Understanding the relationship between the personal and professional use of technology by K-12 educators
Bibliographic record
Abstract
Much research has been done around how and why teachers integrate technology into classroom practices. Various factors have been shown to be important including teachers??? beliefs, attitudes, comfort, knowledge and skills. This has proven to be a complex mix with the outcome of technology integration depending, in various ways, on all of these factors. A need has emerged for a way to look at this complex mix of variables that takes into account the reasons teachers use technology and the tasks which they complete using technology. This kind of research tool could be used in a variety of ways to analyze these variables. This paper describes the outcomes of a project to develop a multifaceted, domain based survey instrument that looks at the frequency of use and confidence in the use that educators have with various technology tasks, as well as the importance that they place on these tasks for personal and professional use. The instrument was then tested on a small group of teachers in a school board in Ontario, Canada and the data was analysed to determine if it could be used in broader studies to answer such questions as have been posed in the literature. The results show that the instrument will be valuable in showing how educators??? beliefs are connected to the frequency of use and confidence they have in certain technologies. It should also be able to determine if those beliefs change over time and if this translates into changes in technology use. It was less clear if the instrument would be useful in determining how educators??? personal and professional use of technology was related and further refinement for this purpose could be considered.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".