Using a ‘Students as Partners’ model to develop an authentic assessment promoting employability skills in undergraduate life science education
Bibliographic record
Abstract
Authentic assessments (AA) include three principles, realism, cognitive challenge, and evaluative judgment, and replicate professional workplace expectations. Developing AA in undergraduate life science education is necessary to promote critical skill development and adequately prepare students for the workplace. Using a 'Students-as-Partners' (SAP) approach, five students, an educational developer and the instructor codeveloped an AA requiring students to utilize scientific literacy (SL) and critical thinking (CT) skills to develop a data extraction table and generate communication outputs for scientific and nonscientific audiences. Subsequently, the SAP-developed AA was completed by students (n = 173) enrolled in a fourth-year life sciences and pathophysiology course who completed an online survey providing feedback about their perceived development of critical skills and the relevance of the assignment to the workplace. The top transferable skills students reported the greatest growth in were SL (41.6%, n = 72), communication (34.7%, n = 60), CT (16.2%, n = 28), and problem-solving (7.5%, n = 13). Student self-assessed and instructor-assessed grades were positively correlated, wherein 60.6% of students assessed their AA grades below the instructor's assessment and 4.7% of students assigned themselves the same grade as the instructor. Students' perceived stress levels were (a) negatively correlated with assignment grades and feelings of enjoyment, hope and pride, and (b) positively correlated with feelings of anger, anxiety, shame, and hopelessness while working on the assignment. This study demonstrates the impact of AA on the student learning experience and the relevance of AA to help prepare students for life science careers.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".