Examining Technology Perception of Social Studies Teachers with Rogers’ Diffusion Model
Bibliographic record
Abstract
Mobile learning has started to take place in education literature with the developing technology, and this technology started to have an increasing spread along with its advantages. This study examines the responses of social studies teachers to the innovations in the field of mobile learning. The study was designed within the framework of theory of diffusion of innovations by Rogers and 161 social studies teachers have participated in it. Data was collected by m-Learning Adoption Scale (MLAS) which is developed by Celik and analyzed in SPSS 18.0 package program. According to the results of this study, approximately 65% of social science teachers are within the groups that give the most positive response to diffusion of innovations theory on mobile learning (innovators, early adopters, and early majority). Also, the characteristics of mobile learning have a descriptive impact on the teachers’ ability to embrace the innovations about mobile learning and to give positive decisions on using it. The study has suggested to develop new reactions for innovations and to develop the mobile learning content through collaborative approach by all the stakeholders in education.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".