EVALUATING LANGUAGE SKILLS IN HIGHER EDUCATION: POTENTIAL ISSUES AND OUTCOMES
Bibliographic record
Abstract
Numerous accounts have been provided regarding the shortcomings of assessments offered during English courses in Pakistan at the tertiary level. In the literature however, the factors underlying these shortcomings have not been identified. Understanding the unsatisfactory assessment practices and their reasons and consequences which this present work intends to do, is crucial. This investigation was qualitative in nature and sought to understand the issues surrounding language assessments and their impact. Using purposive sampling, thirty teachers from public and private universities formed the sample. Data was gathered by means of questionnaires, interviews, and a review of assessment papers. The results suggest that language teachers do face a number of assessment challenges. The challenges to assessment identified by teachers stem from practicality, learners, test construction, administrative work, and the teachers. Teachers do employ techniques to handle these challenges, which in the end are detrimental to language assessment practices concerning selected and productive skills and the variety of tasks presented in tests. Assessment of learning has considerable influence on the processes of teaching and learning, and the shortcomings facing assessment are a big obstacle to language teachers for devising relevant assessment. Consequently, the “Interim report of the language proficiency assessment on the Teaching English as a Second Language (TESL) Practicum in Canada 2021-2022” emphasizes the importance of equipping TESL practitioners with language assessment skills, as seen in the outlined recommendations for future assessment (formative assessment of language learning). In particular, providing English language instructors with professional development opportunities can foster the enhancement of assessment strategies in language pedagogy.
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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 | 0.169 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".