Bibliographic record
Abstract
Since “English has become the lingua franca for academic interaction of learners and academics” (Koo, 2009, p. 77), the development of the EFL learners’ oral performance proficiency constitutes the central interest of current English language teaching methodologists and practitioners. The importance of speaking as a productive skill has been echoed in the literature. Indeed, it is viewed as a crucial “part of the curriculum in language teaching … and …an important object of assessment as well” (Luoma, 2004, p. 1). Thus, the prime aim of this study is to explore the prevailing conceptions and actual practices of the assessment of EFL learners’ speaking skills at the tertiary level. The respondents of the current research were 20 instructors who taught at the Higher Institute of languages in Gabes and at the Faculty of Arts and Humanities in Sfax, Tunisia. To collect the necessary data, a questionnaire survey was utilized. Then, the obtained data were analyzed using SPSS package. The findings revealed that the teachers’ conceptions of assessment are directed towards the development of the learners’ speaking skills. Despite the existence of a number of hardships, the teachers’ classroom teaching practices revealed a compete reliance on authentic, ongoing, organized and thoughtful oral language assessment procedures which were meant to sustain and boost the learners’ oral skill achievements.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".