Understanding the Interrelationship of Instructional Technology Use and Organizational Culture: ACase Study of a Veterinary Medical College
Bibliographic record
Abstract
Many predicted that in the latter part of the twentieth century modern technology would revolutionize higher education and "create a second Renaissance" (Sculley J. The relationship between business and higher education: A perspective on the 21st century. Commun ACM32:1056-1061, 1989 p1061). However, as the reality of the twenty-first century has set in, it is apparent that these revolutionary prophecies have fallen short. Using the lens of Douglas's Typology of Grid and Group, this case study examines (1) the organizational context of a veterinary medical college at a large Midwestern university; (2) individual faculty members' preferences toward instructional technology use; and (3) the interrelationship of culture and the decision process to implement instructional technology use in curricula. The study has several implications for instructional technology use in veterinary medical educational settings that help explain how cultural context can guide leadership decisions as well as influence faculty motivation and preference. The findings suggest that a key mitigating factor to instructional technology implementation is conflict or concord between the cultural biases of faculty members and actual cultural identity of the college (Stansberry S, Harris EL. Understanding why faculty use (or don't use) IT: Implementation of instructional technology from an organizational culture perspective. In Simonson M, Crawford M, eds. 25th Annual Proceedings: Selected Research and Development Papers Presented at the 2002 National Convention of the Association for Educational Communications and Technology, vol. 1. North Miami Beach, FL: Nova Southeastern University:viii, 507).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".