Bibliographic record
Abstract
Internationally there is pressure for significant change in measuring quality in teaching and learning processes (Krause, Barrie & Scott, 2012). Therefore institutions need to design curriculum that make student outcomes explicit, that provide opportunities for students’ to develop these outcomes as they progress throughout the degree and that incorporate assessment s t o foster these outcomes all whilst allowing for quality assurance and enhancement . It is well acknowledged that assessment methods have a greater influence on how and what students learn than any other single factor and so it is crucial that they are developed to foster learning of desired outcomes rather than to purely grade student achievement. This presentation will explore how assessment can be designed to complete a circle of quality assurance. That is, assessment is utilised as a "research instrument" by which the educator learns what their students are NOT learning, which then drives change . It will introduce two practical perspectives, individual assessment task design and whole of curriculum design both focus ing on assessments that not only assure learning but al so encourage development of student learning outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".