Backwash in Higher Education: Calibrating assessment and swinging the pendulum From Summative Assessment
Bibliographic record
Abstract
Now more than ever, there exists a plethora of empirical evidence to uphold that examinations used in educational institutions have a backwash effect, a well-recognized phenomenon among applied linguists, educators and teachers, which is the effect of test on teaching and learning (Alderson & Wall, 1993; Bailey, 1999; Messick, 1996; Widen et al., 1997; Hughes, 2003; Yi-Ching, 2009). This article essentially targets this phenomenon in Moroccan higher education. It seeks to provide a concise theoretical framework to render the reader au fait with such an unfamiliar term. It aims at examining the extent to which higher education assessments affect EFL students’ academic achievements through sketching examples from the summative assessment practices used by faculty instructors at Ibn Zohr University, Agadir, Morocco. It also aims at suggesting some pedagogical implications to harness teaching and learning in Moroccan higher education.
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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 | 0.059 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".