Evaluating Employability Skills Integration in a Citizenship Education Textbook
Bibliographic record
Abstract
The International Labour Organization (ILO) generated a strategic plan for skills and lifelong learning from 2022 to 2030, aiming to improve students’ access to future jobs. One of the target goals of the ILO's plan was to ensure that educational programs meet the changing needs of labour markets. Therefore, there is a rising need to assess whether teaching programmes can prepare learners for the requirements of future jobs. In this study, we investigate how employability skills are integrated in teaching resources. For this purpose, 54 learning activities from a Grade 6 Citizenship Education textbook in Bahrain were analysed using 56 predefined codes elaborated by the Conference Board of Canada in a document that was developed by the Corporate Council on Education, a program of the National Business and Education Centre since 2000. This study employs quantitative analysis, including Pearson correlation coefficients and regression analysis to evaluate the integration of employability skills in the learning activities using three major factors: Fundamental Skills (FS), Personal Management Skills (PMS) and Teamwork Skills (TS). The results show that Fundamental Skills (Mean = 3.87, Standard Deviation = 1.26) are well integrated, whereas Personal Management Skills (Mean= 3.17, Standard Deviation = 1.54) demonstrate moderate integration with notable variations. In contrast, Teamwork Skills display a low level of integration (Mean = 1.02 Standard Deviation = 1.88). The correlation analysis confirms a relationship (r = 0.615, p < 0.01) between FS and PMS but shows insignificant correlations between TS and the other skill sets. These findings highlight a critical gap in teamwork skill integration and suggest a need for increased focus on teamwork in educational materials, while recommending further research to focus on expanding the sample size, incorporating more qualitative analyses, and exploring effective strategies for integrating employability skills into educational resources.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".