The Role of Core Skills Development Through English Language Teaching (ELT) in Increasing Employability of Students in the Saudi Labor Market
Bibliographic record
Abstract
The National Commission for Academic Accreditation & Assessment (NCAAA), Saudi Arabia, aims to determine ‘standards and criteria for academic accreditation and assessment and for accrediting post-secondary institutions and the programs they offer’ (2012). Along this line of teaching, learning standards, each department is required to submit the Course Report (CR) and the Course Specifications (CS) reflecting the quality of learning and the management of courses aiming to achieve the highest international standards. As mandatory procedure, English Language Centers (ELC) in the Saudi universities also prepare quality assessment reports. The reports include data about the assessment of English language skills of speaking, listening, reading and writing skills. In addition, the CR and CS both seek to establish whether students at the Preparatory Year have mastered core skills such as communication skills, problem solving skills, thinking skills, language skills, attention, executive skills, memorizing and other cognitive and interpersonal skills. This qualitative study highlights the major findings the teachers’ perceptions about integrating core skills into English language teaching that would potentially increase employability of students in the labor market in Saudi Arabia and as well as contribute to the national vision 2030 that includes ‘learning for working’. Our study focuses on the relationships between teachers’ perceptions about core skills development during English language teaching and teachers’ decisions about using teaching activities to enhance those skills. Data were collected through questionnaire, semi-structured interviews and class observations and coded into different categories and labelled and then the results were drawn analyzing the connection between those categories.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".