, (2) how such abilities influence their likelihood of getting employed and (3) student opinions on whether a computer lab-based specialization in software training can boost employability. This study is a survey-based methodology. The sample size encompasses 140 final year students in the College of Business Administration at Prince Sattam bin Abdulaziz University during the academic year 2023-2024. Descriptive statistical analyses indicate that most students believe they have sufficient technical skills but not enough for securing better jobs. Moreover, it is clear among them that expertise in specialized software packages enhances career prospects significantly. The research results also show a huge gap between technological competencies learners have now and what employers demand currently. In response, this study suggests that PSAU should establish software laboratories in their colleges for specialized training on software as required by the job market and workplace. The Vice Rectorate for Academic and Educational Affairs launches a program called “Graduate and Professional Skills Development Program (PSAU-GPSDP)”, which emphasizes student employability as it develops, implements, and evaluates mechanisms to enhance students’ chances of getting jobs upon graduation. The results of this study are in line with SDG No. 8 (Decent Work and Economic Growth), SDG No. 4 (Quality Education), and Saudi Vision 2030. Therefore, this study has practical implications for decision-makers at the Ministry of Education and university levels, university professors, researchers on how employability skills of students could be enhanced in the higher education institutions.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".