Innovative Teacher’s Perceptions of Their Development When Creating Learner-Centered Classrooms with Ubiquitous Computing
Bibliographic record
Abstract
Though many USA schools embrace ubiquitous computing, few teachers reach a pedagogical developmental stage that makes the most effective use of technology for learning. In order to better understand the advanced stage of pedagogical development, this research gathers the perceptions of seven innovative – advanced teachers, from four different schools in order to report on their change processes. All participants once taught in traditional classrooms and now create learner-centered classrooms with ubiquitous computing. The results are based on interviews in a comparative case study framework. Despite teaching in various contexts, results revealed that teachers had common experiences. Qualitative themes were based on combining three common developmental change theories. The “entry” stage was heavily influenced by dissatisfaction of societal needs and past ineffective teachers. In later stages, teachers developed strong beliefs coupled with student observations and project creation techniques, and they overcame obstacles of fear through collegial collaboration, furthering their continuous growth. As innovators, teachers’ current concerns focused on how to deepen student learning with meaningful experiences so that technology was worth the cost of time and effort. Teachers’ experiences suggest concepts for further exploration in research and professional 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".