Learning outcomes and program mapping: Tools for transparent assessment
Bibliographic record
Abstract
Program mapping (PM) provides a method to examine curriculum alignment, the assessment of learning outcomes, and to determine if program goals are aligned to learning activities and assessments. At a higher education institution in Western Canada, the Centre for Teaching and Educational Technologies (CTET) provides support to academic units using PM techniques to determine how learning outcomes are being implemented. The School of Education and Technology partnered with CTET to examine how to implement learning outcomes in a meaningful way for students and faculty and how to utilize program mapping as a means to evaluate success. To inform this work, a meta-synthesis research project was undertaken. Four main themes were identified in the literature: (1) faculty/student engagement; (2) organizational culture; (3) constraints, and (4) intentional process. Examples of a PM approach are shared and the implications for students and faculty of moving to outcomes-based learning approaches are discussed.
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 | 0.325 | 0.520 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".