The Impact of Assessment for Learning on Students’ Achievement in English for Specific Purposes A Case Study of Pre-Medical Students at Khartoum University: Sudan
Bibliographic record
Abstract
This study tries to identify the effect of assessment for learning on a group of Sudanese pre-medical students’ performance in English for Specific Purposes (ESP). The study also attempts to identify students’ perception and attitudes towards this type of assessment. The sample of the study is composed of 53 subjects from the Pre -medical students at Khartoum University in Sudan. These students are placed into two groups; an experimental and a control group. The experimental group students are taught their ESP material in accordance with assessment for learning principles and techniques, the control group; however, is taught the same material using the traditional summative assessment procedures. The experiment lasts for one term, i.e., 16 weeks. The experimental group instructor is subjected to an intensive training course on how to implement assessment for learning strategies in classroom setting. At the end of the term, the two groups sit for a final exam which is intended for all Pre-medical students. Comparison of the scores of the students reveals a significant difference between the two groups in favor of the experimental group. Students’ attitudes towards assessment for learning are checked through a questionnaire and interviews. Qualitative and quantitative analysis of the students’ responses show their positive attitudes towards this type of assessment. The study ends up with a set of recommendations and suggestions to improve assessment for learning practice and to make it more effective in a Sudanese setting.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".