Addressing Mismatch between TVET Programs and Skill Needs in the Finance and Banking Sector – A Case Study from Qatar
Bibliographic record
Abstract
Links between Qatar’s labor market and post-secondary education are not fully effective and often result in neglected or duplicated development of human capital. Therefore, most development efforts occur in isolation involving outmoded programs with many complacent faculty unaware of new technologies and developments relevant to labor market sectors. Analyses of secondary data from government departments and international studies were combined with a survey on “Improving and enriching the Human Capital of the State of Qatar through Identification and Development of 21st Century Skills”. This explored perception of both employers and TVET program leaders toward the skills needed for economic and social development in a changing world by meeting human capital needs through 21st century skills. A total of 85 managers and professionals completed the survey, together with 35 TVET program leaders from one university and five government TVET institutions (the survey was adapted to fit the context of TVET institutions surveyed). Thirty-two of the industry managers and professionals were from Hydrocarbon and Energy, 26 from Built Environment and 27 from Banking Finance sectors. Subsequently, 32 semi-structured interviews were conducted. Descriptive statistics using T-test and effect size for comparison, showed a major mismatch between perceptions of TVET program leaders and business finance sector’ managers and professionals in many aspects of 21st century skills requirements. These were mainly in social skills and some specific technology skills. Significantly, the study indicated weak links between employers and TVET institutions. To address these issues, minimizing the skills’ mismatch can be achieved by placing greater emphasis on reforming the curricula of Qatar’s TVET institutions, to facilitate faster transitions into the workplace.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".