Engaging Student Stakeholders in Developing a Learning Outcomes Assessment Framework
Bibliographic record
Abstract
Learning outcomes assessment and alignment contribute to the transparency, quality, and progression of a program. We set forth a learning outcomes framework that aligns learning outcomes at the course, major, program, and university levels. Senior undergraduate students were recruited to analyze assessments from eight core courses required for Molecular and Cellular Biology (MCB) majors at the University of Guelph. This analysis was conducted to achieve two goals: (a) to develop tools to assess learning outcomes in the MCB Department, and (b) to incorporate insights shared by the student perspective. Almost 1,600 Individual questions and their attributes were coded, compiled, and linked into the learning outcomes framework. The students then connected the questions to course concepts and assigned a cognitive domain indicated by Bloom’s Taxonomy level. After training and calibration, two undergraduate students evaluated all questions in the eight core courses with an average of 93.2% ± 1.6% (n=8) agreement between evaluators. These data were used to generate assessment profiles for individual courses and as an aggregate to provide insights regarding the program. This work makes constructive use the learning outcomes framework and illustrates the importance of leveraging undergraduate student perspectives in discussions of learning outcomes in higher education.
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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 | 0.210 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".