Integrating information and communication technology into curriculum: a case study in Atlantic Canada
Bibliographic record
Abstract
This study captured and analysed the perspectives, perceptions and lived experiences of educators in integrating Information and Communication Technology (ICT) in the curriculum. A qualitative research approach, which afforded the leverage for open-ended questioning of personnel working to facilitate ICT integration in one school board in Atlantic Canada, was used to obtain the relevant data for this study. In addition, the framework set by local and international policies, standards and guidelines, was used to investigate and analyse issues surrounding technology in practice, bridging the digital divide, professional development, challenges, and the cost of integrating ICT. Data generated from the research reflected the participants‟ role and exposure as leaders in ICT integration, and their perception of the teaching and learning experience of other educators within the School Board with whom they interacted while mentoring, coaching, training and providing supporting services. The findings indicated that there is tremendous support for ICT integration across all school levels amidst challenges of shifting technology ideals, funding, and competing curriculum and school demands. The drive to improve the epistemological experiences in the learning environments of the Board through ICT integration is reported as a continuous balance between wills and the prudence required to optimise resources available. The inclusion of additional research on teachers‟ and students‟ perspectives and perceptions on the teaching learning process with ICT integration can be of value to the body of 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".