Let's Get Digital: Teachers' Perspectives and Practices of Effective Technology Integration
Bibliographic record
Abstract
The goal of this research study was to explore the assessment and implementation of meaningful and effective technology use in different classrooms, as well as the factors and resources supporting and/or challenging these efforts. The main research question guiding this study was: How does a sample of teachers define “effective technology integration” and how do they enact this in practice? Data was collected through semi-structured interviews with two Ontario Certified Teachers working in the Greater Toronto Area who have demonstrated a dedication to effectively integrating technology into the classroom. Findings suggest teachers mainly integrated technology within the classroom for curricular, communicative, and assistive needs. Also, student-directed learning was a key success criterion for the effective integration of educational technology. Additionally, motivation to effectively integrate technology and access to resources supports the quality of technology use within the classroom. Findings identify obtaining resources and finding effective strategies to use those resources as challenges to effective integration. As well, findings suggest that effective technology integration produced positive intrapersonal and interpersonal student outcomes in academic and non-academic domains. The implications of these findings suggest positive outcomes are observed through effective technology practices, specifically relating to student achievement, and social-emotional 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".