The Internationalization of the Student Body at Oceans College, Canada – An Appreciative Inquiry and Shared Leadership Approach to Change
Bibliographic record
Abstract
This Organizational Improvement Plan (OIP) addresses a problem of practice (PoP) at Oceans College (a pseudonym), a multilocation, panprovincial community college. Oceans College currently focuses on recruiting, admitting, and graduating domestic, in-province students. The number of international students, however, is growing. Currently there are no formal processes at the College for international students, unlike at the other postsecondary institutions in the province which actively recruit and enroll international students. The PoP is the lack of readiness at Oceans College to cope with the significant enrollment of international students. Different leadership theories were examined to develop this OIP, and appreciative inquiry (AI) and shared leadership were chosen as frameworks. They both take a positive approach, looking for the best in people. They both fit the matrix organization of Oceans College. Three key tenets used in this OIP are that Oceans College is doing many things well, that understanding the culture of the College is key, and that communication is core to the success of this OIP. The conceptual model I developed is the 4 C’s of Change—context, culture, clarity, and communication. Using this conceptual model, AI, and shared leadership, along with cultural theory, I developed an implementation plan that will allow Oceans College to internationalize the student body over a period of several years. Assessment and evaluation plans were also developed, as was a detailed plan to communicate the change to all stakeholders. The conjunction of theory, concepts, and experience has created an OIP that not only solves the PoP, but also keeps in mind what is already being done well and what would complement the college culture.\nKeywords: organizational improvement plan, appreciative inquiry, shared leadership, internationalization, change
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".