Graduate Attributes: Intentional Mapping and Assessment Portfolios
Bibliographic record
Abstract
In 1987, the University of Guelph introduced Learning1. CurricKit Outcomes Mapping has been created to support intentional curriculum development through aggregating faculty input on course outcomes to a program perspective.2. Progression Maps have been created to aid in the visualization of a program’s curriculum structure, through courses, semesters and program years3. A Portfolio System has been developed to permit student, educator and program portfolios to be built. These portfolios allow for reflection and for assessment of learning outcomes based on the artefacts of student work.This presentation will share current status and Guelph’s visions for the future - a future in which every student has a learning outcomes based portfolio and every program has an intentional curriculum map and a program level portfolio.By the end of this session, participants will be able to:• Describe the processes and tools being used at the University of Guelph,• Consider how to apply or adapt them for use in theirObjectives for all of its undergraduate programs. In 2004, the NSERC Chairs in Design Engineering released a white paper on Engineering Design Competencies. In 2009, the Province of Ontario mandated University Undergraduate Degree Level Expectations (UUDLEs). And finally, in 2010, the Canadian Engineering Accreditation Board (CEAB) began reviewing and assessing progress towards twelve graduate attributes. These initiatives are based on an outcomes philosophy towards curriculum development that is distinctly different from our historical, and still common, inputs based approach. Success in a learning outcomes approach relies on engaging students,educators and program leaders and is data-informed, educator- and student-driven, intentional and assessed. Guelph has been developing a combination of tools and processes to advance learning outcomes pedagogy:local context.
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.
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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".