Integrating Technology: The Principals’ Role and Effect
Bibliographic record
Abstract
There are many factors that influence technology integration in the classroom such as teacher willingness, availability of hardware, and professional development of staff. Taking into account these elements, this paper describes research on technology integration with a focus on principals’ attitudes. The role of the principal in classroom practice was found to be substantial. The purpose of this research was to assess the current attitudes concerning technology integration in schools from the school principal’s perspective. This research investigated the value principals place on using technology in student learning, what principals believe prevents teachers from succeeding in technology integration, what can best facilitate teacher development, and if principals perceive peer coaching or mentoring to be a viable option. The research herein consisted of a survey and an interview to help assess principals’ attitudes regarding the importance of technology integration, the perceived challenges and whether or not teacher coaches are a viable option for the future. This examination concluded that most principals in this research study value technology in education, perceive teacher willingness and professional development to be the strongest obstacles, and think teacher coaches would be a viable option for success. This study sheds light on a couple of paths for future research.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".