Authentic assessment design in human physiology using the students-as-partners model
Bibliographic record
Abstract
‘Authentic assessments’ are described as educational assessments that prioritize realism and essential skill development. In higher education, authentic assessments are increasingly implemented to improve practical connections, namely in BSc. courses that were previously test-oriented. At the University of Guelph, Human Physiology was identified as a BSc. course that could benefit from the implementation of new authentic assessments. To effectively address student concerns during assessment creation, BSc. faculty chose to use the ‘students-as-partners’ model, which involves direct collaboration with students. Thus, we aimed to introduce authentic assessments in Human Physiology to improve real-world connections through a project titled, ‘Students-as-Partners in Assessment Design’.\nIn 2022, 4 student discipline-leads and faculty pairs were chosen for key BSc. courses, including Human Physiology, to collaboratively co-create new assessments with the support of an education developer. The initial goals of the project included strengthening practical skills whilst accounting for growing class sizes. For our course, we created a new group assignment titled, ‘Physiology Connections’ in which students presented a mini-lesson on a real-world physiology topic of their choice. The assignment was tested by 6 students prior to its implementation in the W’23 semester. To quantify the success of the new assignment, a class-wide survey was introduced to gather feedback. Results of the survey and details regarding the group assignment will be shared in the poster. Through this work in educational research, we aim to inspire additional institutions to value the benefits of the students-as-partners model, and strive to continue improving the quality of education through authentic assessment implementation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".