Engaging Student Stakeholders in Developing a Learning Outcomes Assessment Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it