Departmental leadership for learning in vocational and professional education
Bibliographic record
Abstract
To maintain relevance, institutes for vocational and professional education (VPE) need to be responsive to changes in society and industry. This requires leaders and educators in VPE to keep developing themselves and their practices. In institutes for VPE many decisions regarding program curriculum and teaching practices are made at the department level. Yet, the preparation of department chairs in post-secondary contexts rarely focuses on improving teaching and learning. A substantive knowledge base regarding leadership of teaching and learning in VPE is lacking. A model of leadership for learning in VPE is proposed as comprising three elements: (1) developing a shared vision and goals; (2) promoting instructor learning; and (3) leading the educational program. The study explores ways in which seven chairs and associate chairs from western Canadian VPE institutes conceive of and enact leadership for learning in their departments. The paper thus aims to contribute to the building of a knowledge base on leadership for learning in VPE. A multiple case study approach was used to explore chairs’ conceptualization and enactment of their leadership for learning. Data collected included interviews with five chairs and two associate chairs from five departments across three institutes for VPE in western Canada. Observational data from department meetings and interviews with instructors were used to provide contextual information and corroborate our findings. In cross-case analysis, chairs’ and associate chairs’ conceptualizations and strategies were themed and categorized according to the three elements of leadership for learning as identified in the literature. Findings show great variability amongst study participants in beliefs regarding the importance of a shared vision and goals, and only one chair had taken steps towards developing a shared vision. All participants expressed that they value quality teaching and learning and have taken promising steps to promote instructor learning, yet in different ways. Leading the educational program was considered a common and expected part of the role of department chair. Participants’ beliefs regarding the enactment of leadership for learning varied greatly and seemed idiosyncratic. The proposed model of leadership for learning seems to hold promise for conceptualizing leadership for learning in the context of VPE. However, further studies will need to elucidate how each element of leadership for learning might best be enacted to optimize student learning in VPE departments. Such studies will need to consider the organizational and cultural context of the VPE department. Institutes for VPE may invest in leadership development programmes that allow chairs to develop leadership for learning practices. Institutes might also focus on enacting leadership for learning at the faculty and institute level.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".