Perceived Employability of Moroccan Engineering Students: a PLS-SEM Approach
Bibliographic record
Abstract
This study aims to build an employability model on the skills of young Moroccans, their perceptions of the competencies towards the labour market, and enhance the understanding of the employment landscape through an exploratory study based on the Conference Board of Canada (Employability Skills 2000+).Therefore, the competencies and skills under discussion are presented according to the Employability Skills 2000+ model; comprising Fundamental skills (FS), Personal Management Skills (PMS), and Teamwork skills (TWS).Accordingly, the approach used the Confirmatory Factor Analysis (CFA) and the Structural Equation Modeling (SEM) with SmartPLS software.The primary data was collected through a survey of Moroccan engineering students from the ENSAK (National School of Applied Sciences of Kenitra in English) belonging to the Ibn Tofail University of Kenitra.The survey participants included 411 students from six departments, relying on the non-probability and voluntary response sampling methodology.Finally, the results obtained revealed different perceptions regarding the priorities of certain skills in the labor market; where Personal Management Skills (PMS), Teamwork Skills (TWS), and Work Safely (WS) were perceived as highly demanded in the professional context with a medium effect on the model.Otherwise, the remains elements and features have a small effect and impact on the model, especially the fundamental skills and its sub-constructs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".