Factors That Influence Superintendents in Iowa to Implement a One-To-One Computer Initiative
Bibliographic record
Abstract
The purpose of this study is to identify factors that influence superintendents in Iowa to implement a one-to-one computer initiative. An in-depth, phenomenological based interview method was used. Two interviews were conducted with each of the five superintendents in the study. The superintendent responses were then organized using the Saskatchewan Educational Indicators, which are context, process and outcome indicators. The major questions addressed in the study were: 1. What factors influenced the decision to implement a one-to-one computer initiative? 2. What are the results of implementing a one-to-one computer initiative? 3. What decision-making process do superintendents in Iowa use when deciding to implement a one-to-one computer initiative? Factors that influenced superintendents in the study to implement a one-to-one computer initiative were visits to other districts that had implemented a one-to-one computer initiative, student engagement, equity of student access to laptop computers and online resources, political considerations, staff readiness and doing what is best for students. Factors that did not influence the decision of the superintendents were student achievement, enrollment trends, the bandwagon effect and budget. While budget did not influence the decision, what made this possible was the ability to fund the initiatives through two specific funding mechanisms separate from the general fund; the School Infrastructure Local Option tax and the Physical Plant and Equipment Levy. The results of implementing a one-to-one computer initiative included greater student engagement and a change in the student/teacher relationship. This was characterized as moving away from a teacher-centered approach and toward a student-centered approach, which included students in formal and informal teaching roles with adults and peers. The decision making process the superintendents used was similar to the process typically used when making large, district-affecting decisions. Each superintendent communicated with various stakeholder groups and most facilitated the process rather than overtly advocating for the initiative. The major departure from their typical approach included sending various teams to visit other school districts that had implemented a one-to-one computer initiative to gather information and see the initiative first hand. The study concluded with recommendations for further study.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".