CREATING FACULTY BUY-IN: LEADERSHIP CHALLENGES IN IMPLEMENTING CEAB GRADUATE ATTRIBUTES
Bibliographic record
Abstract
In 2009 the Canadian Engineering Accreditation Board (CEAB) announced its intention requiring all undergraduate engineering programs in Canada to utilize twelve graduate attributes for assessing the capacities of its students. In response, engineering faculties across the country have been experimenting with creating processes that incorporate these graduate attributes as a means to stimulate program improvement to achieve curricular and program innovation. Many of the support resources (like the inter-university collaboration EGAD, for example) have focused largely in three directions – definitional, programmatic and information management challenges faced by different engineering programs.Less attention has been given to identifying and addressing leadership challenges faced by faculty administrators in piloting curricular and programmatic changes such as the CEAB graduate attributes. We argue that these challenges result from fundamental features of university educational culture: faculty members place great value upon autonomy in their workplace, and likewise expect a high degree of intellectual independence in designing courses. The introduction of CEAB attributes, together with the mandated changes they will bring to course design, is perceived by faculty members as an external imposition. Such a perception we suggest introduces some scepticism in the faculty about its efficacy leading to a disengagement from the change process. Thorough attention to these cultural factors impacting on graduate attributes adoption is crucial to the implementation of successful curriculum development.Describing these challenges in detail, this paper will outline some pathways that can circumvent these impediments to curricular innovation.
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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 | 0.042 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| 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".