Instructor Leadership and the Community of Inquiry Framework: Applying Leadership Theory to Higher Education Online Learning
Bibliographic record
Abstract
Higher education institutions continue to invest in online learning, yet research indicates instructors often lack experience, preparation, and guidance for teaching online. While instructor leadership is essential for meaningful online learning, few studies have investigated online instructors’ leadership behaviors. This study offers new insights into the conceptual and empirical alignment between instructor leadership, as interpreted through the dual lenses of organizational leadership theory and the Community of Inquiry (CoI) framework, proposing instructor leadership as foundational to the teaching and learning experience in a CoI. Specifically, the convergent mixed methods study investigated students’ (N = 87) and instructors’ (N = 7) perceptions of instructor servant leadership (SL) behaviors in an online graduate-level course designed to facilitate a CoI. Results demonstrate instructor SL behaviors were perceived differently by students and instructors, instructors’ self-perceptions were generally higher than students’ perceptions, and students’ perceptions of instructor SL were positively correlated with their satisfaction with the course and instructor. Implications offer insights into instructor leadership behaviors important for developing instructor leadership presence to facilitate meaningful learning and student satisfaction in higher education online learning.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".