Assessing Students’ Attainment in Learning Outcomes: A Comparison of Course-End Evaluation and Entry-Exit Surveys
Bibliographic record
Abstract
The traditional course-end evaluation for the general education courses at The Chinese University of Hong Kong cangauge student's perception of their attainment of the intended learning outcomes at the end of the course but canhardly reflect the changes of their perception from the beginning to the end. In order to trace the change in students'perception regarding the intended learning outcomes of the General Education Foundation course In Dialogue withNature, a new assessment method that contains a pair of surveys with a set of identical questions, namely entrysurvey and exit survey, were developed and conducted at the beginning and at the end of the course correspondingly.While both assessment methods showed that the course was well-received, inconsistencies were identified and thatthe entry-exit surveys reveal additional aspects which could be overlooked with the traditional course-end evaluation.The study may suggest that entry-exit surveys provide a more truthful representation of students' perceivedattainment of the intended learning outcomes and sheds light on the development of course assessment strategies in general.
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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 | 0.037 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".