ELT Assessment Patterns Dictate Teaching-Learning Approaches: A Hindrance to Map out Employability and Life Skills
Bibliographic record
Abstract
There is a plethora of research on the multifarious dialogues on English Language as (EFL and ESL), its teaching-learning approaches, assessment patterns, the learners’ employ ability and their life skills. How all these aspects affect and influence one another, need further exploration. The most important and vital point is that English Language and Literature syllabus may be different in different colleges and universities but the assessment patterns are approximately the same. The alarming situation is that maximum questions are responded through cramming and rote learning where there is no reflection of creative skills and competency in English Language. However, exceptions are always there. The focus and significance of the present study is “how can the ELT approaches and assessment patterns be adapted and transformed specifically to meet the demand of the labor market, employability and life skills. (i) the researcher collected and analyzed 75 Question Papers of English from the Kingdom of Saudi Arabia, India, Bangladesh and Pakistan, and (ii) developed questionnaires cum opinionnaires for the 50 ELT teachers and the students in Jazan University, Jazan (KSA), and administered online. To determine findings and conclusion, the collected data have been analyzed in the employability, life, and soft skills perspectives that confirmed the validity and reliability of the present research hypothesis.
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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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".