Student Led Observations for Course Improvement (SLOCI)
Bibliographic record
Abstract
As universities strive to enhance course delivery and the student experience, typical end-of-semester course evaluations have been demonstrated to provide insufficient and potentially biased detail for course improvement and innovation. The Student Led Observations for Course Improvement (SLOCI) team at The University of Queensland aims to provide high-quality student experience data through a student-led approach. The team comprises current undergraduate university students who have a basic understanding of pedagogical strategies and methods of evaluation, bridging the gap between students and academics. SLOCI utilises a course partnership model to work with academics to identify key research questions that can direct and inform a process of real-time feedback. Since 2018, SLOCI has conducted 48 single-semester course partnerships and nine research partnerships focussed on other aspects of the student experience. The student experience data generated from these collaborations has underpinned improvements resulting in higher student engagement and better 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".