Design and Intervention of an Educational-Leadership Program: Student Voice and Agency, Expectations and Internationalization
Bibliographic record
Abstract
This paper explores the lived experiences of a diverse student cohort enrolled in a master’s degree educational-leadership program. The program’s global focus was on the quality of teacher education, prospective teachers’ workplace preparedness and leaders in the workforce in higher education. Internationalization, real-life experiences and student voice served as an enacted intervention curriculum for an educational leadership course designed to reveal the gap between theory and practice. An epistemological diversity lens stimulated critical reflection on students’ participation in the course design and its connection to realities in the field. Diverse higher-education classrooms pose specific challenges for educational leadership programs in including effective internationalization, workplace relevance and improving the generalizability and content validity of the educational leadership course. This small qualitative exploratory investigation provides an in-depth understanding of the value of student voice in informing course and program design. Interviews, observations, two surveys and a document analysis triangulated the data and provided information on the complexities in higher-education classrooms. The findings focus on teacher-educators and higher-education classroom management as well as on the value of critical inquiry, reflection and intervention for existing course designs and transformation.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".