Global Leadership in Higher Education Administration: Perspectives on Internationalization by University Presidents, Vice-Presidents and Deans
Bibliographic record
Abstract
The purpose of the study was to identify international university administrators' perspectives on organizational strategies to support higher education internationalization. Internationalization is the conscious effort to integrate international, intercultural, and global dimensions into the ethos and outcomes of higher education (NAFSA, 2008). A descriptive survey design method was used and the instrument entitled "Strategic Internationalization Priority Scale" was developed for this research. This study is quantitative and cross-sectional. The online survey was sent to 1,043 top university administrators at 149 universities in 50 countries. These universities had active international agreements with the University of South Florida at the time of the study.\nApproximately 350 university presidents, vice-presidents, and deans, from 33 countries, and 65 universities, participated in the study. ANOVA, MANOVA, and Multiple Regression analyses were used to examine data in the three dimensions of internationalization: 1) Planning and operations, 2) Student Education, and 3) Teaching and Faculty Development. The statistical programs used for data analysis were SAS 9.2, SPSS 18.0 and Mplus 5.\nIn general, the study participants perceived the three dimensions as having a medium priority level. Planning and operation strategies, and student education strategies, were rated higher than those for teaching and faculty development. Four of the 34 strategies were perceived as having a high priority level: 1) Motivating students to participate in study abroad programs, 2) Establishing institutional collaboration with foreign universities, 3) Communicating an institutional global vision, and 4) Increasing visibility of international focus on institution's web site. In contrast, the following strategies were perceived as having a low priority level: 1) Creating a branch campus abroad, and 2) Considering foreign language fluency in salary and promotion decisions.\nThe research findings revealed that there were differences in perceptions based on the following demographic characteristics: 1) Institutional description, 2) Institution's world region, 3) Institutional status, 4) Number of international undergraduate students, 5) Administrators' position, 6) Administrators' English proficiency, and 7) Administrators' International experience. Furthermore, the participants identified the following top difficulties in achieving internationalization at their institutions: 1) Lack of economic resources, 2) Lack of faculty involvement, 3) Lack of planning and coordination, and 4) Lack of governmental support. The implications of these results are presented as they relate to the research and practice of higher education administration, educational leadership and policy development.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".