Assessment in Medical Education; What Are We Trying to Achieve?
Bibliographic record
Abstract
Within the arena of medical education, it is generally acknowledged that assessment drives learning. Assessment is one of the most significant influences on a student’s experience of higher education and improving assessment has a huge impact on the quality of learning (Liu, N. and Carless, D, 2006). Ideally we want to enhance student’s capacity for learning and engagement with the curriculum (ACGME Outcome Project, 2000). However, this doesn’t always happen as it is heavily dependent on the form of assessment used and whether or not timely comprehensive feedback is given. This paper focuses on the challenges associated with assessment in medical education and looks at the current trends. Well-designed formative assessment can focus students on effective learning and divert them away from summative assessment, which focuses attention on grades and reproductive thinking (Liu, N. and Carless, D, 2006). Whether one decides to utilise summative or formative assessment methods, both methods of assessment are useful when applied in the correct setting and at an appropriate stage of learning. It is apparent that assessment is the gatekeeper of higher learning and we need to embrace new methods of assessment in order to meet the challenges associated with ‘Generation Y’. Novel assessment methods such as self and peer assessment are growing in popularity. Students who participate in these forms of assessment may initially feel that it is challenging but worthwhile overall, as it helps to develop their critical thinking skills. Incorporating complimentary assessment components could benefit student’s learning without sacrificing the integrity of the curriculum.
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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 | 0.110 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.031 | 0.044 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.017 | 0.032 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".